Introduction
Even if it doesn't appeal to you, you might want to think about it when you work in a larger microservice landscape or have a serious big data platform...Proof data consistency in a microservice landscape. When we google this subject I already get 1.2 MLN results so there’s something going on here.
To ensure data consistency several practices are available:
Saga Pattern
Reconciliation
Event Log
Orchestration vs. ChoreographySingle-Write With EventsChange-First
Event-First
Consistency by Design
Accepting Inconsistency
But in this episode, we won't go over these practices.
What this episode covers
We will dive into the verification part. The proof of the correct operation of your implementation.
Within bol.com we implemented a Data Quality Service (DQS). Actually, the second generation is already in place. The first generation focused on the immutable data in the 2nd improved version mutable data is covered as well. We will go over these questions to explain how we proof data consistency in a microservice landscape:
StatementsAs a starter, we discuss these statements first
Guests
* Mykola Gurov – Of course, you all know him since he was in our very first episode about Kotlin. Or otherwise from one of his testing in production talks. Jack of all trades.
* Chris Gunnink – Software Engineer on a crusade - DQS
* Sourygna Luangsay – Tech Lead in experimentation, forecasting and the finance product a lot more products
Notes
Bigquery - bol.com adoption story
BigQuery - Google’s Data warehouse running in the Google Cloud Platform (GCP)