Debezium Blog
The team has been quite busy these last couple months preparing for a condensed release timeline for Debezium 2.3, and I am thrilled to announce that the next installment has arrived, Debezium 2.3.0.Final is now available! Despite a condensed release schedule, this release is packed with tons of new features and improvements. Debezium 2.3 includes a brand-new notification subsystem, a rewrite of the signal subsystem to support additional means to send signals to Debezium connectors,...
It is my pleasure to announce the next Debezium 2.3 release, 2.3.0.CR1!
The main focus of this release is to stabilize the Debezium 2.3 release in preparation for a final release in the coming weeks, which typically means we’re focusing on bugfixes; however, this release includes two new features. Lets take a moment and dive into these new features and any bug fixes that are noteworthy!
It’s my pleasure to announce the next release of the Debezium 2.3 series, 2.3.0.Beta1!
While this release focuses primarily on bug fixes and stability improvements, there are some new improvements with the PostgreSQL connector and the new notification and channels subsystem. In addition, there are also some compatibility breaking changes.
This release contains changes for 22 issues, so lets take a moment and dive into the new features and any potential bug fixes or breaking changes that are noteworthy!
It’s my pleasure to announce the first release of the Debezium 2.3 series, 2.3.0.Alpha1!
This release brings many new and exciting features as well as bug fixes, including Debezium status notifications, storage of Debezium state into a JDBC data store, configurable signaling channels, the ability to edit connector configurations via Debezium UI, the parallelization of Vitess shards processing, and much more.
This release contains changes for 59 issues, so lets take a moment and dive into several of these new features and any potential bug fixes or breaking changes that are noteworthy!
With the recent success of ChatGPT, we can observe another wave of interest in the AI field and machine learning in general. The previous wave of interest in this field was, at least to a certain extent, caused by the fact that excellent ML frameworks like TensorFlow, PyTorch or general data processing frameworks like Spark became available and made the writing of ML models much more straightforward. Since that time, these frameworks have matured, and writing models are even more accessible, as you will see later in this blog. However, data set preparation and gathering data from various sources can sometimes take time and effort. Creating a complete pipeline that would pull existing or newly created data, adjust it, and ingest it into selected ML libraries can be challenging. Let’s investigate if Debezium can help with this task and explore how we can leverage Debezium’s capabilities to make it easier.
