Featured Posts

Tags
analytics announcement announcement discussion survey apache kafka apache kafka apicurio avro aws batch caassandra camel cassandra cdc channels community community stories connectors containers cqrs custom datalake db2 ddd debezium debezium server debezium ui deduplication discussion docker elasticsearch event sourcing exactly once semantics example examples features fedora flink hiring ibmi iceberg images informix integration introduction jaeger jdbc json kafka kafka streams kafka streams kogito ksql kubernetes lakehouse machine learning mariadb metrics microservices mongo mongodb monitoring mysql news newsletter notifications online learning operator oracle outbox performance postgres presentation production quarkus questdb rds releases schema scylla secrets sentry serialization signaling smt snapshots spanner spark sql sqlserver tensorflow testcontainers tests time series timescaledb topics tracing transactions ui vagrant vitess website

Debezium Blog

I’m excited to announce the release of Debezium 1.9.7.Final!

This release focuses on bug fixes and stability; and is the recommended update for all users from earlier versions. This release contains 22 resolved issues overall.

Read More    
October 20, 2022 by Vojtěch Juránek

When developing the tests for your project, sooner or later you will probably get into the situation when some of the tests fail randomly. These tests, also known as flaky tests, are very unpleasant as you never know if the failure was random or there is a regression in your code. In the worst case you just ignore these tests because you know they are flaky. Most of the testing frameworks even have a dedicated annotation or other means to express that the test is flaky and if it fails, the failure should be ignored. The value of such a test is very questionable. The best thing you can do with such a test is of course to fix it so that it doesn’t fail randomly. That’s easy to say, but harder to do. The hardest part is usually to make the test fail in your development environment so that you can debug it and understand why it fails and what is the root cause of the failure. In this blog post I’ll try to show a few techniques which may help you to simulate random test failures on you local machine.

Read More    

Today it’s my great pleasure to announce the availability of Debezium 2.0.0.Final!

Since our 1.0 release in December 2019, the community has worked vigorously to build a comprehensive open-source low-latency platform for change data capture (CDC). Over the past three years, we have extended Debezium’s portfolio to include a stable connector for Oracle, a community led connector for Vitess, the introduction of incremental snapshots, multi-partition support, and so much more. With the help of our active community of contributors and committers, Debezium is the de facto leader in the CDC space, deployed to production within lots of organizations from across multiple industries, using hundreds of connectors to stream data changes out of thousands of database platforms.

The 2.0 release marks a new milestone for Debezium, one that we are proud to share with each of you.

Read More    

I am excited to announce the release of Debezium 2.0.0.CR1!

This release contains breaking changes, stability fixes, and bug fixes, all to inch us closer to 2.0.0.Final. Overall, this release contains a total of 53 issues that were fixed.

Read More    

This post is part of a 3-part series to explore using Debezium to ingest changes from an Oracle database using Oracle LogMiner. In case you missed it, the first part of this series is here.

In this second installment, we will build on what we did in part one by deploying the Oracle connector using Zookeeper, Kafka, and Kafka Connect. We are going to discuss a variety of configuration options for the connector and why they’re essential. And finally, we’re going to see the connector in action!

Read More