Debezium Blog

With Debezium 2.3, we introduced a preview of a brand new Debezium Operator with the aim to provide seamless deployment of Debezium Server to Kubernetes (k8s) clusters. The Debezium 2.4.0.Final release brings the next step towards the full support of this component. With this release, we are happy to announce that Debezium Operator is now available in the OperatorHub catalog for Kubernetes as well as in the community operator catalog embedded in the OpenShift and OKD distributions. The operator remains in the incubation phase; however, the full support of this component is approaching fast.

Welcome to the third installment of our series on Debezium Signaling and Notifications. In this article, we continue our exploration of Debezium signaling and notifications. In particular, we will delve into how to enable and manage these features using the JMX channel.

We will also explore how to send signals and get notifications through the REST API leveraging Jolokia.

As the summer months wind down and we enter autumn with cooler temperatures, the team has diligently prepared the next major milestone of Debezium. It’s my pleasure to announce the immediate release of the next minor version, Debezium 2.4.0.Final.

As the team begins the journey toward the next development iteration, let’s take a moment and review all the new features, changes, and improvements that are included in Debezium 2.4, which includes 231 issues resolved by 68 unique contributors.

In the previous blog post, we have shown how to leverage Debezium to train neural-network model with the existing data from the database and use this pre-trained model to classify images newly stored into the database. In this blog post, we will move it one step further - we will use Debezium to create multiple data streams from the database and use one of the streams for continuous learning and to improve our model, and the second one for making predictions on the data. When the model is constantly improved or adjusted to recent data samples, this approach is known as online machine learning. Online learning is only suitable for some use cases, and implementing an online variant of a given algorithm may be challenging or even impossible. However, in situations where online learning is possible, it becomes a very powerful tool as it allows one to react to the changes in the data in real-time and avoids the need to re-train and re-deploy new models, thus saving the hardware and operational costs. As the streams of data become more and more common, e.g. with the advent of IoT, we can expect online learning to become more and more popular. It’s usually a perfect fit for analyzing streaming data in use cases where it’s possible.