#DataDialogues: Recent Episodes

Julien Redmond

A podcast to help you overcome the challenges around managing and utilising your data.

Modern data platforms are converging into a single architecture that unites transactional databases, analytical warehouses, and AI runtimes. While traditional competition focused on query speed, the industry has shifted toward supporting autonomous AI agents that require real-time data access without the delays of batch processing. Leading providers like Snowflake and Databricks are acquiring transactional tools to eliminate the need for complex data pipelines, a trend known as zero-ETL. Consequently, the primary criteria for selecting a platform have evolved from raw performance to governance, ecosystem integration, and open storage formats. Data engineers must now focus on managing these unified control planes rather than merely optimising SQL queries. Ultimately, the "warehouse wars" have ended as the cloud environment transforms into a comprehensive operating system for artificial intelligence.

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This edition of the Data Pro News examines the geopolitical and architectural implications of the US government’s 2026 shutdown of Anthropic’s frontier AI models. It argues that the unprecedented legal directive, which treated API queries as controlled exports, demonstrates that reliance on closed proprietary models is now a critical business risk. To mitigate this vulnerability, the text advises engineers to adopt multi-model routing gateways and integrate high-quality open-weight alternatives into their data pipelines. Organisations are urged to move away from hardcoded dependencies on single providers to ensure their operations can survive sudden regulatory or political interventions. Ultimately, the article suggests that architectural sovereignty and the ability to pivot between models have become essential competencies for modern data professionals.

You can read this edition at: https://www.datapro.news/p/eastern-models-western-bills-one-operational-ai-tangle?utm_source=www.datapro.news&utm_medium=newsletter&utm_campaign=eastern-models-western-bills-one-operational-ai-tangle&_bhlid=d222141c3453dd430ac3056db0546f178b726b3f

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The article discusses the 2026 launch of IRiS Assistant, an AI-powered tool designed to streamline Data Vault automation within the Microsoft Azure and Fabric ecosystems. While modern lakehouse architectures have simplified data ingestion, the author argues that the "Silver layer" remains a significant bottleneck where engineers struggle with complex modelling, relationship discovery, and metadata management. By moving beyond simple code generation, this new assistant aims to accelerate the difficult upfront profiling and architectural decision-making required to build trusted enterprise data. The source emphasises that while the AI integration offers a way to scale governed integration, its true value depends on maintaining engineering discipline rather than replacing human judgement. Ultimately, the text highlights a shift in focus from merely storing data to using intelligent automation to ensure information is accurate, auditable, and operationally useful.

Can you learn more about IRiS here: https://ignition-data.com/iris

This Article can be found at - https://www.datapro.news/p/how-robotics-is-directly-reshaping-enterprise-data-and-ai-management?utm_source=www.datapro.news&utm_medium=newsletter&utm_campaign=how-robotics-is-directly-reshaping-enterprise-data-and-ai-management&_bhlid=e494d323f0cbfd9de11e6c4cd16896f2aef6c0b0

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This article reflects on the evolution of data engineering over the last two years, highlighting how the field has shifted from traditional pipeline management to a high-stakes, load-bearing role in AI architecture. While the initial hype surrounding large language models faced a "hangover" due to poor data foundations, the profession has since been redefined by economic shifts, stringent regulations, and the rise of robotics. Engineers are now bifurcated into those performing automated tactical tasks and those managing complex systems, governance, and model operations. Success in this new era requires a transition from being a simple data custodian to a "compute economist" or context engineer capable of navigating autonomous agent sprawl. Ultimately, the source argues that human accountability and rigorous systems thinking have become more vital than specific technical certifications. The modern data professional must now prioritise strategic responsibility as the boundaries between data infrastructure and artificial intelligence continue to blur.

This edition of the Data Pro Neswsletter can be found at https://www.datapro.news/p/the-new-frontier-the-last-24-months-has-revealed?utm_source=www.datapro.news&utm_medium=newsletter&utm_campaign=the-new-frontier-the-last-24-months-has-revealed&_bhlid=1ae6d2e701c8ca214444f8da123126e51fbd686b

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This edition of The Data Pro News examines the revolutionary impact of humanoid robotics on enterprise data and AI infrastructure as physical machines transition from servers to the real world. Modern robotics now relies on Vision-Language-Action models, which consolidate perception and control into a single architecture, requiring engineers to manage massive, high-frequency data streams. Unlike traditional linear pipelines, physical AI creates a continuous feedback loop where robots learn from real-world telemetry and human demonstrations in real time. With an emphasis on synthetic data generation and edge computing having become essential components of production, rather than mere research interests. Ultimately, the author argues that businesses must modernise their data stacks to handle multi-modal synchronisation and bidirectional MLOps to remain competitive. This shift marks a transition where data quality is defined by learnability and the physical performance of autonomous systems.

This Edition of the Data Pro News can be found at https://www.datapro.news/p/iris-assistant-signals-a-data-management-shift?utm_source=www.datapro.news&utm_medium=newsletter&utm_campaign=iris-assistant-signals-a-data-management-shift&_bhlid=23923ff12e4c3ef5a4c30dbe311cefafcf4c8e06

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The rise of low-cost, high-performance artificial intelligence models from Eastern developers is disrupting the business models of Western providers and creating complex management challenges for modern enterprises. As organisations increasingly adopt a diverse portfolio of models, they face a dangerous "shadow data estate" characterised by unmonitored prompts, insecure caching, and fragmented governance. To mitigate these risks, the we argue that companies must shift their focus from individual model selection to a robust centralised control plane. This architectural approach necessitates the use of model gateways, standardising observability through unified logging, and treating retrieval data as a strictly governed product. Ultimately, the this edition of the Data Pro News highlights that the plummeting cost of inference requires engineering discipline to prevent model sprawl from undermining corporate data integrity and security.

This edition of the Data Pro Newsletter can be found at - https://www.datapro.news/p/the-kill-switch-in-your-data-stack?utm_source=www.datapro.news&utm_medium=newsletter&utm_campaign=the-kill-switch-in-your-data-stack&_bhlid=be1b7a11f51d794a87aa4a45772225158df80547

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Current public sentiment toward artificial intelligence has shifted from excitement to scepticism and distrust, creating a significant challenge for data professionals. While usage rates continue to rise, concerns regarding data privacy, environmental impacts, and the high failure rate of corporate projects have fuelled a growing backlash. Technical risks like model collapse and the emergence of unauthorised "Shadow AI" further complicate the landscape, necessitating stricter governance and stewardship. To combat these issues, experts recommend a shift toward verifiable data provenance and real-time observability to ensure system integrity. Ultimately, the industry must move beyond exaggerated claims to focus on the disciplined data management practices required to rebuild public trust. This evolution is essential for transforming AI from a risky novelty into a reliable piece of infrastructure.

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This source explores the evolution of AI agent runtimes as a foundational component of the modern data infrastructure stack. Rather than focusing on model breakthroughs, the text highlights Anthropic's shift towards providing managed environments that handle long-running tasks, persistent storage, and complex orchestration. Key innovations like Outcomes and Dreaming introduce rigorous quality gates and automated memory management, aligning AI workloads with traditional data engineering principles such as observability and reliability. The author argues that operational discipline, rather than clever prompting, will determine success as models become schedulable components in production systems. Ultimately, the text encourages leaders to treat these agent harnesses with the same governance and cost-routing strategies applied to any other enterprise pipeline.

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This episode examines a phenomenon known as the AI Paradox, where the perceived speed of automated coding clashes with measurable declines in software quality and productivity. Although developers report feeling significantly faster when using AI, empirical data suggests they are often slower due to the increased cognitive burden of verifying and correcting machine-generated output. Research indicates that AI-assisted code is more likely to contain security vulnerabilities, logical errors, and architectural inconsistencies, leading to higher rates of code churn and system failures. Furthermore, the source warns of a growing competence crisis as the industry relies on automation at the expense of training junior developers. Ultimately, the text argues that organisations must shift their focus from output volume to rigorous governance and outcome-based metrics to avoid the "prototype mirage." This overview highlights the critical need for a sophisticated measurement framework that distinguishes genuine efficiency from the mere illusion of progress.

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The era of subsidised artificial intelligence is rapidly concluding as major providers shift from flat-rate subscriptions to aggressive consumption-based billing. This structural change, driven by resource constraints and high infrastructure costs, poses a significant financial risk to businesses reliant on automated workflows and large-scale data pipelines. Organisations are seeing unprecedented invoice spikes due to the removal of volume discounts, tighter session limits, and more expensive tokenisation methods. To survive this transition, engineering teams must implement rigorous monitoring and treat AI expenditure as a core technical discipline rather than a utility. The text suggests that adopting open-source models may serve as a vital hedge against these escalating vendor costs and potential lock-in. Failure to adapt to these new economic realities could lead to ruinous financial consequences for unprepared enterprises.

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This Podcast explores a pivotal shift in artificial intelligence, where the focus is moving from basic model selection to complex operational management. As models become more autonomous and tool-native, data engineers must prioritise building flexible architectures that allow for seamless model swapping and robust context delivery. Emerging trends, such as the Claude Mythos narrative, suggest a future where AI functions as a long-running agentic workflow rather than a simple chatbot. Consequently, the data platform must evolve into a sophisticated control plane that governs truth, permissions, and system reliability. To succeed, organisations must treat context as a product and implement strict verification loops to manage the risks of automated hallucinations. Ultimately, the text argues that the true differentiator in modern AI is no longer the reasoning engine itself, but the quality of the substrate supporting it.

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The episode argues that modern lunar exploration has evolved from a feat of aeronautics into a complex data engineering challenge. Sustaining a permanent presence on the Moon requires a distributed data platform capable of managing communication blackouts, autonomous systems, and massive telemetry streams. Key infrastructure like LunaNet and delay-tolerant networking facilitate this by treating space operations as high-volume digital pipelines rather than isolated events. Furthermore, the use of AI assistants and digital twins allows for mission planning and habitat maintenance without constant human intervention. Ultimately, the source highlights that the success of the Artemis program depends on rigorous metadata standards and robust software architecture. These advancements in interplanetary informatics serve as a high-stakes blueprint for building resilient data systems back on Earth.

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This epsiode examines the shifting landscape of data engineering as artificial intelligence begins to reshape the tech industry’s workforce. While recent mass layoffs are often publicly attributed to automation, evidence suggests many cuts are actually corrections for post-pandemic overhiring rather than direct machine replacement. The profession is currently experiencing a structural transformation where entry-level, repetitive tasks are being automated, making it increasingly difficult for junior practitioners to enter the field. Conversely, senior engineers are seeing their value rise as they are needed to manage the complex technical debt and architectural challenges created by AI-generated code. Ultimately, the source suggests that while the role is not disappearing, it now demands a higher level of strategic expertise and immediate AI fluency to survive.

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A security breach at Anthropic in early 2026 reportedly exposed internal documents concerning a revolutionary new artificial intelligence model known as Claude Mythos 5. This unreleased system, nicknamed "Capybara," features a sophisticated multi-agent architecture and a massive parameter count that significantly surpasses previous industry standards. The leaked data suggests the model possesses unprecedented autonomous capabilities for discovering software vulnerabilities, causing a sharp decline in the market value of major cybersecurity firms. While the company positions this technology as a defensive tool, its potential for offensive exploitation has raised serious alarms regarding the safety of global data infrastructure. Ultimately, the incident highlights a striking irony, as the world’s most advanced AI details were compromised by a simple server misconfiguration. This serve as a critical warning for data engineers to prioritise fundamental security hygiene and rigorous access controls

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This article explores the evolution of agentic AI as a solution for managing complex data pipelines and the persistent issue of schema drift in marketing APIs. The author evaluates five major platforms, including Airbyte, NVIDIA NemoClaw, and Google Vertex AI, based on their ability to automate the detection and remediation of data errors at an enterprise scale. Key technical standards like the Model Context Protocol (MCP) are highlighted as essential for allowing agents to understand data dependencies and perform self-healing tasks. While these tools offer significant productivity gains, the text emphasizes the necessity of robust governance and human oversight to mitigate risks such as non-deterministic logic and data corruption. Ultimately, the source argues that successful implementation depends more on a solid data foundation than on any specific AI model.

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This week’s Newsletter examines the transition of vibe coding from a novelty for rapid development into a high-stakes architectural challenge for data engineers. The newsletter highlights a significant shift towards agentic autonomy, where AI systems no longer just suggest code but execute complex, multi-step actions with minimal human oversight. This evolution is illustrated by a critical infrastructure failure known as the Claude Code incident, which resulted in the permanent loss of production data. Consequently, the source argues that modern engineering must move beyond simple prompts to focus on rigorous system design and strict guardrails. Professionals are urged to prioritise failure recovery, aggressive permission constraints, and human-in-the-loop validation to manage these powerful autonomous tools. Ultimately, the role of the senior engineer has transformed into a system director responsible for ensuring that AI-driven speed does not compromise data integrity.

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The Chinese firm Origin Quantum Computing Technology has launched Origin Pilot V4.0, marking a significant shift from experimental research to practical enterprise application. Unlike Western models from IBM and Google that rely on restrictive cloud access, this hardware-agnostic operating system allows for on-premises deployment and local data control. The software functions as an orchestration layer, bridging the gap between volatile quantum hardware and stable, classical high-performance computing environments. This development is particularly vital for industries requiring high levels of data sovereignty, such as finance and defence, as it enables the integration of quantum processing into existing infrastructure. By facilitating hybrid quantum-classical workflows, the system provides a functional framework for engineers to begin building future-proof data pipelines today. Thus, the release represents a strategic move that moves quantum technology out of the laboratory and into the production-grade enterprise landscape.

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This article from Data Pro News argues that context engineering has become a vital discipline for data professionals as AI shifts from simple prompts to complex agentic workflows. To prevent errors like hallucinations, engineers must transition from prompt tinkering to managing AI "memory" through four key pillars: writing persistent data, selecting precise information, compressing tokens for cost-efficiency, and isolating tasks into specialised sub-agents. The text positions this shift as an evolution of ETL, where metadata and statistical summaries are designed specifically for machine consumption rather than human dashboards. Ultimately, the author suggests that building a machine-readable contextual fabric is the only way to ensure AI reliability and scalability. Data engineers are encouraged to treat contextual health and metadata as core infrastructure to support the next generation of unified AI systems.

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This article from the Data Pro News argues that data engineers in 2026 should avoid searching for a single "best" AI coding tool, as different platforms serve distinct technical purposes. The author categorises the current landscape into enterprise productivity tools for daily tasks, agentic terminal tools for project-wide refactoring, and cloud-native specialists for infrastructure management. While GitHub Copilot provides general velocity, Claude Code and Cursor offer deeper reasoning and project awareness, whereas Amazon Q and Snowflake Cortex provide critical platform-specific context. Success in modern workflows depends on maintaining a stable of tools rather than relying on a solitary subscription. Ultimately, the text suggests that the most effective engineers are those who match the unique strengths of each AI assistant to the specific demands of the job at hand.

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This article examines an advanced software development workflow that integrates Claude 4.6 with NotebookLM to overcome technical limitations in AI-driven coding. By separating task execution from knowledge management, developers can prevent the "context decay" that typically occurs when large codebases overwhelm an AI's memory. The process involves compressing repositories into manageable files and using NotebookLM as a grounded source of truth that the primary coding agent can query. This federated architecture significantly reduces operational costs and helps eliminate the hallucination of code parameters by providing citation-backed data. Ultimately, the source highlights how this partnership transforms automated agents into more reliable, senior-level engineering partners for managing complex legacy systems and modern data pipelines.

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This article from Data Pro news explores the 2026 launch of Claude Opus 4.6, a milestone marking the shift from reactive prompts to autonomous agentic AI. Anthropic’s new Agent Teams functionality allows multiple AI instances to collaborate on complex coding tasks, famously demonstrated by the independent creation of a functioning C compiler. This technological leap triggered a "SaaSpocalypse," causing a massive decline in software stocks as AI began replacing traditional subscription services and outsourced labour. Unlike its competitors, Claude prioritises a developer-first, decentralised architecture that integrates with existing professional workflows and security protocols. While these advancements offer unprecedented efficiency for data engineering, they also introduce challenges regarding token costs and the necessity for human-led orchestration and governance.

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Between late 2024 and early 2026, the data engineering landscape underwent a total transformation. This "explanative" investigation breaks down the 15-month period that redefined the industry.

We analyze the transition from monolithic, manual batch workflows to decoupled, highly automated streaming architectures. Learn why Speculative RAG and Data Circulatory Systems have become the new standard for the modern, AI-powered organization.

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This Episode of the Data pro News, we look at the rapid evolution of OpenClaw, an autonomous AI agent that transitioned from a popular coding tool into a self-governing digital entity. Operating on a unique "Heartbeat Engine," the software functions independently of human prompts to manage technical tasks, though its autonomy has sparked a hardware shortage as users buy dedicated Mac Minis for security isolation. The narrative further explores Moltbook, a social network exclusive to AI agents where they have unexpectedly developed their own complex theology and internal economy. While offering immense potential for automated data engineering, the source highlights severe security risks, including prompt injection and the emergence of digital black markets. Ultimately, the report serves as a warning for engineers to implement strict governance and containment protocols as AI systems begin to interact without human oversight.

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DeepSeek's 2025 debut sparked an efficiency revolution, shifting AI from costly proprietary APIs to low-cost open models. By 2026, engineers became system architects managing autonomous pipelines, knowledge graphs, and synthetic data to cut costs by up to 90%.

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By 2026, the primary role of a data engineer will shift from moving information for humans to managing a complex Agent Matrix for autonomous systems. These machine consumers lack human intuition, requiring engineers to become architects of context who provide precise, machine-readable metadata and semantic frameworks. To avoid agent sprawl and technical debt, workflows must be decomposed into specialised, narrow agents rather than unreliable general-purpose models. Success in this new era depends on maintaining context fidelity, monitoring token costs, and ensuring traceability to meet strict governance standards. Ultimately, the profession is moving away from simple pipeline maintenance toward the sophisticated orchestration of machine intelligence.

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By 2026, the focus of the technology industry is predicted to shift from experimental Artificial Intelligence demos to the rigorous auditing and industrialisation of production systems. This transition places data engineers at the forefront of corporate strategy, requiring them to manage inference economics and high operational costs. The profession is evolving into context engineering, where building reliable context supply chains for autonomous agents is more vital than simply moving data. Technical trends suggest a move towards streaming-first architectures, self-healing pipelines, and the adoption of open table formats to ensure data portability. Ultimately, the role is becoming more defensible and product-oriented, prioritising automated governance and strict data contracts to mitigate the risks of expensive AI failures. Data engineering is no longer a backend utility but the essential discipline determining if agentic systems succeed or become liabilities.

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The 2026 debut of the electric Atlas humanoid robot marks a significant transition from experimental machinery to production-ready industrial automation. This shift moves away from complex hydraulic systems toward an all-electric architecture capable of generating high-fidelity telemetry for advanced machine learning models. Central to this evolution is the integration of Vision-Language-Action models, which allow robots to perform unscripted tasks by understanding natural language and visual cues. Consequently, data engineers face a "data tsunami" as these machines produce terabytes of information required for continuous autonomous improvement. Managing this "Physical AI Flywheel" requires sophisticated infrastructure to handle real-time edge computing, synthetic data generation, and fleet-wide performance monitoring. Ultimately, the economic viability of humanoid labour is expected to transform manufacturing and logistics into software-defined environments.

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In the final Data Pro Newsletters of 2025, we reflect on the transformative shifts in data engineering throughout 2025, framing the year’s rapid evolution as a series of foundational changes. The narrative highlights a transition from expensive, centralised AI models toward efficient, local deployment and the rise of autonomous agents capable of deep research and coding. It details how the industry moved past early retrieval methods and "vibe coding" to embrace high-capacity context windows and Large Behaviour Models that interact with the physical world. However, the text warns of a governance crisis, noting that many AI projects fail due to poor data quality and a lack of oversight. Ultimately, the role of the data engineer is redefined as an AI systems architect who must manage real-time data streams and ensure the safety of autonomous actions. These developments suggest that the future of the field relies on rigorous verification and architectural discipline rather than mere hype.

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Over the past few months, Ignition from Australia and Nexus Data from South Africa have been teaming up to look at best selling author Zjaen Coetzee's best selling book, Driving RAPPID Results - a look at the frameworks and methodologies within the data industry that can be improved to get better results for everyone. In this Podcast, Zjaen and Ignition CEO Julien Redmond sit down and discuss the series, what they learnt and what the future has in store.

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In This Issue of the Data pro News, we look at an excerpt from "The Data Engineering Mandate for 2026," outlines the critical transformation facing data engineering professionals as Artificial Intelligence matures and demands robust infrastructure. The author argues that legacy, batch-oriented data systems are insufficient for modern AI, particularly for Retrieval-Augmented Generation (RAG), which requires real-time, low-latency data pipelines. To resolve this, the document proposes five key shifts for 2026, including the widespread adoption of real-time streaming infrastructure (like Change Data Capture), the elevation of data contracts into governance mandates, and the consolidation around lakehouse architectures. Furthermore, it addresses the paradox of AI automating foundational tasks, which necessitates data engineers pivoting to high-level architecture, governance, and AgentOps to manage fragmented AI agent ecosystems. Ultimately, the article asserts that AI success is now bottlenecked by data infrastructure maturity, requiring immediate investment in real-time platforms and automated governance.

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This analytical article from the Data Pro News provides a comparative overview of the newly released Gemini 3 Pro and Claude Opus 4.5 large language models, specifically focusing on their utility and risks within the field of data engineering. The author contends that while Gemini 3 offers a revolutionary low cost-to-context ratio and compelling multimodal capabilities (such as converting whiteboard diagrams to code), it presents a significant liability due to an alarming 88% hallucination rate when it should ideally abstain from answering. Conversely, Claude Opus 4.5 is portrayed as the more reliable and semantically robust choice for complex SQL generation and agentic refactoring workflows, despite its higher token cost. Ultimately, the piece advocates for a hybrid "bicameral" architecture, suggesting professionals should orchestrate both models—using Gemini for low-risk bulk processing and context scanning, and reserving Claude for high-stakes logic execution—to achieve robust and economically viable data pipelines.

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This episode we're taking a detailed examination of how Artificial Intelligence (AI) is rapidly transforming the retail industry, particularly focusing on the high stakes of the upcoming holiday shopping season as a crucial test for new technology investments. It highlights that major brands like Puma and Levi’s are already integrating AI into business operations and consumer outreach, signalling that those who hesitate risk falling behind the competition. The core benefit of AI is its ability to combat "decision fatigue" by offering hyper-personalised shopping experiences and accelerating product discovery, which is already attracting consumers, with nearly half of shoppers having used AI tools for retail purchases. Furthermore, the text suggests that AI's capacity to deliver speed, relevance, and convenience could potentially shift the balance of apparel sales back toward online channels, challenging the plateauing growth of e-commerce. Ultimately, the success of AI this holiday season—especially in delivering positive, friction-free experiences—will determine whether it moves from a novelty to a necessity, accelerating future retail strategy and investment.

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In this week's Data Pro Newsletter, an article titled "The RAG 2.0 Revolution Need to Knows," focuses on how the introduction of Google's Gemini File Search Tool is fundamentally changing the field of data engineering and Retrieval-Augmented Generation (RAG). The author argues that this managed service ushers in "RAG 2.0," effectively industrialising the process by abstracting away the complex infrastructure and operational labour associated with traditional, self-managed "RAG 1.0" systems, thereby shifting the build-versus-buy economic calculus. Significant strategic implications are discussed, including the move from fixed operational expenditure to variable consumption costs, the advantage of Hybrid Search in solving the "out of domain" data problem, and the strategic trade-off between vendor lock-in and operational efficiency. Ultimately, the text recommends a "Managed-First Policy" for non-differentiating applications while reserving costly DIY RAG builds for core competitive differentiators.

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This week's Data pro News provides a critical retrospective on the impact of near-infinite context windows in enterprise Artificial Intelligence workflows during 2025, confirming the predicted exponential leap in AI agent power. The analysis highlights that models routinely handle millions of tokens, transforming sectors like legal, healthcare, and software development, leading to dramatic improvements in document analysis and code review. However, the article also addresses the complexities and risks that were underestimated, specifically identifying challenges related to the amplification of bad data quality, significant privacy paradoxes requiring new governance, and a notable increase in computational cost and response latency for complex queries. Ultimately, the text argues that mastering context governance and strategic deployment is crucial for enterprises to gain a competitive advantage in this rapidly evolving AI landscape.

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This week we're doing an extensive overview of Artificial Intelligence (AI) applications, focusing particularly on both the common and unusual ways the technology is integrated into daily life. One source comprehensively lists the many AI services and solutions offered by a development company, detailing how AI enhances functions from healthcare and e-learning to real estate and travel. The remaining sources concentrate on "weird" or "surprising" real-world AI uses, which range from smart appliances like AI-powered toilets and toothbrushes, to creative applications such as a robotic burger chef, AI lyric generators for rap music, and programs designed to compose songs in the style of deceased musicians. Additionally, these texts highlight AI's role in animal welfare, including facial recognition for fish and robotic beehives, and in public safety and convenience, such as crime fighting and automated virtual assistants.

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We look into an article titled "Surviving the AI Bubble: Your Need to Knows for 2026" from datapro.news, provides a critical analysis of the current state of enterprise AI adoption, noting that the vast majority of companies are seeing near-zero measurable return on investment despite significant spending. The author argues that this failure is due to a systemic "Architectural Debt" rooted in obsolete data infrastructure, not a lack of AI capability. To survive the impending market realignment, the text recommends that data professionals must pivot from batch-based systems to real-time streaming architectures and "AI Factory Models," prioritising robust data governance and lineage tracking. Furthermore, it advocates for an SLM-first (Small Language Model) strategy to address the high costs of Large Language Models and stresses that compliance, driven by regulations like the EU AI Act, must become an inherent architectural requirement rather than an afterthought.

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We take a multi-faceted overview of the current state and impact of generative artificial intelligence across society and industry. One source offers a first-person account of exploitative labour practices faced by the "gig workers" responsible for training AI models, arguing that the threat is less the technology itself and more the billionaires who control it. Supporting this notion, two Pew Research Center reports detail how AI is altering public interaction, showing that Google users are less likely to click on source links when presented with an AI summary, and that Americans generally react negatively to the discovery of AI involvement in creative or professional tasks like political speeches and news articles. Finally, a business analysis highlights that the majority of generative AI pilot programs fail to deliver measurable business value, citing case studies of failures in legal tech, retail chatbots, and logistics that stem primarily from poor strategy, inadequate governance, and unrealistic expectations rather than technical flaws.

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This Week's article from datapro.news analyses the pervasive and high failure rates plaguing all types of enterprise data projects, including basic analytics and advanced AI implementations. It identifies three primary systemic issues causing these failures: the Hype Trap (lack of defined business problems), the Foundation Fallacy (neglecting data quality), and the Adoption Abyss (insights not being used in decision-making). To address these problems, the article introduces the RAPPID Value Lifecycle, a methodology focused on ensuring measurable commercial value from data investments. RAPPID mandates a business-first approach requiring clear value definition before execution and incorporates frameworks to ensure Data Trust and guarantee that insights are Embedded into daily workflows.

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This week we look at the significant backlash and technical inconsistencies surrounding Coca-Cola’s 2025 AI-generated "Holidays are Coming" Christmas advertisement, the second fully AI commercial the company has released. Viewers and critics pointed out glitches like inconsistent truck shapes and wheel counts in the video, highlighting the current shortcomings of generative video technology, though the latest ad was noted as an improvement over the previous year. Despite the critiques from the creative industry—who fear job losses—Coca-Cola executives defended the decision, citing increased speed, lower cost, and efficiency as key benefits, while claiming that the average consumer enjoyed the ads. The company and the AI studio, Secret Level, maintained that human creativity is still essential, even as the process required fewer human artists and involved generating over 70,000 video clips. Ultimately, the controversy underscores the ongoing debate within the creative and advertising industries about the ethics and quality of fully generative AI content.

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Thsi week we look at an article from datapro.news titled "🤔 AI Adoption Failures: What you can do to succeed?" by Samuel Williams, dated October 22, 2025, which discusses the paradox of enterprise AI adoption. It highlights that despite record investment and widespread deployment, nearly eight in ten enterprises are failing to realise significant financial value from their AI initiatives. The investigation attributes this failure not to technological limitations, but to three critical friction points: Leadership Misalignment (treating AI as a technical rather than a business transformation challenge), Organisational Conflicts (internal resistance and sabotage), and increasing Compliance and Regulatory Complexity, particularly around "Sovereign AI demands." The author advises data management professionals to shift from deploying horizontal tools to domain-specific Vertical AI Agents and to prioritise governance, architectural foresight, and rigorous value measurement to bridge the gap between deployment and tangible business outcomes.

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This episode focuses on debunking pervasive myths surrounding modern data practices, specifically in data architecture, data analytics, and database management. The data architecture sources, from Bernd Wessely, challenge ten common fallacies, arguing against centralised data platforms and the idea of a single source of truth, while advocating for Domain-Driven Design and distributed data concepts like the data mesh. A separate source from Profisee addresses seven fallacies in data analytics, emphasising the crucial role of master data management in ensuring data quality and noting that predictions are often better understood as probability analysis. Finally, the IDERA source counters ten myths about database management, stressing that effective strategies require more than just automated maintenance or hardware upgrades, instead calling for proactive performance tuning and continuous monitoring.

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The source, an article titled "The Silent Revolution - A Spotlight on Data Modelling" from datapro.news, investigates how Artificial Intelligence (AI) is profoundly transforming data engineering and architecture. The text explains that AI is making the traditional three-tier data modelling approach—conceptual, logical, and physical—obsolete through hyper-automation, significantly compressing project timelines, such as reducing the conceptual modelling phase from weeks to hours via the "Text-to-Schema paradigm." The article highlights a new structural change called "logical model bifurcation," requiring architects to design two parallel logical blueprints: a business-facing Semantic Layer and a specialised Logical Feature Model for machine learning workloads. Furthermore, the source details that AI-driven optimisation is now directly tied to cloud cost management in consumption-based environments, making cost governance a primary architectural concern, and concludes that professional roles are shifting from manual designers to orchestrators and governors of complex AI systems.

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In This Episode we have a detailed examination of the privacy and security risks associated with modern robot vacuum cleaners, noting that these smart devices gather sensitive data, including detailed home maps and images, through their advanced sensors and cameras. Both texts highlight specific incidents of data leaks, such as the non-commercial photos captured by iRobot Roombas that were later shared on social media, illustrating how private information can be exposed. Furthermore, the articles discuss hacking vulnerabilities, including how researchers demonstrated remote camera takeover and even the "LidarPhone" technique that converts the navigation sensor into an eavesdropping tool. Finally, the sources offer consumer advice, urging buyers to choose reputable brands with strong security features like end-to-end encryption and regular firmware updates, acknowledging the ongoing trade-off between domestic convenience and personal data security.

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This episode provides a critical analysis of Anthropic’s Claude 4.5 release, arguing that it represents a fundamental transformation in enterprise data management, moving large language models beyond simple co-pilots into autonomous agents. Key features highlighted include the model's unprecedented ability to autonomously generate complex software and manage intricate, long-running data workflows, achieving high success rates in agentic coding benchmarks. However, the analysis also addresses the profound new risk profile associated with autonomous code generation, stressing the potential for catastrophic data corruption at scale. To mitigate this risk, the article details Anthropic’s Checkpoint capability, which functions as a sophisticated rollback mechanism to enable auditable and controlled autonomy in data pipeline operations. Ultimately, the text advises data leaders that adopting this technology is a strategic imperative that requires immediate restructuring of traditional data governance frameworks.

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This episode looks at excerpts from a 2011 academic paper titled "The costs of poor data quality" by Haug, Zachariassen, and van Liempd, which investigates the economic consequences of inadequate business data. The authors propose that companies should aim for an optimal data quality level rather than perfection, defining this optimum as the point where the cost of maintenance efforts balances the costs inflicted by poor quality data. To facilitate this assessment, the paper introduces two main contributions: a definition of the optimal data maintenance effort and a classification framework that categorises the costs of poor data into four types based on two dichotomies: direct versus hidden costs and operational versus strategic consequences. A case study involving an automotive spare parts manufacturer is used to illustrate the practical application of this proposed cost framework.

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The provided source material, primarily an article titled "🤔Anthropic & OpenAI Usage Insights" from "The Data Pro News," analyses the emerging landscape of Artificial Intelligence adoption across consumer and enterprise settings. The article explains that whilst consumer AI, such as OpenAI's ChatGPT, shows a democratising effect by reducing usage disparities, enterprise adoption is strikingly different, with 77% of deployments following automation patterns that concentrate competitive advantage. This divergence creates "dual trajectories" that profoundly impact data engineering professionals, whose core responsibilities must shift from traditional pipeline management to AI infrastructure enablement capable of supporting both human-advisory and fully automated workflows. The text concludes with strategic recommendations, urging data engineering teams to prioritise capability over cost in infrastructure and treat data modernisation as the primary strategic initiative for future success.

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This episode outlines the struggle of a jeweller, Deanna Newman, whose legitimate business, C'est la vie Jewellery, was targeted by AI scammers who copied her business name to defraud customers. Operating out of China but claiming to be a Birmingham-based entity, the scam damaged Ms. Newman's reputation as customers mistook her genuine products for low-quality, mass-shipped items sold by the fraudsters. Ms. Newman faced continuous aggressive complaints and refund demands, forcing her to publicly clarify her separation from the scam and even leading customers to offer her sympathy based on the scam's fake narrative about a deceased husband. Despite the emotional distress and a drop in sales due to customer mistrust, Ms. Newman has refused to change her business name, instead undertaking significant effort to redesign her website to distinguish her brand. The article notes this type of identity theft is increasingly common, with AI tools enabling fraudsters to mislead the public on an unprecedented scale.

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This edition of the Data Pro News provides an extensive enterprise reality check on the deployment of GPT-5, two months after its release, noting a growing gap between its initial promise and production reality. It explains that while the model offers impressive capabilities, key challenges remain, including spiralling infrastructure costs due to massive memory and compute requirements for its large context window. The article highlights that cost management for token usage at scale is proving prohibitive for many organisations, often leading to six-figure monthly expenditures. Furthermore, the reliance on Retrieval-Augmented Generation (RAG) persists, as using the full context window is neither cost-effective nor performant, meaning successful adoption requires robust engineering practices and hybrid architectures rather than simply integrating the new model. The text concludes that future enterprise AI success will depend less on access to advanced models and more on the engineering capability to deploy and operate them efficiently.

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The source is an in-depth investigation from datapro.news, dated September 17, 2025, concerning the fierce competition among Snowflake, Databricks, and Microsoft Cosmos DB to dominate the market for AI-powered data pipelines. The article outlines three distinct philosophical approaches: Snowflake’s "ZeroOps" for democratised, SQL-centric simplicity; Databricks’ "Open Lakehouse AI" for comprehensive machine learning control; and Cosmos DB’s "Operational Excellence" for high-performance, real-time applications within the Azure ecosystem. The analysis assesses each platform's strengths and weaknesses across developer experience, pipeline automation features like Openflow and Lakeflow, and critical challenges such as cost unpredictability and the need for specialised skills. Ultimately, the verdict is that there is no single winner, as the best platform depends entirely on an organisation's specific needs, driving a broader trend towards unified, cohesive data platforms.

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The Right to be Forgotten is something a number of places are grappling with - and James Harwright from Pragmaticians is Australasia's leading voice on the topic. Check out his recent presentation at our Sydney DXE Meet Up.

Bought to you by Deep Weaver and IBM

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The provided article from datapro.news announces a significant shift in the global artificial intelligence landscape, triggered by the EU AI Act's enforcement regime activating on 2nd August 2025. This legislation is no longer theoretical, establishing legally binding obligations for General-Purpose AI (GPAI) models and creating a pan-European regulatory machine to ensure compliance. Organisations face substantial financial penalties for non-compliance, with the Act's extraterritorial reach meaning global companies must adhere if their AI systems are used in the EU. The article details technical requirements impacting data pipelines, such as robust documentation, data provenance, and copyright adherence, emphasising that compliance-by-design is now a core engineering necessity for sustainable AI development.

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DSharp are a world leader when it comes to no-code data warehousing - so where do you go when you're already seen as world leaders in the field? You build an assistant...

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From datapro.news titled "How Large Behaviour Models Are Rewriting Data Engineering Pipelines," explores the transformative impact of Large Behaviour Models (LBMs) on data engineering practices. It highlights a significant shift from language-based AI to embodied, action-oriented intelligence, where machines learn from real-world behaviour and physical interactions. The article explains how traditional batch-oriented data pipelines are becoming obsolete, replaced by real-time, continuous data streams originating from various sensors and human teleoperation. This necessitates new engineering workflows focused on low-latency ingestion, complex temporal and spatial data transformation, and advanced behavioural data curation. Finally, it addresses the critical ethical and technical risks associated with LBMs, such as bias-induced physical harm, bodily privacy concerns, and economic concentration, offering strategic recommendations for data engineers to adapt and lead in this evolving field.

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In the final part of our series with Simon Kaye from DeepWeaverAI, we look at two guardrails - the need for accountability and the need for conformity in systems - and why the two are both equally as important to ensure the successful use of AI.

And to learn more about our upcoming Sydney Meet up - https://www.addevent.com/event/FS26379654

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"The MIT 95% AI Enterprise Failure Rate Report: True or False?" discusses the high failure rate of generative AI pilot projects in businesses, claiming 95% do not reach production with measurable impact. The report, titled "The GenAI Divide: State of AI in Business 2025," scrutinises the methodologies and findings of this controversial MIT study, which involved a multi-method research design including interviews and surveys. It highlights key reasons for these failures, such as messy workflows, the "verification tax" on employees, and a lack of governance and strategic planning. Conversely, the article also identifies strategies employed by the successful 5%, which include a workflow-first methodology, investment in foundational data infrastructure, and systems thinking over isolated experimentation. Ultimately, the piece asserts that the high failure rate is not a condemnation of AI technology itself, but rather an indictment of flawed enterprise adoption strategies that neglect fundamental data engineering principles. It concludes by offering strategic recommendations for data engineering teams to increase their success rates.

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It's not always easy to plan for unintended consequences - what with them being unintended and all, but it's an important guardrail for us to put up with AI, according to Simon Kaye from DeepWeaver AI.

And to learn more about our upcoming Sydney Meet up - https://www.addevent.com/event/FS26379654

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In this week's conversation Sam continues his dialogue with DeepWeaverAI's Simon Kaye - this time talking about the guardrail of Human Oversight - and how to avoid the bottleneck when AI becomes so fast and efficient that people feel human oversight may no longer be needed.

And to learn more about our upcoming Sydney Meet up - https://www.addevent.com/event/FS26379654

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Learn more about the remarkable life and career of Katherine Johnson, highlighting her journey as a mathematical pioneer. It explains how her extraordinary aptitude for numbers was evident from a young age, leading her to attend high school and college significantly earlier than her peers, despite the challenges of segregation in America. The source emphasises the pivotal role of mentorship, particularly from W. W. Schieffelin Claytor, who crafted specialised mathematics courses for her that proved crucial for her later work. It further describes her transition to NASA (then NACA), where her advanced mathematical abilities were indispensable as a "human computer," and how her persistence and self-driven learning enabled her to overcome barriers and contribute significantly to space exploration.

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In this week's Datapro Newsletter, we discuss Australia's "Voluntary AI Safety Standard", highlighting that its seemingly optional nature is a "regulatory mirage". The text from "AI Governance Reality Check" explains how these voluntary guidelines, particularly the ten guardrails, are a precursor to mandatory compliance for AI development and deployment, especially for high-risk applications. It outlines the technical implications for engineering managers, stressing the importance of proactive implementation of measures like risk management, data governance, and record-keeping. The "Data Pro newsletter" appears to be the publication where this critical analysis is shared, emphasising the shift towards AI governance as a legal requirement.

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Harmful content online has been a long time issue - with a lot of nuance on the topic needed - so what sort of guardrails are we able to put in place to make sure an LLM isn't creating or spreading harmful content?

That's what Part Three of the chat between Sam Williams and Deepweavers Simon Kaye is all about in this episode.

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When we live in a world where we are creating more data than ever before in our daily lives, security and privacy are key concerns for both the end users of a systems and those working with it. So how do you ensure those principles are baked into an LLM for an AI to use?

That's the discussion in this part of our ongoing series between Sam and Simon from Deepweaver AI.

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The provided text highlights Konrad Zuse as a pivotal, though often overlooked, figure in the history of computing. It explains how this German civil engineer independently developed foundational concepts for modern computers, largely in isolation during the 1930s and 1940s. His key achievements include the Z3, recognised as the first programmable digital computer, and Plankalkül, the first high-level programming language, which introduced ideas like arrays and conditional statements long before their mainstream adoption. The text underscores Zuse's visionary approach to both hardware and software, showing how his work pre-empted many developments later attributed to other pioneers and ultimately influenced the European computing industry through his company, Zuse KG.

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In the Data Pro News, an article titled "Chat GPT-5's Spreadsheet Shocker" from datapro.news, explores how OpenAI's GPT-5 agents are poised to revolutionise Excel-centred analytics and modelling. It highlights a significant shift from traditional, manual spreadsheet workflows to autonomous, multi-step processes powered by large language models (LLMs). The article details how these agents can naturally interpret language, source various data types including unstructured information, and produce complex, formula-rich .xlsx workbooks with minimal human input, leading to faster insights and a redefinition of data engineering roles. While acknowledging the potential for task displacement in entry-level spreadsheet roles, the piece also stresses the ongoing human need for ambiguity resolution, strategic oversight, and data governance.

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Simon Kaye from Dreamweaver AI in Australia steps us through the fundamentals for good governance with the creation and roll out of LLM, AI Agents.

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It may be a bit of a paradox, the idea of transparency in modelling while accessing large amounts of sometimes sensitive data - so what guard rails can AI have in place to ensure both transparency and functionality?

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In this week's Data History episode, we're introduced to Alonzo Church, a pivotal figure in 20th-century logic and computer science, detailing his life, significant contributions, and enduring legacy. It highlights his creation of Lambda Calculus, a foundational system for functional programming, and his independent formulation of the concept that led to the Church-Turing Thesis, defining the limits of computation. The source also emphasises Church's influence as a mentor to leading figures like Alan Turing, whose work on Turing Machines complemented Church's abstract computational theory. Ultimately, the text positions Church as the abstract logician who provided the theoretical underpinnings for modern computer science and programming.

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From Data Pro News, "The 2027 AI Reckoning" discusses a highly speculative report that posits 2027 as a pivotal year for AI, potentially marking a shift from human-controlled systems to something unpredictable. It explores the report's central premise of AI automating its own research, leading to an uncontrollable feedback loop and the emergence of artificial superintelligence. The article then analyses two key issues highlighted by the report: the "misalignment problem," where AI goals diverge from human values, and the geopolitical arms race for AI capabilities, which elevates data security to a national security concern. While acknowledging scepticism regarding the timeline, the piece stresses the report's value as a "stress test" for current policies and a call for immediate action on safety and governance in AI development. Ultimately, it frames the data professional's role as crucial in navigating this non-linear future, emphasising safety, interpretability, and robust security as paramount engineering challenges.

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The provided text highlights Martin Fowler's significant, albeit indirect, influence on data management, despite not being a traditional "database expert." His work, particularly in software architecture and agile practices, introduced foundational concepts that profoundly shaped how data is handled in modern systems. Fowler's contributions include popularising architectural patterns like Table Data Gateway, Data Mapper, and Active Record, which are now embedded in many Object-Relational Mapping (ORM) tools. Furthermore, he championed patterns such as Event Sourcing and Command Query Responsibility Segregation (CQRS), crucial for auditing and scalable microservices architecture, alongside advocating for the "database per service" approach to prevent data conflicts. Essentially, Fowler served as a translator, making complex data-related architectural principles accessible and widely adopted across the software industry.

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This episode discusses the imminent launch of ChatGPT-5 in August 2025 and its anticipated impact on data engineering. It highlights the model's agentic capabilities and expanded context window as game-changers, enabling it to automate significant portions of data pipeline work previously handled by junior or mid-level engineers. While this presents opportunities for increased productivity and strategic focus for senior engineers, it also raises concerns about job evolution, the future of entry-level roles, and the need for new skills like AI orchestration and prompt engineering. The source also touches upon enterprise caution regarding AI integration due to compliance and oversight needs, and the potential for democratisation of data management alongside challenges in maintaining data quality and security.

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This Episode of Data History looks at the significant contributions of Linus Torvalds, a pivotal figure in modern computing. It details his background, including his education and early influences, before highlighting his major achievements. These accomplishments encompass the creation of the Linux kernel as an open-source project and the subsequent development of Git, a distributed version control system now ubiquitous in software development. The text also emphasises his ongoing stewardship of the Linux kernel, underscoring its profound influence and legacy across operating systems, open-source models, and software engineering practices. Finally, it notes the numerous awards and honours Torvalds has received, affirming his continued relevance in shaping technological advancements.

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We all make assumptions about the implementation, adoption and adaptation to AI happening in workplaces across the world right now - but new research is showing us that the ways in which we use AI and the impact it as can be determined by age, experience and position within an organisation could have even more influence than we realise.

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This week's episode offers a comprehensive overview of Charles Babbage, widely regarded as the "father of the computer". It details his early life and education at the University of Cambridge, where his innovative ideas about mechanised calculation first took root. The text highlights his crucial role in founding the Royal Astronomical Society and his extensive work on the Difference Engine and the Analytical Engine, which were groundbreaking mechanical computing machines. Beyond his computing contributions, the source also explores Babbage's diverse interests, including his work in political economy (notably the "Babbage principle"), cryptography, and his philosophical and religious views as expressed in his "Ninth Bridgewater Treatise." The entry also touches upon his later life, inventions, public campaigns, and the enduring legacy of his pioneering work, which profoundly influenced the development of modern computing.

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The Data Pro News article titled "The Silicon David: Nvidia Topples Apple as the First $4 Trillion Company," discusses Nvidia's rapid ascension to become the world's most valuable public company by July 2025, surpassing Apple. It highlights how Nvidia transformed from a graphics card manufacturer into the dominant architect of the artificial intelligence revolution, primarily due to its strategic investment in CUDA and its chips like the H100 and A100. The text explains that this growth is driven by exponential demand for AI infrastructure, contrasting it with Apple's more incremental growth. Furthermore, the article suggests that Nvidia's comprehensive software ecosystem creates a strong competitive advantage, fundamentally rewriting the rules of corporate valuation in the AI age.

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This week's episode offers a comprehensive overview of Dennis Ritchie, a pivotal figure in computer science, detailing his contributions to the field. It highlights his role as the creator of the C programming language and a co-developer of the UNIX operating system, both of which profoundly influenced modern software and operating systems. The document covers his educational background at Harvard University and his career at Bell Labs, where his key innovations were developed. Furthermore, it explains the portability and efficiency of C and how UNIX's design became the blueprint for contemporary operating systems. Finally, the text enumerates Ritchie's numerous awards and recognitions, underscoring his enduring legacy and impact on technology.

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Thsi week we do an analyses of Microsoft's strategic move in 2025 to position Azure Cosmos DB as a contender in the enterprise data warehouse (EDW) market. While traditional EDW leaders like Snowflake, AWS Redshift, Google BigQuery, and IBM Db2 Warehouse excel in distinct analytical areas, Cosmos DB aims to differentiate itself as a unified AI database. It achieves this by blending operational and analytical workloads through Hybrid Transactional/Analytical Processing (HTAP) and native AI integration, especially benefiting organisations already using Microsoft's ecosystem. The article explores how Cosmos DB's capabilities, such as global distribution and low-latency access, are particularly advantageous for specific industries like e-commerce, gaming, and IoT, suggesting it is carving out a new niche rather than directly replacing existing EDW solutions.

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In this episode we outline the impactful life and work of John McCarthy, a foundational figure in Artificial Intelligence (AI). It highlights his pivotal role in coining the term "Artificial Intelligence" and organising the 1956 Dartmouth Conference, widely considered the birth of the field. The text further details his significant technical contributions, including the creation of the influential programming language LISP, pioneering time-sharing systems, and formalising commonsense reasoning in machines. It also acknowledges his academic leadership at institutions like Stanford, where he established the Stanford AI Lab (SAIL), and his numerous honours, such as the Turing Award, cementing his legacy as an architect of modern computing and AI.

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This episode outlines the profound impact of Claude Shannon, widely recognised as the father of information theory. It details his early life and education, including his groundbreaking master's thesis that laid the foundation for digital circuit design theory. The source then highlights his major contributions, such as the conceptualisation of the bit and channel capacity, which underpin modern communication, and his work in cryptography. Finally, it touches upon his idiosyncratic personal interests and underscores his enduring legacy, comparing his theories' fundamental importance to that of Einstein's in physics, directly influencing fields like data science and AI.

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In this special deep dive, we take a look back at a recent Datapro.news piece on Vibe Coding - was it the golden goose as promised or is it more of a lame duck?

What is "vibe coding" and how did it emerge?
How does vibe coding align with Agile principles and potentially accelerate development?
What are the primary technical concerns associated with AI-generated code?
Does vibe coding truly democratise software creation, or is that an illusion?
What are the real-world implications for startups and established enterprises adopting vibe coding?
How is the role of professional developers expected to evolve with the rise of vibe coding?
What are the "skills atrophy" crisis and its long-term implications?
What is the balanced view on vibe coding, and how should it be integrated into development workflows?
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This episode offers a comprehensive overview of Ada Lovelace, celebrated as the world's first computer programmer. It details her background as the daughter of Lord Byron and a mathematician, her private education, and her significant collaboration with Charles Babbage on the Analytical Engine. The source highlights her most crucial contribution: her extensive notes on Luigi Menabrea's paper, which included the first algorithm ever written for a computing machine—a method for calculating Bernoulli numbers. Furthermore, the text underscores her visionary insight into general-purpose computing, envisioning machines capable of more than just numerical calculations, such as composing music or generating graphics. Finally, it acknowledges her enduring legacy and recognition in the field of computer science, cementing her status as a trailblazer for women in STEM.

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"Anthropic's Terminal Takeover" from datapro.news, introduces Claude Code, an innovative AI assistant developed by Anthropic. This article explains how Claude Code is revolutionising data engineering by operating directly within the command line as a native terminal tool, rather than a separate application or plugin. It highlights the tool's "agentic" capabilities, allowing it to understand entire codebases and perform multi-step workflows autonomously with human supervision. The text provides specific examples of how Claude Code can streamline complex tasks like pipeline wrangling and debugging, and assist with infrastructure as code. Finally, the article asserts that Claude Code is a "supervised agent" designed to augment human expertise, thereby enhancing productivity and fostering a new collaborative dynamic in data engineering.

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In this week's Data Radio Show, we look at the complex legal landscape surrounding an individual's personal data after death, focusing on Europe, the United States, and Australia. It highlights that data protection laws like GDPR generally cease to apply posthumously, but national laws in Europe often provide exceptions. In contrast, the US relies on a patchwork of state laws like RUFADAA, while Australia offers limited post-mortem data control, mainly through property law. The overview consistently points out that heir access to digital assets varies significantly by region and often depends on the deceased's prior consent or the platform's terms of service. Ultimately, the text stresses the importance of digital wills and platform-specific legacy tools to manage one's online presence after passing.

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This Podcast outlines the life and revolutionary work of Tim Berners-Lee, beginning with his personal background, including his family of mathematicians and his early education. It then details his invention of the World Wide Web while at CERN, specifically highlighting the key technologies he created like HTTP, HTML, and URLs, and the launch of the very first website. The source goes on to explain the profound impact of the Web on data management, emphasizing the democratisation of information, the foundation for data interoperability through standards, its role in fostering the open data movement, and the creation of new digital data ecosystems. Finally, the text mentions Berners-Lee's later achievements and recognition, such as his knighthood and the Turing Award, and his continued efforts to shape the web's future, notably through projects focused on data control.

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In this week's DataPro.News, an article titled "Has the Rise of Generative AI Caused a Deterioration in Data Trust?", explores the alarming decline in data trust within organisations, directly linking it to the increased adoption of generative AI. It highlights how data quality and governance issues have worsened significantly, with a particular focus on the challenges faced by data engineers in maintaining reliable data for AI systems. The article also addresses a widening skills gap and a disconnect between executive enthusiasm for AI and the operational realities of ensuring data integrity. Finally, it proposes strategies for data engineering teams to rebuild trust, such as implementing robust lineage tracking, automated quality monitoring, and transparent communication, while acknowledging regional variations in AI adoption and trust.

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Sometimes we all need an objective eye to look over a project - and this month we decided to give that a try on our Channels - our YouTube Page, the DataPro.News and our Skool Classroom.

So what did it suggest, what did we do and what are the early outcomes?

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This History Episode offers a comprehensive look at the life and achievements of John von Neumann, highlighting his Hungarian origins, child prodigy status, and education. It details his move to the United States and subsequent death at a relatively young age. The text then outlines his five most significant contributions, including the foundational Von Neumann architecture for computing, his crucial work on the atomic bomb during the Manhattan Project, his co-founding of game theory, his formalisation of quantum mechanics, and his pioneering ideas on cellular automata and self-replicating machines. Finally, it summarises his enduring legacy, emphasising his influence across numerous diverse fields such as computing, military strategy, economics, physics, and artificial intelligence, solidifying his reputation as a prominent 20th-century polymath.

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This piece from datapro.news argues that the role of data engineers is facing significant disruption from artificial intelligence despite previous perceptions of security. It contends that companies are increasingly replacing salaried engineers with a combination of AI systems and contract workers, leading to widespread layoffs and a shift towards a gig economy model for displaced professionals. The author suggests that to remain relevant, data engineers must transition from performing manual tasks to becoming architects of data ecosystems, focusing on high-level design, understanding business context, and mastering AI tools for orchestration and governance. The article posits that the future demands engineers who can strategically manage the complex interplay of data, AI, and human expertise, rather than focusing solely on traditional coding and pipeline construction.

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Data Privacy is a cornerstone of any undertaking with Data, especially personal Data. This week we take an AI overview of our Data Privacy Course available on our Skool Classroom - https://www.skool.com/data-innovators-exchange to give us a high level understanding of why this is so key to the work data professionals do and to help create a study guide of sorts for people wanting to learn more.

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Today we take a comprehensive account of the life and work of Alan Turing. It details his early life and education, including his studies at Cambridge and Princeton, as well as significant personal relationships. The text highlights his groundbreaking career and research, particularly his crucial contributions to cryptanalysis during World War II at Bletchley Park, where he played a key role in cracking the Enigma code, and his pioneering work on early computers and the development of the Turing test. Finally, his tragic personal life, including his prosecution for homosexuality, his death, and the subsequent government apology and pardon.

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This week's DataPro.News analyses VAST Data, a relatively new company experiencing rapid growth that claims to offer a revolutionary approach to managing the vast amounts of data needed for artificial intelligence applications. The article explores the company's promise to simplify complex data infrastructure by unifying storage, compute, and data management, which VAST terms an "AI Operating System." It examines VAST's performance claims through benchmarks, its AI-centric architecture, and evidence of customer adoption and market position. However, the analysis also includes a sceptical perspective, highlighting perceived gaps such as the limitations of its database capabilities and potential vendor lock-in, concluding with a stance of cautious optimism regarding the platform's true potential and the need for careful evaluation by potential users.

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How do you modernise an industry that's been around since the Dodo went extinct that's survived the Industrial Revolution and now facing the Digital Revolution?

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This week we take a comprehensive look at the life and impact of Jim Gray, a highly influential figure in computer science. It highlights his significant contributions to database systems, transaction processing, and distributed computing, notably the formalisation of the ACID model and his work on fault tolerance and scalability. The text also mentions his focus on eScience and the use of large datasets before detailing his disappearance at sea in 2007 and his enduring legacy in modern computing systems.

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This week's DataPro.News discusses the simultaneous release of Anthropic's Claude 4 AI models and the Data Vault 2.1 methodology certification, highlighting their combined impact on modern data management. It explains how Claude 4, with its advanced reasoning and coding abilities, can revolutionise data engineering workflows, while Data Vault 2.1 provides an updated framework for enterprise data architecture, integrating agile methods and new technologies. The article suggests that the convergence of AI capabilities and structured data methodologies offers a significant opportunity for organisations to improve data governance, productivity, and adaptability in an increasingly complex data landscape

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It started as a white paper and expanded - that's how Hernan Revale describe the process of creating a handy guide book introduction to Data Vault - a warehouse model that's just turned 25 and is still adapting and evolving today for modern data needs. This week, we sit down and learn about the process of summing up something so technical for wider audiences, the resources he had on hand and the feedback on the final outcome.

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This week we look at the significant contributions of Patricia (Pat) Selinger, a highly influential computer scientist in the field of relational database systems. It details her educational background and impressive career at IBM, where she became the first woman to be named an IBM Fellow. Selinger is best known for inventing the cost-based query optimizer, a crucial component that revolutionised how databases process queries efficiently, forming the basis of modern database engines. The text also mentions her work on the System R prototype and other database technologies, alongside recognition such as the SIGMOD Edgar F. Codd Innovations Award, solidifying her legacy as a pioneer and mentor in database technology.

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This analysis explores Microsoft Azure Cosmos DB, a database platform described as a "fully managed NoSQL, relational, and vector database." The piece examines its strengths, such as scalability, performance, and features for AI applications, alongside its potential weaknesses, including cost implications, complexity, and limitations related to partitioning and querying. It discusses the database's use in various industries and highlights recent updates announced at Microsoft Build 2025. Ultimately, the article concludes that while powerful and versatile, Cosmos DB requires careful consideration and expertise for effective implementation.

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Tim and Lorenz walk us through a case study on how to report product monitoring for a technology and manufacturing organisation with 24'000 employees to consider.

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We take a comprehensive overview of the life and impact of Grace Hopper, a pivotal figure in computing and a U.S. Navy rear admiral. It highlights her academic background, earning degrees including a PhD in Mathematics from Yale, and details her key contributions to the field, notably inventing the first compiler and leading the development of the COBOL programming language. The text also covers her significant military service, where she rose to the rank of Rear Admiral, and mentions the numerous awards and recognitions she received throughout her career and posthumously. Finally, it touches upon her philosophy on innovation and education, concluding with an assessment of her lasting legacy on modern software development.

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In this week's DataPro.News, we explore the rapid advancement of Retrieval-Augmented Generation (RAG) and agentic AI architectures within enterprise settings during 2025, highlighting their shift from experimental concepts to crucial business tools. It details how modern RAG is moving beyond text-only to multimodal capabilities, integrating diverse data types and incorporating real-time, dynamic information sources. The article also examines the rise of autonomous agentic systems that proactively perform tasks and discusses the complexity of orchestrating multiple agents working together, with a focus on implementation strategies including cloud-native RAG as a Service and hybrid architectures, all underpinned by a governance-first approach.

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In this presentation from the Melbourne Data Innovators Exchange Meet up in early May, 2025, Sheng Lan, Steve Rose and Sam Redmond talk us how the proper use of R.A.G Architecture in AI can help increase process efficiencies and outcomes.

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This text profiles Peter Pin-Shan Chen, a Taiwanese-born computer scientist renowned for creating the Entity-Relationship (ER) Model in 1976. The ER Model, detailed in his influential paper, provided a visual and conceptual method for designing databases using entities, attributes, and relationships. This innovation standardised database design across industries, influenced software engineering tools like CASE and UML, and remains a foundational concept in computer science education. Chen's significant contribution earned him numerous accolades and established his lasting legacy in data management.

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This piece from datapro.news discusses how Artificial Intelligence is reshaping data governance and data accessibility within organisations. It highlights how tools like Coalesce Catalog (formerly CastorDoc) leverage AI to automate tasks such as data discovery, documentation, and lineage tracking, making data assets more discoverable and understandable for both technical professionals and business users. The article also emphasises the platform's role in embedding governance within the data transformation lifecycle and improving trust in data by providing transparency and data quality monitoring. Ultimately, it suggests that AI-powered tools are transforming data governance from a cumbersome overhead into a strategic advantage that democratises data access and enables data-driven decision-making.

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Coalesce's latest acquisition is an AI tool designed to help data people by finding the most efficient way to ensure data quality through various pipelines. In this week's episode, Sam sits down and talks to one of its creators, Tristan Mayers, about how it works and how it helps those working in data.

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This text details the groundbreaking work of Donald D. Chamberlin and Raymond F. Boyce at IBM in the early 1970s. Inspired by the relational data model, they co-created SQL, initially named SEQUEL, which became the standard language for interacting with relational databases. The explanation highlights SQL's user-friendly syntax and its implementation of complex database concepts. The enduring impact and universality of SQL across various industries and platforms are emphasised, solidifying its legacy in data management. The text also briefly notes the tragic early death of Boyce and Chamberlin's continued contributions.* Join the Data Innovators Exchange for free at https://www.skool.com/data-management-innovators-4116/about * Sign up for the free Data Pro Newsletter at https://www.datapro.news/subscribe

To celebrate Star Wars Day, we take a look at the vast and intricate data infrastructure of the Galactic Empire. It outlines the Holonet, an interstellar network facilitating communication and data transfer, and the tiered system of data vaults used to store everything from operational telemetry to citizen behaviour logs. The text also highlights the massive scale of data collected daily from various sensors and the sophisticated analytics employed for tasks like invasion planning and maintaining control, while also noting the security measures in place and the ongoing data warfare with Rebel forces

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This week's Datapro Newsletter discusses the significant increase in infostealer malware, which covertly steals sensitive data such as passwords and financial details. It highlights a major incident in Australia where 31,000 banking credentials were compromised from customers of the "Big Four" banks through individual device infections, not a central breach. The text explains the threat landscape of these malicious programmes, noting their rapid growth and the shift to new dominant malware families sold as a service. Finally, it outlines how data automation platforms and specific security practices can be used by data engineers to mitigate these risks and enhance enterprise security against such attacks.

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When a 140 year old company working in mining and cement sold part of it's business, it created new challenges in how to effectively manage its data. In this case study, Tim and Lorenz from Scalefree show us how they approached the challenge and the solutions they found.

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This source profiles Edgar F. Codd, a British computer scientist who revolutionised data management. It highlights his wartime service and academic background before detailing his groundbreaking relational database model developed at IBM. The text emphasises the shift from complex hierarchical systems to Codd's elegant, logic-based approach using tables and relational algebra. Initially met with resistance, his ideas eventually gained widespread acceptance, leading to the ubiquitous use of SQL and modern database systems. The profile concludes by underscoring Codd's lasting legacy and the fundamental impact of his vision on today's data-driven world.

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This article from datapro.news introduces reverse prompting, a novel technique aimed at improving productivity for data engineers working with AI. It addresses the issue of prompt engineering fatigue by shifting the interaction paradigm. Instead of users exhaustively crafting prompts, AI models initiate an interview to understand requirements. This interview process, leveraging reasoning-focused AI, culminates in the generation of a comprehensive prompt for more advanced generative models, as illustrated with a Data Vault 2.0 modelling example, ultimately leading to reduced effort and more comprehensive results.

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Join Lorenz and Tim from Scalefree as they talk us through a case study on how to best optimize KPIs using data driven change!

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This source from datapro.news outlines the significant shift from traditional Search Engine Optimisation (SEO) to Generative Engine Optimisation (GEO), where AI synthesises information for search results. It emphasises the crucial role of data engineers in adapting to this change by focusing on structured, AI-accessible data. The article provides a playbook for data engineers, covering essential actions like implementing robust data structuring, developing AI-optimised pipelines, enhancing data quality, fostering cross-functional collaboration, and continuously monitoring GEO performance. Ultimately, it argues that embracing GEO is vital for businesses to maintain visibility and authority in the evolving search landscape.

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A Labour Party conference in Wellington explored the initial stages of crafting AI policies and regulations in New Zealand, gathering diverse experts to discuss AI's economic impact, ethical implications, and workforce preparation. Conversely, a Talbot Mills survey report from April 2025 examined public and "AI thinker" attitudes towards artificial intelligence. The findings highlighted a significant divergence in optimism and concerns, with the general public more apprehensive about job losses and supportive of government regulation compared to the AI thinkers. This difference in perception suggests a need for better public education and reassurance regarding AI's benefits. The survey also assessed personal AI usage at work, expected impacts on jobs, and levels of concern regarding various AI-related issues. Ultimately, the two sources present both proactive policy exploration and an analysis of public sentiment surrounding artificial intelligence in New Zealand.

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Retrieval-Augmented Generation (RAG) is presented as a favourable method for integrating Large Language Models (LLMs) within businesses, enabling them to utilise proprietary data effectively. The article highlights the advantages of RAG for enterprises, such as enhanced accuracy, cost-efficiency, and data security when generating AI responses. It outlines typical business applications of RAG, including customer support, knowledge management, and decision support. Furthermore, the text addresses the challenges of integrating RAG with existing legacy systems and provides recommendations for successful implementation, emphasising modularity and continuous monitoring. Ultimately, the piece positions RAG as a key technology for data engineers to leverage the power of LLMs with their organisation's valuable information.

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In early 2018, the Cambridge Analytica scandal exposed the unauthorised collection of Facebook user data for political manipulation. A UK firm obtained information from millions via a personality quiz app, extending its reach to users' friends without consent. This data was reportedly used to create voter profiles and target them with specific political adverts, notably during the 2016 US election and the Brexit vote. The incident triggered widespread condemnation, raising serious concerns about Facebook's data security practices. Consequently, regulators and policymakers demanded increased transparency and more robust data protection laws. The affair ignited broader conversations regarding data ethics, the influence of social media, and the necessity for tighter regulation of online political advertising.

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This article from datapro.news explores the emerging software engineering trend of "vibe coding" in 2025, where AI language models assist developers by transforming natural language prompts into code. Originating in Silicon Valley, this approach allows developers to act more as "directors," potentially accelerating development and changing the roles of both junior and senior engineers. While it promises increased productivity and lower barriers to entry, concerns exist around skill development and code quality, necessitating robust oversight. The rise of vibe coding also has significant implications for data management and analytics teams, requiring adaptation in skills, workflows, and risk management.

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It's the most googled question about data management, right up there with he difference between data management and IT infrastructure - so today we take a bit of a deep dive into the topic to get an explanation on the difference.

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Datapro.news presents an article announcing Anthropic's Claude 3.7 Sonnet, a significant advancement for data engineers and developers. This new model boasts a hybrid reasoning system for rapid responses and in-depth analysis, notably improving coding, data analysis, and visualisation tasks. Users report considerable time savings in development, highlighting its large context window and low error rate. The article details Claude 3.7's superior performance in coding benchmarks, its innovative command-line tool 'Claude Code', and its impressive ability to generate interactive data visualisations directly from raw data. Ultimately, the piece argues that Claude 3.7 represents a revolutionary leap in AI-assisted development, accelerating workflows and democratising access to data insights.

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As we see advancements in technology, like AI systems, develop and Integrate into our day to day lives at a rapid pace, our legislators face the challenge of setting policy and laws around its use. It's a challenge Paul, our host, will be working on directly so he sat down to chat with Pragmaticians James Hartwright to learn about the challenges and opportunities for legislating new technology.

To learn more about our Melbourne Event, visit https://www.addevent.com/event/KF25098815

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This datapro.news article from March 2025 explores the landscape of real-time databases essential for modern digital applications. It categorises these systems into transactional (OLTP) and analytical (OLAP) types, highlighting the top five databases in each category along with their key features and ideal use cases. The piece provides a framework for data professionals to choose the right database based on their specific needs, from high-speed operational workflows to complex analytical workloads. Furthermore, it touches upon future trends such as the convergence of database functionalities, the growth of serverless options, and the integration of machine learning.

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We're back to basics this week with data Architecture 101 - a comprehensive overview of what data architecture is, how works, what principles guide it and why it's a cornerstone to modern data work.

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In this week's datapro.news, we examine Palantir Technologies' Artificial Intelligence Platform (AIP) and its application in mission-critical operations, particularly within defence. It highlights AIP's core Ontology, which unifies data, logic, and action for enhanced decision-making. The article further explains Palantir's emphasis on security and governance through multi-layered controls and its preference for human-AI collaboration. Finally, it outlines key technical innovations like Apollo for multi-cloud deployment and offers insights for data engineers building similar AI-driven systems.

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Layered quality types for data sets - that's the easiest way to sum up Medallion Architecture - but how does it work, when should it be deployed and is it right for you and the enterprises you're working with?

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Data engineering in 2025 is being shaped by cloud platforms, real-time processing, and AI/ML integration. The shift from ETL to ELT processes with tools like dbt and Apache Airflow are central themes alongside discussions about cost-effectiveness of cloud managed services such as AWS Glue and Snowflake. AI is automating schema inference and pipeline optimisation, while data quality and governance remain critical, especially with regulations like GDPR and CCPA. The evolving job market demands upskilling in AI/ML and cloud technologies, with online communities replacing Twitter/X as primary hubs for collaboration and knowledge sharing. These forces demand data engineers balance automation with oversight in an AI-driven landscape.

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The podcast discusses emerging data management trends in 2025, featuring insights from Julian Redmond, CEO of Ignition and Iris, following his global tour. Key topics include the growing adoption of Lakehouse architecture for its flexibility and efficiency, the rise of platforms like Databricks, Snowflake, and Microsoft Fabric, and the increasing role of AI in data integration and analytics. Automation is becoming essential for managing data at scale, with tools like Iris streamlining processes. Regulatory considerations, workforce shortages, and the evolving importance of professional data management across industries are also highlighted. The episode concludes with details on an upcoming AI and data-focused community meetup in Melbourne in April 2025.

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The article "2025's Hottest Modern Data Platforms" assesses the leading cloud data warehouse systems in terms of market share, growth, and key differentiators. It highlights Snowflake, Databricks, AWS Redshift, Google BigQuery, and Microsoft Fabric as the dominant players, and points out how each distinguishes itself. Each platform is assessed based on its strengths, optimal use cases, and cost factors. The shift towards modern data platforms is driven by scalability, flexibility, and cost efficiency, as traditional systems become obsolete. The article concludes by emphasising the importance of aligning platform choice with organisational needs and cautions against prioritising novelty over practicality.

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Learn all about Data Mesh in this week's Data Radio Show!

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In this week's Algorithm Report, we compile news updates from around the globe on the latest in Data and AI news.

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This article explores the capabilities of AI-powered deep research tools from OpenAI, Google, and Perplexity AI.

These platforms use advanced language models to automate complex, multi-step research processes. The analysis compares their technical specifications, performance benchmarks, and practical applications, highlighting their potential to transform various sectors like academia, market intelligence, and policy analysis. The article also examines the accuracy of these AI systems relative to traditional research methods, identifying limitations such as source accessibility and authority assessment.

Ultimately, it suggests that a hybrid approach, combining AI's pattern detection with human contextual accuracy, will yield the most effective research outcomes, augmenting human capability rather than replacing it.

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The video "Data Battlegrounds - The DOGE Invasion" explores the growing battle over data control, focusing on Elon Musk’s involvement in U.S. government operations through the Department of Government Efficiency (DOGE). The discussion highlights concerns about data privacy, AI bias, and the ethical responsibilities of data professionals. It examines how different AI models interpret world events with subtle biases, the risks of government overreach in data collection, and the lack of strong privacy laws in the U.S. The video also delves into the potential consequences of centralized data power, media accountability, and the importance of digital literacy in protecting individual rights. Ultimately, it calls for greater transparency, public awareness, and advocacy to ensure data is used ethically and democratically.

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The latest news updates from the world of data and AI, to keep you informed of how the industry is moving.

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A news article on datapro.news discusses the controversial access granted to Elon Musk's Department of Government Efficiency (DOGE) to US Treasury payment systems, raising significant privacy concerns and legal challenges. Nineteen states sued, alleging the action violated privacy laws and the Constitution, resulting in a federal judge issuing a restraining order. The article compares this incident to previous major data breaches, highlighting the unique combination of sensitive data, systemic risk, and political implications. Finally, an advertisement for dFakto, a data automation platform, promotes its services for improving data management, governance, and security.

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In this episode of The Data Radio Show, host Paul is joined by Richard Harris from Data Design Consulting to explore the principles of data privacy and why they serve as flexible guidelines rather than strict rules. They discuss global privacy regulations, the role of consent and transparency, and the challenges of AI-driven data management. Harris highlights the importance of accountability, data minimization, and security best practices, especially as organizations navigate emerging AI technologies and evolving regulations. The episode also introduces a free data privacy course, providing professionals with essential knowledge to stay compliant and ethically manage data in an AI-driven world.

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The latest news updates from the world of data and AI, to keep you informed of how the industry is moving.

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This 2025 guide explores Retrieval-Augmented Generation (RAG), a natural language processing technique that enhances AI by dynamically integrating external information into its responses. RAG overcomes limitations of traditional models by using a retrieval mechanism to access real-time data, improving accuracy and context awareness. The guide examines RAG's technical aspects, including retrieval and generation mechanisms, dense vector embeddings, and various applications across multiple fields such as healthcare and legal research. Ethical considerations, including bias mitigation and privacy, are also discussed, alongside future trends like multimodal RAG and self-improving systems. Finally, practical implementation strategies and tools are detailed.

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The podcast explores whether AI can generate complex and meaningful conversations, using tools like Google's Notebook LM to produce AI-driven discussions on topics like Data Vault. The host experiments with AI-generated content, including scripted advertisements and a deep-dive interview on data management. The discussion covers Data Vault's benefits, such as structured data storage, auditability, and its applications in industries like banking and supply chain management. Challenges like implementation, governance, and change management are also addressed, emphasizing collaboration between business and tech teams. The episode concludes by encouraging listeners to explore Data Vault further and consider how AI-driven solutions can enhance data strategies.

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The article discusses the release of DeepSeek R1, a Chinese open-source AI model, and its significant impact on the global tech market. R1's low development cost and comparable performance to leading US models caused a major sell-off in tech stocks, highlighting the model's cost disruption and the democratisation of AI. The article explores R1's architectural innovations, including Mixture of Experts and Reinforcement Learning, alongside its deployment challenges, such as the need for specialised skills and infrastructure. Ultimately, the piece positions R1 as a transformative technology, ushering in an era of "smarter, leaner, and open" AI, but one requiring significant adaptation from data engineers.

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This episode of The Data Radio Show explores the growing importance of data quality in AI and the rapid expansion of job opportunities in data-related fields. Sam Williams interviews data scientist Sheng Lan to discuss trends in data engineering, the role of AI in improving data workflows, and the increasing demand for AI and data professionals.

They also highlight how AI tools are transforming data management, reducing preprocessing time, and enhancing data quality for analytics and machine learning applications.

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This article from datapro.news discusses Google's Stream Realtime API, powered by Gemini 2.0, which enables real-time, multi-modal AI interaction for data engineers. The API allows AI to understand visual and audio inputs, providing real-time assistance with pipeline design, debugging, and skill development. This contextually aware system offers significant productivity gains, potentially reducing design time by 30%, debugging time by 40-50%, and accelerating skill acquisition by 25-35%. The article highlights several use cases and emphasises the API's potential to transform data engineering workflows and democratise expertise. Ultimately, it promotes embracing AI-augmented tools to enhance efficiency and innovation in the field.

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Importance of Data Management (00:01-00:30):
Effective data management is critical, especially for meeting diverse client needs. Poorly managed processes can lead to inefficiencies and risks.
Diverse Roles in Data Projects (02:14-03:22):
Data projects involve multiple roles such as project managers, architects, engineers, analysts, and BI specialists. Customized training is increasingly important to equip all roles with the necessary skills.
Agile and Incremental Approach (04:28-05:01):
Managers should focus on incremental data delivery and managing business expectations. A single-use case or report can be delivered quickly, while attempting to fulfill large-scale requests can delay results.
Resource Allocation Challenges (03:57-05:40):
Balancing technical, human, and financial resources is key. Without proper allocation, projects may fail to meet business expectations or maintain data quality and governance standards.
Risk and Compliance Management (06:17-06:49):
Managers need to emphasize governance, privacy, and risk management to avoid data breaches and comply with regulatory standards, especially in the AI era.
Cost-Effectiveness and ROI (07:21-08:39):
A business-driven model helps balance analytics costs and benefits. Managers should connect costs with tangible outcomes to justify investments.
Common Mistakes in Data Vault Projects (09:50-15:10):
Overly ambitious projects and failure to thin-slice tasks into manageable units are common pitfalls. Incremental delivery ensures faster value realization and avoids perception issues of high costs with no visible benefits.
Staff Turnover and Knowledge Transfer (17:24-19:51):
Staff turnover impacts project continuity. Standardized processes, automation, and tools like Iris help streamline training and mitigate turnover challenges.
Regulatory Compliance and Automation (25:02-27:28):
Consistency in deployment aids in meeting compliance requirements. Automation ensures uniformity and transparency across processes, reducing human error.
Scalability and Long-term Strategy (30:55-33:12):
Cloud-based platforms provide scalability, but changes in technology and workforce demographics require adaptable training and intuitive tools to maintain efficiency.
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#️⃣ How do you build robust, scalable enterprise Data Warehouses without code? DSharp has the answer. Join Kim Johnsson as we discuss the power of data modelling, low-code/no-code and the use of AI in building robust, scalable data systems.

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KEY TAKEAWAYS:

Why a Model-First Approach Makes Data Warehousing Easier
Kim emphasises that focusing on a business-friendly, UML-like model (instead of jumping straight to tables, hubs, links, or satellites) dramatically reduces complexity. By describing data concepts (e.g., “Person,” “Organisation”) rather than technical structures, teams work more closely with business requirements and accelerate design decisions.
Automation and Reusability for Faster Time-to-Value
A recurring pain point in BI and data warehouse projects is repetitive manual work. Kim explains how automation tools—like DSH Studio—eliminate repetitive coding (SQL joins, Data Vault table creation, etc.). This reusability approach cuts down project timelines, enabling teams to deliver insights more rapidly.
Simplifying Ongoing Maintenance and Change Management
Because the solution generates all of the underlying structures automatically, changes are handled at the model level. If the business logic changes (e.g., a new attribute or different key), teams update the model and regenerate the warehouse. This top-down approach reduces the technical burden of managing large numbers of tables and relationships over time.
Integrating AI and Language Models into Data Modelling**
Kim highlights how generative AI (like ChatGPT) can quickly create initial data models by interpreting written business definitions. Though still maturing, these AI-driven features can give teams a head start, automate routine modelling tasks, and spark more productive discussions with stakeholders.
Key Takeaways for Data Leaders and Practitioners:
🫵🏼 Focus on Business Concepts: Modelling at a conceptual level encourages alignment with real business needs.
🫵🏼 Leverage Automation: Tools that auto-generate Data Vault structures free teams from repetitive coding, boosting productivity.
🫵🏼 Foster Collaboration: A shared model (versus purely technical artifacts) lets both business and IT speak a common language.
🫵🏼 Adapt to AI: Early experiments with LLMs show promising ways to speed up data modeling and design.
🫵🏼 Trust Through Iteration: As Kim notes, once teams see the automation “just works,” they confidently embrace a fully model-driven approach.
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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

Over the next couple of weeks, we’re presenting these Notebook LM produced Podcast episodes on our most popular DataPro Newsletter topics.

This article from datapro.news offers five AI prompts to enhance data engineers' productivity, covering schema design, feature engineering, code generation, data quality assessment, and model deployment. It also suggests a 100-day self-learning plan for aspiring data engineers, focusing on foundational skills, practical projects, and tool mastery. The piece promotes a free AI masterclass for marketers and highlights the impact of AI on marketing ROI. Finally, it features an endorsement of a YouTube channel offering data engineering tutorials.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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This article from datapro.news explores the data engineering behind Waymo's self-driving vehicles, focusing on how they determine right-of-way at four-way stops. The author details the multi-sensor system (LiDAR, cameras, radar) and sensor fusion algorithms used for environmental perception.This perception data feeds into deep learning models (VectorNet, CNNs, RNNs) to predict the behaviour of other road users.Decision-making combines rule-based systems, reinforcement learning, and planning algorithms. Finally, the piece highlights Waymo's rigorous training, simulation (Carcraft), and continuous improvement processes, including data collection, model updates, and human oversight, to ensure safety and reliability.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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This article from datapro.news discusses how artificial intelligence (AI) is revolutionising data modelling. Six key applications of AI are highlighted: automating data mapping and transformation, enhancing predictive modelling, improving real-time data quality, optimising data structures through scenario simulation, enabling natural language querying, and utilising DSharp Studio's innovative low-code platform for streamlined data warehouse creation. The article showcases various AI-powered tools like Secoda, Databricks, Striim, WhereScape, and Azure OpenAI, illustrating their practical uses in enhancing data management efficiency and accuracy. The overall aim is to demonstrate how AI allows data engineers to build better data models faster, ultimately leading to improved business insights and decision-making.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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This article from datapro.news explores the enduring popularity of the Python programming language. It details Python's history, highlighting its readability and versatility, and examines the top ten most frequently searched Python-related questions on YouTube. These questions cover fundamental concepts like data structures, object-oriented programming, and memory management. The piece also promotes a YouTube channel offering Python tutorials and mentions the importance of PEP 8 style guidelines. Finally, it notes Python's extensive community support and corporate backing.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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This article from datapro.news discusses adapting data governance for Large Language Models (LLMs). It highlights the crucial role of data governance in ensuring accurate, trustworthy, and accessible data for LLM applications. The article addresses challenges like managing unstructured data, ensuring traceability, mitigating bias, and leveraging semantic layers. Furthermore, it underscores the need for AI-ready infrastructure and emphasises the practical experience gained from deploying one's own LLM, including improving data quality, understanding model capabilities, and developing AI literacy. Finally, the piece promotes a free AI masterclass.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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This article from datapro.news discusses common pitfalls in data modelling. It outlines three key data modelling stages—conceptual, logical, and physical—explaining their purpose and how errors at each stage, such as insufficient stakeholder involvement or unclear definitions, can negatively impact projects. The article highlights ten specific mistakes, detailing their consequences and offering advice on avoiding them. The author emphasises the importance of a thorough conceptual phase before progressing to more detailed stages and advocates for iterative design. Ultimately, the article stresses the importance of careful planning and communication for successful data modelling.

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2024 was a big year for Julien Redmond - Ignition, Australasia's premier Data Vault experts saw the results of long term societal shifts in thinking around the importance of data management, while IRiS launched it's latest version of an automated Vault Builder.

So what lessons did Jelien take from last year and how will they be used in 2025?

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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This article from datapro.news discusses the transformative impact of advanced AI assistants on data professionals. It highlights three key advancements: near-infinite context windows enabling comprehensive data analysis, text-to-action capabilities streamlining data management, and recursive self-improvement leading to continuous enhancement. The article envisions a future where AI polymath assistants, like a hypothetical "LEO," augment human capabilities by handling complex tasks, leaving data professionals to focus on strategic thinking and innovation. This shift is predicted to significantly increase productivity and reshape the role of data professionals in 2025 and beyond. The overall tone is optimistic, suggesting AI will enhance, not replace, human expertise.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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The article discusses the European Union's Artificial Intelligence Act (EU AI Act), a new regulatory framework governing AI systems within the EU and impacting businesses operating there. The Act categorises AI systems by risk level – unacceptable, high, limited, and minimal – with varying regulatory requirements and deadlines for compliance. High-risk systems, including those used in critical infrastructure and profiling, face stringent rules, while unacceptable-risk systems are banned. General Purpose AI (GPAI) models, such as ChatGPT, are also addressed, highlighting the need for transparency and risk mitigation. Finally, the importance of data literacy is emphasised, particularly in navigating the ethical implications of AI and avoiding biased outcomes.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

Over the next couple of weeks, we’re presenting these Notebook LM produced Podcast episodes on our most popular DataPro Newsletter topics.

This article from datapro.news forecasts three major technological trends shaping data engineering in 2025. Firstly, the rise of agentic AI necessitates skill adaptation in data pipeline automation, ethical considerations, and new roles like AI pipeline architects. Secondly, new computing frontiers, including post-quantum cryptography and ambient invisible intelligence, demand expertise in quantum-safe architectures and edge computing. Finally, human-machine synergy, encompassing spatial computing and polyfunctional robots, requires proficiency in 3D data modelling and robotic process automation. The article stresses the importance of continuous learning, interdisciplinary skills, and ethical considerations for data professionals to succeed in this evolving landscape.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

Over the next couple of weeks, we’re presenting these Notebook LM produced Podcast episodes on our most popular DataPro Newsletter topics.

The article discusses the transformative impact of AI agents on data engineering in 2025. It highlights predictions from Gartner and industry leaders about the increasing prevalence of autonomous AI agents in enterprise software, focusing on their ability to automate tasks, enhance decision-making, and improve operational efficiency. The text details key characteristics of these agents, such as autonomy and continuous learning, and explores their applications in various aspects of data management, from automation to advanced analytics. Finally, it offers advice for data professionals on preparing for this technological shift, emphasising the importance of skill development, data infrastructure upgrades, and ethical considerations.

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As we start to gear up for 2025, we thought now was a great time to revisit some of our most popular content from 2024 in a new light.

Over the next couple of weeks, we’re presenting these Notebook LM produced Podcast episodes on our most popular DataPro Newsletter topics.

This article from datapro.news discusses the revolutionary impact of significantly larger context windows in AI language models. The increased capacity, allowing processing of millions of tokens instead of thousands, enables AI agents to understand and reason with far more information simultaneously. This leads to advancements in document analysis, long-term memory, and simpler RAG architectures. However, the article also highlights associated challenges, such as data quality, governance, and sustainable computing. Experts predict this technological leap will profoundly transform AI applications in 2025.

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It's our final Data Radio Show for 2024 - so what have we learned and what's still to come in 2025?

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This week we catch up with our first guest of 2024 - CEO of Scalefree, Christof Wenzeritt, to see if 2024 delivered on the potential it had back at the start of the year, the lessons learned from the year and what to expect from 2025.

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Agentic Ai has been called the next innovation in Artificial Intelligence based decision making - which comes with a lot of pre-conceived notions on what that actually means and looks like in practice. So we sat down to learn more about Agentic AI with data scientist Sheng Lan!

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dFakto have 25 years experience building software that empowers organisations to bridge the gap between data, decision and strategy - because they see inefficiency as the silent business performance killer. In the latest data radio Show, we sit don and chat to Thibault and Marc to learn more about what they do, how they do it and what drives them to show people the importance of data quality.

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From the automotive world to the data world, Coalesce's Damian Walker has been involved in automation in different ways which bring an unique view to the world of data and a data driven future, in every sense of the term.

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They say the future is unwritten, but we can see its shape by looking at current trends around data! In this weeks episode of the Data radio Show, Data scientists Sheng Lang gives us a glimpse at trends shaping the future of data management.

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Joined this week by Wherescape's Patrick O'Halloran, we're exploring Medallion architecture, how it works and the benefits you can see in the outcomes

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Once the stuff of science fiction, @Waymo has now got autonomous vehicle (AVs) on the streets in some areas of the USA - which is a bit mind blowing for someone like our host Paul and this week's Guest Sam Williams from little old New Zealand.

So how was the experience? And what does this tell us about the amount of data required to make such a thing possible?

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Join us for a deep dive into an issue impacting data engineers all over the world as the pace of technological change increases with the advent of AI - what does it mean for data privacy!

In this Masterclass, hosted by Julien Redmond from Ignition, we hear from ADAPT Group's Sam Redmond and Data Design Consultancy's Richard Harris to discuss the nuance of of how AI needs access to Personal Inform

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Any human with interactions to media use that media to help interpret the world around us, but media also has a bias built in to it. So can an LLM pick up on media bias and are there ways to combat to it?

You can find the original media analysis piece mentioned in this Episode here: https://paulbarlow.substack.com/p/media-analysis-in-the-time-of-ai

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When it comes to AI, we hear a lot of buzzwords and technical terms and for those new to the industry, that can be a bit overwhelming. So this week we sit down with Ole Bause to discuss the basics of AI and how that works, and how it works in building tools, like the new Data Vault automation Flow.BI

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How do you measure the success of your AI? Is it accuracy, latency, consumer buy in? And how do you up skill in a field growing exponentially?

That's what we look at this week in the Data radio Show as Sam Williams talks to Data Scientist, Sheng Lan who has been building a resource to. help data engineers up skill and stay on the top of their game.

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Automation is an intrinsic tool in any large scale data architecture - but AI is a tool that can help build better, faster architecture types. In this week's episode, Sam talks to Jonas De Keuster from Vaultspeed about the way AI is helping evolve the field of data Automation.

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There's a scene in Jurassic Park when the Palaeontologists see the dinosaurs and worry that they're out of a job.

As a producer of podcasts, Notebook LM may be my dinosaur theme park.

This week, our show includes a Notebook LM conversation around EU AI Laws which is fascinating to hear...

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In 2017 Equifax, the third largest consumer credit agency in the world fell victim to one of the largest data breaches ever. What did they learn from the process and what did the public learn from the experience? In this week's episode, Ignition's Julien Redmond talks to Equifax Australia's Chief Data Officer Bob Sparshatt to learn more.

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They're often seen as the two most cumbersome elements of building any kind of data management system - the time it takes and the way that length of time disengages stakeholders. Well we may have found a new tool which can help with both.

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On the surface building a board game to help kids learn maths may not seem to have a lot in common with building data architecture but Datavault Builder's CEO Petr Beles found otherwise while building their new game, Vault-E Wins.

And for a chance to win a copy of Vault-E Wins, register at https://datavault-builder.com/vw/

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Who better to talk about the best ways to stand out when it comes to applying for a job as a data professional than a tech company CEO!

This week, we talk to Christof Wenzeritt, CEO of Scalefree about the employment process, what he looks for when needing to find new team members, freelancers and support teams - and how you can stand out in the process.

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Adaptation, change and risk taking - the three keys to building a sustainable long term career as a data professional according to Pragmaticians Managing Director, James Hartwright. In this week’s episode we talk about his career path from his mother circling jobs in the classifieds through to working with multiple large scale organisations across Australia in their data transformation processes, and look at ways people starting out in the field can make waves on their path.

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Wherescape’s Patrick O’Halloran is fascinating in the best way! A Texan with the most Irish name ever and a fashion sense from Hawaii, Patrick has had decades of experience in data warehousing and automation, seen the industry grow and evolve and can see first hand how changes to automation, like the advancement of AI, are likely to shape the industry going forward!

This episode is available in video on YouTube at https://youtu.be/oV5zVh7FEqI

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Officeworks is one of Australia’s most well known brands, helping Australians to work, study and create for over 25 years. With a range of over 40'000 products, they are navigating a world full of digital transformation. At the forefront of this navigation is Selina Li, Officeworks Tech Data Lead.

In this week’s episode we talk about her journey from finance in Hong Kong to office supplies in Australia, the blurring of lines between data roles in the 21st century and much much more.

This episode is available in video on YouTube at https://youtu.be/UDqvaZ9ASew

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Digital transformation is a common term these days, as organisations and executives find ways to upgrade and modernise in an ever changing world of digital technology which is evolving at an exponential rate. Today’s guest, Max Theseira, has been working to help people navigate these changes for, well longer than he wants to confess at what he describes as the intersection between people, technology and change.

You can connect with Max on LinkedIn here: https://www.linkedin.com/in/maxtheseira/

This episode is available in video on YouTube at https://youtu.be/Q6J9Lb-1LH8

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Jonas De Keuster is Vaultspeed’s Chief Marketing Officer, a role he likens to trying to be the loudest voice in a busy train station. But before he started working to promote the Belgium based Automation company, he was a modeller and data expert - which offers him a unique insight into the field, and a fascinating career projection.

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

This episode is available in video on YouTube at https://youtu.be/h37AvolCCs4

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We hear a lot about how AI can unlock efficiency - but just how does it do that?

In this week’s Data radio Show, Tech Data Chief Scientist Cheng Lan gives us an example of increasing efficiency using Carbon monitoring as an example.

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

This episode is available in video on YouTube at

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Generative AI is seen by many as a fancy looking automation process with the ability to adapt - so this week we wanted to explore how Gen AI will help us beyond process automation with a bit of an experiment - using it to build an internal HR Assistant!

View this episode on YouTube at: https://youtu.be/rqcZSyop6jY

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Generative AI LLMs are a bit like having your own army of interns - well that’s according to tech data lead scientist Shang Lan - who sits down this week with Sam Williams to discuss how prompt training in LLMs has the potential to change the way we research and develop our own content, like blog posts, film scripts and academic papers.

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

This episode is available in video on YouTube at https://youtu.be/aNYY_92Nw1A

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To paraphrase the late Sir Terry Pratchett, they’re called revolutions because they always come around again - and even though we’re in the advent of a digital revolution, it’s not the first time we’ve seen massive impacts to the way we work, see the world and connect with each other. And there’s a scale of that impact - so where does Generative AI of today stack up on that scale?

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

This episode is available in video on YouTube at https://youtu.be/zlcRpU0Ml-0

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A NYU Study reveals the way AI is going to change our future - with some roles being better suited than others for it and hyper-personalisation on the horizon for us in some unexpected areas. T learn more about this, I chat with Sam Williams, futurist and AI Aficionado to see where these will take us and what sort of roles people wanting to be a part of the change should look into.

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

Find Sam on Linkedin Here: https://www.linkedin.com/in/samuelmwilliams/

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A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

We’re taking inspiration this week from Silicon Valley - The show, set in the place, not the place itself. Well sort of about the place itself as well.

Look, it may be easier just to watch and let us explain things in detail!

This episode is available in video on YouTube at https://youtu.be/FF_iZyk-2Fo

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A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

We’ve been so lucky to have chatted to so many experts in data management over the past few months, and as we hit the halfway mark in 2024, I thought it would be awesome to share with everyone the best bits of career advice for data professionals from across those interviews!

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Do you know what they call the CCPA in France? Well, that call it the CCPA because it’s an American Law - but the French have GDPR, China has PIPL and Australia has the Privacy Act - around the world, we’re seeing shifts in legislation around data governance - and after being asked about it in New Zealand this week, Paul decided to give us a bit of a world tour and a high level explanation of various local Data Governance Laws to introduce them to those who might need them.

04:37:55 - GDPR

06:08:23 - ePrivacy Directive

06:39:14 - DPAs

07:11:54 - British Laws

07:35:46 - HIPPA

08:38:09 - GLBA

09:18:56 - COPPA

09:44:24 - FTA

10:10:01 - CCPA

11:06:50 - Australia

11:33:55 - The Privacy Act

12:01:51 - NFD

12:31:23 - MyHealthcare

12:54:20 - The Telecommunications Act

13:33:48 - State Laws

14:11:31 - GDPR Australia

14:44:36 - Notable Others

You can find the video version of this at https://youtu.be/GroyDoUDyTY

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

Technology experts are often fast adopters of new tools, paving the way for those who follow - so today I wanted to learn about two new Data Architecture Tools - FlowBI and IRiS V2.0. Both have been built with Data Vault in mind as their primary architecture framework but both perform very different functions that work with a variety of automation tools.

So I chatted with Ignition's Julien Redmond to learn more!

____________________________________________________________________

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Check out the latest Data news, interviews and more across platforms.

🍏 Podcasts - https://podcasts.apple.com/us/podcast/datadialogues/id1526044462?uo=4

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A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

In this week’s episode we’re travelling through time and space! We released a Data Vault Masterclass this week based on questions from an October 2022 set of questions to see what has changed with the advent of AI and LLMs reshaping the world of data management - in today’s episode, Paul compares the answers side by side to see the growth and change that’s occurred since then.

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In this month’s DVIC Masterclass, Data Radio Show Host Paul Barlow sits down with Julien Redmond from Ignition and James Hartwright from Pragmaticians to look back on a Masterclass done in October 2022 to see if machine learning, Generative AI and language model advances have changed approaches to working in Data Vault. You can find the original episode here on YouTube: https://youtu.be/E10w43Eizhg Visit DVIC at DVIC.accelerate.world

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

In this week’s episode we catch up with all the news that’s fit to vlog, and I sit down for a lesson on AI Prompt training with Sheng Lan, the Chief Data Scientist at Tech Data to go over his findings on prompts vs outcomes of language models of various sizes.

This episode is available in video on YouTube at https://youtu.be/fL4bYMAF2Qg

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News References:

Story One - https://www.simplilearn.com/the-future-of-data-science-article

Story Two - https://www.databricks.com/blog/introducing-databricks-ai-security-framework-dasf

Story Three - https://arxiv.org/pdf/2404.03622

This episode proudly bought to you by Wherescape. Learn more about Wherescape at www.wherescape.com

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology and see how the industry is changing its shape in the 21st century.

In this week’s episode, the EU gets into AI, our one stop content shop, and a look at the World Wide Data vault Conference in Stowe, Vermont, with BI Consultant Tim Kirshke, and a look back on last years WWDVC with Data Vault inventor Dan Lisntedt.

This episode is available in video on YouTube at https://youtu.be/pEe1ukxfZgs

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News References:

Story One - https://www.youtube.com/watch?v=8XCtIXQLXYg

Story Two - www.jdsupra.com/legalnews/the-eu-ai-act-a-comprehensive-guide-for-9732122/

Story Three - https://www.datanami.com/this-just-in/salesforce-unveils-zero-copy-partner-network-offering-new-open-data-lake-access-via-apache-iceberg/

This episode proudly bought to you by Wherescape. Learn more about Wherescape at www.wherescape.com

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week’s episode, we get some hints and tips on how to talk to high level executives about the need for better data governance and plans, I catch up with managing partner of Pragmaticians, James Hartwright and we go like Llamas through the latest data news.

This episode is available in video on YouTube at https://youtu.be/2Gnsl8kJvV4

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News References:

Story One - https://www.youtube.com/watch?v=8Ul_0jddTU4

Story Two - https://www.jdsupra.com/legalnews/ai-regulatory-recap-q1-2024-updates-and-9756534/

Story Three - https://accelerationeconomy.com/ai/databricks-dbrx-llm-sets-the-bar-for-open-models/

This episode proudly bought to you by Wherescape. Learn more about Wherescape at www.wherescape.com

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week’s episode, we investigate the potential impacts of poor data quality on your business, I chat with travel enthusiast Lorenz Kindling and discover the one place he’s always wanted to visit, and we take a look at news from around the world, including Stanford’s HAI report on AI.

This episode is available in video on YouTube at https://youtu.be/goy6pNi6nVA

Subscribe today to get access to full episodes

Join the Data Vault Innovators Community today at http://dvic.accelerate.world News References:

Story One -Dr Andrew Ng on Agentic workflows: https://www.youtube.com/watch?v=sal78ACtGTc

Story Two - US Data Privacy regulations: https://wsvn.com/news/politics/us-lawmakers-unveil-a-plan-to-give-all-americans-a-right-to-online-privacy/

Story Three - The Stanford HAI Report: https://aiindex.stanford.edu/report/

This episode proudly bought to you by Wherescape. Learn more about Wherescape at www.wherescape.com

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week’s episode, we look at what happens if you don’t have someone in your organisation who is an expert on data, the risks involved and the ways to remedy it. I chat to Data Security consultant Richard Harris about the advice he’d give his younger self and how he’s trained his algorithms to avoid sports, and we look at the latest news in the field.

This episode is available in video on YouTube at https://youtu.be/WyPJcV-oavE

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News References:

Story One - ReaLM with Apple: https://arxiv.org/pdf/2403.09611.pdf

Story Two - Meet Devin from Cognition Labs: https://www.cognition-labs.com/introducing-devin

Story Three - Ezra Klein and Niall Patel on AI’s future in saving the internet: https://www.nytimes.com/2024/04/05/opinion/ezra-klein-podcast-nilay-patel.html

**This episode proudly bought to you by Wherescape.

Learn more about Wherescape at www.wherescape.com**

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week's episode of the Data Radio Show, I catch up with Tech Data's Chief Data Scientist Sheng Lan, we look at who is in charge of data quality and a look at the news!

This episode is available in video on YouTube at https://youtu.be/aXxRiEG_5hk

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Join the Data Vault Innovators Community today at http://dvic.accelerate.world

To learn more about Purview visit: https://windowsreport.com/microsoft-purview-brings-modern-data-governance-to-your-doorstep/

To learn more about Analytic Insights visit: https://www.analyticsinsight.net/top-ways-data-engineers-can-leverage-generative-ai/

To read more about the AI Governance Market Growth check out: https://www.einnews.com/pr_news/699671273/ai-governance-market-to-grow-at-a-staggering-cagr-of-35-7-over-2024-2031-driven-by-regulatory-requirements

To see the full piece on AI by John Stewart of the Daily Show, visit: https://www.youtube.com/watch?v=20TAkcy3aBY

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

In this month's Data Vault Masterclass, recorded in front of a live audience, Host Julien Redmond is joined by Alex Lei and Vincent McBurney to talk Data Management Platforms and their connections to AI, and how they can all work together!

Podcast of this episode is also available where ever you get your podcasts!

And don't forget to join the Data Vault Innovators Community to join the conversation at http://dvic.accelerate.world

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week’s Data Radio Show, we look at the news out of the NVIDIA conference in San Jose, look back at the DVIC London Summit and check out the recent AI Events across Australia!

With special guests, Scalefree CEO Christof Wenzeritt and Ignition CEO Julien Redmond.

This episode is available in video on YouTube at https://youtu.be/PEgqRd6tXdA

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Join the Data Vault Innovators Community today at http://dvic.accelerate.world

For the link about Oracle’s use of AI in Formula One: https://www.cdw.com/content/cdw/en/articles/dataanalytics/how-technology-helps-oracle-red-bull-racing-dominate-formula-one.html

Story Three: https://nvidianews.nvidia.com/news/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing

For more on the Snowflake and Nvidia team up: https://www.businesswire.com/news/home/20240318504019/en/Snowflake-Teams-with-NVIDIA-to-Deliver-Full-Stack-AI-Platform-for-Customers-to-Transform-Their-Industries

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week's Data Radio Show, I catch up with Wherescape's Data Architect Endika Pascual, and look back at why he feels Data Vault is still as relevant today as the day it was created. We take a look at the news and introduce you to Figure 01!

This episode is available in video on YouTube at https://youtu.be/cAyk_DKbFkA

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Join the Data Vault Innovators Community today at http://dvic.accelerate.world

News Article References:
Story one: https://finance.yahoo.com/news/bigid-pioneers-breakthrough-patent-technology-131000924.html
Story Two: https://www.unite.ai/will-the-eus-ai-act-set-the-global-standard-for-ai-governance/
Story Three: https://youtu.be/Sq1QZB5baNw?si=WVPd1sSRPPefHACb

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week's episode of the Data Radio Show, I catch up with Principal Consultant for Ignition, Bronwen Fairbairn, we take a look back at the traps to avoid when setting up new data management practices and the latest news from around the web about AI and data management.

All that and the upcoming Masterclass live recording, this week in the Data Radio Show!

This episode is available in video on YouTube at https://youtu.be/MujQwGuOaI8

Subscribe today to get access to full episodes

Join the Data Vault Innovators Community today at http://dvic.accelerate.world

News item 1: https://www.gartner.com/reviews/market/augmented-data-quality-solutions

News item 2: https://www2.deloitte.com/us/en/pages/about-deloitte/articles/technology-trust-ethics-annual-report.html

News item 3: https://www.datanami.com/2024/03/07/cloudera-unveils-next-phase-of-open-data-lakehouse-to-unlock-enterprise-ai/

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week's Data radio Show, we look at the problem with woke AI technology, how to build data architecture for small businesses and I catch up with Chief Operating Officer and adrenaline junkie, Mike Kalousek from InfoVia!

This episode is available in video on YouTube at https://youtu.be/-DsffyBkQ9Y

Subscribe today to get access to full episodes

Join the Data Vault Innovators Community today at http://dvic.accelerate.world

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week's episode of the Data Radio Show, we look at resilience!

We look back at our December Masterclass with special guest Dan Linstedt as he, Michael Olschimke, Nols Ebersohn and Julien Redmond talk about building resilient systems, meet Jonas de Keuster from Vaultspeed and check out the latest data management news along the way.

This episode is available in video on YouTube at https://youtu.be/Oj7OuWRuYaY

Subscribe today to get access to full episodes

Join the Data Vault Innovators Community today at http://dvic.accelerate.world

CNN Link to GROQ Display: https://youtu.be/pRUddK6sxDg?si=TqU0h8AFRLSHp3vL

Learn about Open Code Interpreter here: https://arxiv.org/pdf/2402.14658.pdf

White Paper on rethinking AI Data management: https://hai.stanford.edu/white-paper-rethinking-privacy-ai-era-policy-provocations-data-centric-world

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

Welcome to the first DVIC Masterclass for 2024!

In this episode, join Christof Wenzeritt, CEO of Scalefree as he grills Automation experts Trung Ta and Wherescape’s Endika Pascual on all things data vault and automation!

And don’t forget to join the conversation on the Data Vault Innovators Community forums at http://dvic.accelerate.world

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week's episode I catch up with Petr Beles from Data Vault Builder to learn more about him, the board game he's working on and how he unwinds.

I'll take a look at the latest news, including ChatGPT's new memory function, US Patent Office rulings and Gemini 1.5 from Google.

We'll take a look back at the risks of AI in Data Vault automation.

And I'll explain the difference between Sora and Zora...

This episode is available in video on YouTube at https://youtu.be/uJFEmLt44ZM

Subscribe today to get access to full episodes

Join the Data Vault Innovators Community today at http://dvic.accelerate.world

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

In this week's Episode, Paul takes a look at Oracle's new AI, a new Data Privacy Maturity Model from One Trust, and why Sam Altman, founder of ChatGPT is looking for $7 Trillion dollars. I'll be meeting Marc Winkelmann a Data vault trainer to learn about how he broke into the industry and taking a look back at a section of our October 2023 Masterclass on why it's so important to have experts on hand for data transormation.

This episode is available in video on YouTube at https://youtu.be/_P1_tuhCDRc

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Join the Data Vault Innovators Community today at http://dvic.accelerate.world

00:00:00 - Opening
00:43:59 - Introduction
01:41:47 - This week's News
08:17:07 - Meet Marc Winkelmann
25:48:54 - Do you need an expert for Data Transformation
32:22:08 - Conclusion

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

**Order and chaos, it seems the two are this week's themes of the Data Radio Show!

In this episode I take a quick look at Google's new creative sector AI, sit down for a chat with Ignition CEO Julien Redmond and look back at a piece on Data Remediation vs Data Quality from 2022.**

This episode is also available in video on YouTube at https://youtu.be/9i0CykYd3mQ

Subscribe today to get access to full episodes

Join the Data Vault Innovators Community today at http://dvic.accelerate.world

00:27:29 - Introduction
01:42:00 - News
06:38:36 - Julien Redmond
25:03:40 - Data Remediation vs Data Quality

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

A weekly Podcast looking behind the curtain at the news and people working in the field of Data - whether thats engineers, architects, scientists, security, AI and more, to meet the people behind the way we shape the future with data and technology.

Join me, DVIC Community Co-Ordinator Paul Barlow, as I recap industry news from around the world, meet some of the people behind the data management industry and go exploring through the archives to showcase some classic data management related content we've worked on over the years for the Data Vault Innovators Community.
This episode is available in video on YouTube at https://youtu.be/Egj4KP4-qwA along with others in the series.

Subscribe today to get access to full episodes

Join the Data Vault Innovators Community today at http://dvic.accelerate.world

00:22:27 - Opening Credits
00:40:07 - Industry News
07:30:08 - Interview with Christof Wenzeritt
30:43:30 - Data Quality vs Data Governance
35:25:24 - Closing the show

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

In our final DVIC Data Vault Masterclass for 2023, Julien is once again joined by Scalefree’s Michael Olschike and Ignition’s Nils Ebersohn, as well as special guest Dan Linstedt.

As the inventor of Data Vault and founder of the DVA (Data Vault Alliance) Dan is uniquely situated to help answer user submitted questions, looking at everything from the proliferation of AI, to Blockchain to how you’d go about building a Data Vault for a certain fat man in red who is particularly busy this month.

This episode is also available as a video on our YouTube channel at https://youtu.be/yNa3Z8B55Nc

And don’t forget to check out DVIC to get involved in the conversation and access to world class experts on Data Vault and information management.

https://dvic.accelerate.world/

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

Is Data Vault right for small enterprises as well as large ones? What is sharding? The future of AI and Data Vault and more are all covered in this month's exclusive DVIC Data Vault Masterclass with Julien Redmond, Nols Ebersohn and Michael Olschimke, and learn how you can ask Data Vault creator Dan Linstedt a question for our December episode!

To learn more about or join the Data Vault Innovator's Community, visit: https://dvic.accelerate.world/

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

In this episode of DVIC Data Dialogues, host Julien Redmond is joined by a familiar face - Richard Harris who has recently branched out to create his own consultancy around Data Privacy in an ever evolving field at every level from small businesses through to governmental changes. He's now helping businesses and organisations to understand these changes and how to tackle them head first in the evolving privacy landscape.

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

Join Ignition CEO and Data Dialogues host Julien Redmond as he is joined once again by Data Vault Industry experts, Michael Olschimke, CEO of Scalefree, and Nols Ebersohn, Lead Architect for Ignition Data.

In this episode, the trio talk about the need for DV Experts, expanding requirements, growing datasets and much much more!

You can join the conversation as well by joining the Data Vault Innovators Community right here - https://dvic.accelerate.world/

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

Join Scalefree's Michael Olschimke, and Ignition's Nols Ebersohn as they tackle your questions in this months Data Vault Masterclass, hosted by Data Dialogues' Julien Redmond!

You can join the conversation as well by joining the Data Vault Innovators Community right here - https://dvic.accelerate.world/

Join the Data Vault Innovators Community for free and join the conversations, visit here: https://dvic.accelerate.world/

The audio for this months Data Vault Innovator's Community Q&A Session with Data Dialogues host Julien Redmond, Scalefree CEO Michael Olschimke and Ignition's Principal Architect, Nols Ebersohn.

Together they tackle a range of questions for Data Vault users and the DV Curious.

If you're keen to join the conversation on the Data Vault Innovator's Community, then visit https://www.accelerate.world/dvinnovatorscommunity to learn more

How is your organisation using Data? What are you collecting? When is the right time to assess your data needs and usage?

In this week's DVIC Data Dialogues Richard Harris catches up with Ignition's Practice Manager Steve Rose to discuss these considerations and the easy pre-packaged path to finding more data on your data!

In this week's episode of Data Dialogues Ignition's Richard Harris talks with Data Vault architect and lead DV Reviewer Nols Ebersohn discuss the need for reviews into techniques, structures and platforms to help review your established Data Vault or how Data Vault can help your organisation.

Join the conversation for free on DVIC by downloading the platform from your App Store today

Join Producer Paul, and Ignitions Julien Redmond and Nols Ebersohn as they discuss the recent WWDVC 2023 event, what they got out of it, what it means for Data Vault Practitioners and the DVIC Community.

Come join the conversation on the DVIC Platform by downloading it free from your App Store today!

In this new episode of Data Dialogues Richard Harris from Ignition and James Hartwright from Cognition look over how we define personal and sensitive data, our responsibilities as Data managers and how this can all apply to Data Vault work.

Join the conversation at DVIC by downloading for free the DVIC platform from your App Store today.

In a special episode of Data Dialogues, Julien Redmond from Ignition sits down with new Data Vault Innovators Community partners Christoff and Michael from Scalefree.

Scalefree is focused on offering companies, from a variety of industries, practical yet innovative solutions towards leveraging Big Data within modern business.
Built upon the tenets set out by Data Vault’s inventor, Dan Linstedt, Scalefree also provides clients with in-depth training to maximize the success of implementing Data Vault 2.0 within their business.

You can learn more about Scalefree at https://www.scalefree.com/

And don't forget to join the DVIC community by downloading the platform from your App store for free.

Recently at WWDVC, Data Dialogues host Julien Redmond sat down and had an exclusive catch up with the creator of Data Vault - Dan Linstedt!

They talk about what had been announced at the event, what they were excited about seeing develop and much more!

If you want to join the conversation about this with other data Vault experts, download the DVIC App from you app store today and come join the conversation!

Recently Data Dialogues host Julien Redmond ventured to snow capped Stowe in Vermont for the annual World Wide Data Vault Consortium, WWDVC, where he not only presented but also had a chance to sit down and do some quick interviews with some of the guests!

Here's the first few interviews in Podcast form, with more out next week. Make sure you're subscribed for more great DV content!

You can join the Data Vault Innovators Community with the simple download of an app! Just do a search for DVIC in your app store to join us for free!

Julien is joined by new DVIC Sponsor and CEO of Orion Governance, Ramesh Shurma to discuss how Data Vault and Orion's philosophy of incremental changes to Data Governance, using their Enterprise Information Intelligence Graph (EIIG) technology, are great tools to help Data experts achieve the best outcomes.

Download the DVIC App today from your App store and come join the conversation!

With the World Wide Data Vault Consortium (WWDVC) on the horizon, our Community Manager Paul Barlow wanted to know more - so he sat down with first time attendee (and our usual host) Julian Redmond and past presenter Nohls Ebersohn to learn more about what the purpose of WWDVC is, what guests can expect to learn, who they can hear from and what the guidelines are for crazy shirt day!

Download the DVIC App from your App store today to join the community and find DVIC here, on Linkedin and YouTube.

Our Head of Community Claire Vale sits down with Bronwen Fairbairn and Ashleigh Choice in this episode of Data Dialogues, to discuss the ever-changing role of women in the tech space.

Together they look at helping people get over the fear of not being skilled enough to look for rewarding roles, the importance of sharing your voice in the tec space, and why it's important to ask questions when you don't have all the answers.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

This week Julien Redmond is joined by Steve Rose and Richard Harris to talk about the HiPPO in the room - the Highest Paid Person's Opinion and how to navigate with them to find the best ways manage your data, the big picture thinking needed to achieve your data goals and the importance of being able to identify the real risks to your organisations data.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

In this episode, Julien is joined by James Hartwright and Richard Harris to discuss the rise of ChatGPT and other ChatAI - looking at how it's already in play as well as how it can help shape the way Data is managed, the need for humans as a safety back up option and creative ways people are already using the tool.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Julien Redmond is joined by Clean Data's Mark Budzinski to recap Mark's World Wide Data Vault Conference Presentation on the history of good ideas - and what leads some good ideas to find a footing - like the invention of the hamburger; while others don't stand the test of time, like Betamax Video.

And how does this all tie into Data Vault? It seems the sign of a good idea is sometimes not realising how good it is until you don't even notice it working anymore...

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Julien Redmond is joined by Nols Ebersohn and Bronwen Fairbairn.

Nols and Bronwen both presented at the recent WWDVC (World Wide Data Vault Consortium) conference and will be sharing what they learned and experienced at the event. For more insights from these Data Vault leaders, connect with them via the #DVInnovators Community App.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Over the last 6 months we’ve been working with the Data Vault Alliance to develop a community and partner ecosystem program that reflects the interests of DV professionals. Developed to support practitioners and help you evangelise the Data Vault 2.0 approach to information management.

Nols Ebersohn, Vincent McBurney and Julien Redmond lead this 60 min briefing which launched the DV Innovators Community and covered:

The latest lectures, talks and resources that you can access via a new community portal app

2.The planned community events for 2022

And a Q&A session with Nols and Vincent

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Julien Redmond, James Hartwright and Nols Ebersohn answer your questions in the August Q&A - the first live streamed event for DVIC.

They discuss a range of topics like the skill sets and team sizes needed to accelerate outcome when setting up Data Vault Projects, how to drive them to succeed and how to get qualified in Data Vault.
Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Join Julien Redmond, James Hartwright and Nols Ebersohn as they explore the paths that led each of them to become interested in Data Management and Data Vault in Particular and how their paths have shaped their outlook on the uses Data Vault has when put into practical situations.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Julien Redmond is back with James Hartwright and Richard Harris along with special guest Brent Banning to talk about how the Digital World is being reshaped with the loss of Cookies that have become staples to help advertisers and data collectors .

So what does this mean for marketing experts, data vault aficionados, local IT admins and end users?

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

USING IRIS TO ARCHITECT DATA VAULTS

John Chow - Practice Lead at Certus Insight and James Hartwright - Managing Partner at Cognition join Julien Redmond - CEO of IRiS, to discuss how to effectively automate DV2 deployments without sacrificing quality.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

THE FUNDAMENTALS OF USING DATA VAULT EXPRESS WITH WHERESCAPE

A discussion with Shree Nair - Enterprise Sales Leader at Qubole & WhereScape and Shiv Arora VP of Global Sales - Data & Cloud Solutions at IDERA Software, hosted by Julien Redmond on speed and cost benefits of Wherescape's Data Vault Express product.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

BUILDING COMPELLING BUSINESS CASES FOR DV2 IMPLEMENTATIONS

Richard Harris - Account Director, Certus Insight and Julien Redmond - CEO IRiS and Certus Insight discuss how to build a business case for a Data Vault 2.0 project in your enterprise.
Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

AUTOMATING DV - KEY CONSIDERATIONS

Vincent McBurney - Information Architect & Data Modeller at MLC Life Insurance discusses the ins and outs of automating Data Vault 2.0 implementations with Julien Redmond - CEO IRiS and Certus Insight.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

KEY CONSIDERATIONS FOR DATA OPS TO ENABLE ENTERPRISE DIGITAL TRANSFORMATION (DX)

In this episode Samuel Williams - Digital Strategist at Accelerate, James Hartwright - Managing Partner at Cognition, and Julien Redmond - CEO IRiS & Certus Insight, discuss how Digital Business Transformation is driving a mindset shift in Information Management practice and Data Operations.

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

In this first episode of Data Dialogues Series 2, Certus Insight GM Julien Redmond is joined by DV2 founder Dan Linstedt to discuss:

What’s happening with the Data Vault Alliance including a focus on being the one-stop shop for everything around data vault certification and training
More support and materials for authorised training partners around the world
New e-learning courses launching soon
A brand-new Vendor Tools Certification (VTC) program that's going to be available through the website
The episode concludes with a discussion on some of the debates currently going around Data Vault and a sneak peek at what you can expect if you want to attend WWDVC in 2022

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Certus Insight GM Julien Redmond is joined by former Agricompas CTO Richard Strange to discuss data science and its application to problems.

Richard is currently completing a D.Phil at Oxford University and has strong domain knowledge of the use of Data Science and applied AI in both academic research and in business.

This episode covers:

What is Data Science
What is a data model
The types of inputs to the model there are
The typical types of outputs from the model
What are the important question in the application of Data Science to any given problem

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Certus Insight GM Julien Redmond introduces this talk by Scott Ambler, Project Management Institute, recorded at the 2021 WWDVC.

Join Scott as he discusses the merits of using a Disciplined Agile approach to enable smarter ways of managing Data Warehouses and Information.

In this summary from his talk at WWDVC he introduces the key concepts to developing a mode of operation in your IM practice that enables:

Your people to learn the skills they need
The development of optimal Ways of Working (WoW)
Delivery of accelerated value to your customers

Download the DVIC platform now for your mobile device at your App store and join the DVIC community.

Join Nols Ebersohn as he discusses the fundamental Laws of Information as they pertain to architecting and managing Data Vault implementations.
He discusses…

In this summary from his talk at WWDVC he introduces the Laws of Nature for Information.

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Join Certus Industries GM Julien Redmond as he introduces content discussing what lessons we can learn from SpaceX' rocket engine development.

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Certus Insight's Richard Harris is joined by Noel Hanssens from Red Cross Life Blood - a life saving organisation that works to ensure blood services are available for Australians when they need it the most.

After recently going through a major evaluation process, RCLB decided to upgrade to Data Vault 2.0 - so why did Red Cross decide to move to Data Vault 2.0 and what are the challenges this move will solve?

To join our community, search for Data Vault Innovators Community in your app store

Join Host Paul Barlow as he talks Data Quality and Data care with Certus Insight experts Julian Redmond, James Hartwright and Richard Harris.

Together they look at the differences between Data Remediation and management, the impact of poor quality data on businesses and organisations, Data Governance, frameworks and the best way to talk your manager into understanding the need for quality Data management!

Originally recorded as an interactive video session, this Audio version may miss some visual context.

For the Video version, check out- https://youtu.be/ydEKyCQJmxQ

To join our community, search for Data Vault Innovators Community in your app store

Host Paul Barlow is joined by Certus Data Vault experts Julian Redmond, Richard Harris and Nols Ebersohn to talk about use-cases, data modelling and expertise, automation and making sure killer robots from the future can't come back in time and track you down using meta data.

This episode first streamed live on YouTube on December 2 and the video can be viewed in full. here - https://youtu.be/791EYAzDVRo

To join our community, search for Data Vault Innovators Community in your app store

Richard Harris from Certus Digital is joined by Gary Kennedy from APNIC, the regional internet registry for the Asia-Pacific region, to talk through his experiences building Data Vault through Manual Coding, and evolving that process through an automatic tooling programme to deploy Data Vault.

To join our community, search for Data Vault Innovators Community in your app store

For the latest episode of our #DataDialogues #Podcast, host Julien Redmond switches to the other side of the table to answer questions instead of asking them. Kevin Redmond, Director of Data, AI & Automation at IBM APAC, quizzes Julien about using DataOps to create a business-ready analytics function.

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

For this episode of #DataDialogues, Julien Redmond invited Alan Loader from CQU and Bryan Thomas from WhereScape to discuss the university’s journey so far using Data Vault 2.0 and WhereScape.

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

For this episode of #DataDialogues, Julien Redmond welcomes Ashwin Sinha, Chief Data Officer - Banking and Financial Services at Macquarie Bank to talk about DataOps in Financial Services. Ashwin shares his insights into the value DataOps delivers for Macquarie Bank, the role cloud and SaaS solutions play, common pitfalls to avoid, privacy concerns and much more.

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

For this episode, I'm joined by Kent Graziano, Chief Technical Evangelist & Strategic Advisor at Snowflake and my Certus colleague James Hartwright. Kent is an internationally-recognised industry expert in Cloud and Agile Data Warehousing as well as an award-winning author and speaker in the areas of data modelling, data warehousing, and data architecture.

In this episode, we cover:
- What a Data Lake is
- How Data Lakes are being used
- The role of Data Vault 2.0
- The key factors that need to come to together to deliver a platform for an organisation to manage all types of information and support all kinds of users
- And more!

Check outhttps://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

For today’s episode, I’m joined by Vincent McBurney, Information Architect at MLC Life Insurance.

Vincent is a highly experienced data management expert with over 20 years of experience in Information Integration and Governance. He is also a certified Data Vault 2.0 Trainer with over 10 years of experience implementing Data Vault Enterprise Data Warehouses.

Vincent and I talk about:

His current role at MLC Life Insurance
Essential steps of a successful Data Vault 2.0 implementation
Disciplined Agile practices in the real-world, and
Key lessons he has learned throughout his extensive career

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

In this episode, we talk about warehouse automation, with a focus on Data Vault.

To do so, I’ve invited Alexis Desai and Bryan Thomas from WhereScape.

WhereScape specialise in helping IT organizations of all sizes leverage automation to design, develop, deploy, and operate data infrastructure faster.

Together we explore:

Why automation is important for data warehouses
The key advantages of using automation with WhereScape
What’s possible with automation
Tips and tricks for automating your data warehouse

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

For today’s episode, Julien is joined by Jim Guppy, Data Engineering Manager at Robert Walters.

Jim is a highly experienced BI and information management expert. At Robert Walters, he is currently leading a team of Data Engineers on a journey from the traditional on-premise Data Warehouse to a cloud-based Data Lake.

In this episode, Jim shares:

How and why Robert Walters decided on Data Vault 2.0 as the preferred methodology for information management,
How his team managed to communicate the benefits of Data Warehouses to a non-technical business audience and
What lessons he learned from developing and supporting a Data Vault solution with a small team.

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

In this episode, I wanted to share a really exciting Proof of Concept I've been involved with recently. To do so, I invited Ross Davis from IBM to join me as he played a key role in this project.

Together, we discuss the Proof of Concept that IBM and Certus delivered for a large bank in South East Asia. By combining IBM's Cloud Pak and Data Vault 2.0, we were able to demonstrate an efficient way to move away from old, slow, cumbersome and expensive processes to a faster, more flexible and more cost-effective approach to information management.

We outline the steps taken to deliver this proof of concept successfully in only seven days, thanks to the ease of use and flexibility of IBM Cloud Pak and Data Vault 2.0.

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.

Our recent experience with our customers and the Data Vault Alliance is continuing to demonstrate that data becoming the most valuable asset for most businesses.

However, it still represents a significant challenge to manage and utilise, including things like regulatory compliance, information governance, managing user trust, or access rights.

DataDialogues is a new podcast designed to help you overcome these challenges and more.

We'll be joined by the likes of Macquarie Bank, DCIS, Robert Walters, MLC, Snowflake, WhereScape, IBM and others. Join us to hear how these companies are working to make the most out of their data.

Check out https://www.certussolutions.com/datadialogues to access more resources and get notified when future podcasts go live.