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Debezium Blog

I’m very happy to announce the release of Debezium 1.8.0.Beta1!

This release is packed with exciting new features like support for MongoDB 5.0, an outbox event router for the MongoDB connector and support for Postgres logical decoding messages, as well as tons of bugfixes and other improvements. Overall, not less than 63 issues have been fixed for this release.

Let’s take a closer look at some of them.

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The Debezium UI team is pleased to announce support for Single Message Transformations (SMTs) in the Debezium UI!

Our goal with the Debezium graphical user interface is to allow users to set up and operate connectors more easily. To that end, we have added support for Kafka Connect single message transformations to the UI. Read futher for more information, and for a video demo of the new feature!

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It’s my pleasure to announce the second release of the Debezium 1.8 series, 1.8.0.Alpha2!

With the holiday season just around the corner, the team’s release schedule remains steadfast. While Debezium 1.8.0.Alpha2 delivers quite a lot of bugfixes and minor changes, there are a few notable changes:

  • MySQL support for heartbeat action queries

  • Configurable transaction topic name

In addition, the latest 1.2 tag of the debezium/tooling image is available. The newest version includes all the latest tools, including kcctl, a super simple, cuddly CLI for Apache Kafka Connect.

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It’s my pleasure to announce the first release of the Debezium 1.8 series, 1.8.0.Alpha1!

With the colors of Autumn upon us, the team has been hard at work painting lines of code for this release. With Debezium 1.8.0.Alpha1 comes quite a number of improvements but most notably is the new native MongoDB 4.0 change streams support!

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Today, it is a common practise to build data lakes for analytics, reporting or machine learning needs.

In this blog post we will describe a simple way to build a data lake. The solution is using a realtime data pipeline based on Debezium, supporting ACID transactions, SQL updates and is highly scalable. And it’s not required to have Apache Kafka or Apache Spark applications to build the data feed, reducing complexity of the overall solution.

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