I’m very happy to announce the release of Debezium 1.3.0.CR1!
As we approach the final stretch of Debezium 1.3 Final, we took this opportunity to add delegate converter support for the
ByteBufferConverter and introduce a
debezium-scripting module. In addition, there’s also a range of bug fixes and quite a bit of documentation polish; overall, not less than 15 issues have been resolved for this release.
I’m very happy to announce the release of Debezium 1.3.0.Beta2!
In this release we’ve improved support for column filtering for the MySQL and SQL Server connectors, and there’s a brand-new implementation for ingesting change events from Oracle, using the LogMiner package. As we’re on the home stretch towards Debezium 1.3 Final, there’s also a wide range of smaller improvements, bug fixes and documentation clarifications; overall, not less than 44 issues have been resolved for this release.
It’s my pleasure to announce the release of Debezium 1.3.0.Beta1!
This release upgrades to the recently released Apache Kafka version 2.6.0, fixes several critical bugs and comes with a renaming of the connector configuration options for selecting the tables to be captured. We’ve also released Debezium 1.2.2.Final, which is a drop-in replacement for all users of earlier 1.2.x releases.
Release early, release often! After the 1.1 Beta1 and 1.0.1 Final releases earlier this week, I’m today happy to share the news about the release of Debezium 1.1.0.Beta2!
The main addition in Beta2 is support for integration tests of your change data capture (CDC) set-up using Testcontainers. In addition, the Quarkus extension for implementing the outbox pattern as well as the SMT for extracting the
after state of change events have been re-worked and offer more configuration flexibility now.
This article is a dive into the realms of Event Sourcing, Command Query Responsibility Segregation (CQRS), Change Data Capture (CDC), and the Outbox Pattern. Much needed clarity on the value of these solutions will be presented. Additionally, two differing designs will be explained in detail with the pros/cons of each.
So why do all these solutions even matter? They matter because many teams are building microservices and distributing data across multiple data stores. One system of microservices might involve relational databases, object stores, in-memory caches, and even searchable indexes of data. Data can quickly become lost, out of sync, or even corrupted therefore resulting in disastrous consequences for mission critical systems.
Solutions that help avoid these serious problems are of paramount importance for many organizations. Unfortunately, many vital solutions are somewhat difficult to understand; Event Sourcing, CQRS, CDC, and Outbox are no exception. Please look at these solutions as an opportunity to learn and understand how they could apply to your specific use cases.
As you will find out at the end of this article, I will propose that three of these four solutions have high value, while the other should be discouraged except for the rarest of circumstances. The advice given in this article should be evaluated against your specific needs, because, in some cases, none of these four solutions would be a good fit.