Change Data Capture For All Of Your Databases With Debezium Data Engineering Podcast

Summary
Databases are useful for inspecting the current state of your application, but inspecting the history of that data can get messy without a way to track changes as they happen. Debezium is an open source platform for reliable change data capture that you can use to build supplemental systems for everything from maintaining audit trails to real-time updates of your data warehouse. In this episode Gunnar Morling and Randall Hauch explain why it got started, how it works, and some of the myriad ways that you can use it. If you have ever struggled with implementing your own change data capture pipeline, or understanding when it would be useful then this episode is for you.

Announcements

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Your host is Tobias Macey and today I’m interviewing Randall Hauch and Gunnar Morling about Debezium, an open source distributed platform for change data capture

Interview

Introduction
How did you get involved in the area of data management?
Can you start by describing what Change Data Capture is and some of the ways that it can be used?
What is Debezium and what problems does it solve?

What was your motivation for creating it?
What are some of the use cases that it enables?
What are some of the other options on the market for handling change data capture?


Can you describe the systems architecture of Debezium and how it has evolved since it was first created?

How has the tight coupling with Kafka impacted the direction and capabilities of Debezium?
What, if any, other substrates does Debezium support (e.g. Pulsar, Bookkeeper, Pravega)?


What are the data sources that are supported by Debezium?

Given that you have branched into non-relational stores, how have you approached organization of the code to allow for handling the specifics of those engines while retaining a common core set of functionality?


What is involved in deploying, integrating, and maintaining an installation of Debezium?

What are the scaling factors?
What are some of the edge cases that users and operators should be aware of?


Debezium handles the ingestion and distribution of database changesets. What are the downstream challenges or complications that application designers or systems architects have to deal with to make use of that information?

What are some of the design tensions that exist in the Debezium community between acting as a simple pipe vs. adding functionality for interpretin