Debezium Blog

Welcome to this series of articles dedicated to signaling and notifications in Debezium! This post serves as the second installment in the series, where we will discuss how to customize the signal and notification channels in Debezium.

Debezium 2.3 introduced new improvements in signaling and notification capabilities. You can set up new signals and notification channels in addition to the pre-defined signals and notification channels offered by Debezium. This feature enables users to customize the system to suit their unique needs and combine it with their existing infrastructure or third-party solutions. It enables effective monitoring and a proactive response to data changes by precisely capturing and communicating signal events and triggering notifications through preferred channels.

The first article in this series, Signaling and Notifications in Debezium, provides an overview of the signaling and notification features in Debezium. It also discusses the available channels & their use cases for various scenarios.

This post is the final 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 installment of this series is found here and the second installment is found here.

In this third and final installment, we are going to build on what we have done in the previous two posts, focusing on the following areas:

Welcome to this series of articles dedicated to signaling and notifications in Debezium! This post serves as the first installment in the series, where we will introduce the signaling and notification features offered by Debezium and discuss the available channels for interacting with the platform.

In the subsequent parts of this series, we will delve deeper into customizing signaling channels and explore additional topics such as JMX signaling and notifications.

In this post, we are going to talk about a CDC-CQRS pipeline between a normalized relational database, MySQL, as the command database and a de-normalized NoSQL database, MongoDB, as the query database resulting in the creation of DDD Aggregates via Debezium & Kafka-Streams.

This tutorial was originally published by QuestDB, where guest contributor, Yitaek Hwang, shows us how to stream data into QuestDB with change data capture via Debezium and Kafka Connect.

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!

This post is part of a 3-part series to explore using Debezium to ingest changes from an Oracle database using Oracle LogMiner. Throughout the series, we’ll examine all the steps to setting up a proof of concept (POC) deployment for Debezium for Oracle. We will discuss setup and configurations as well as the nuances of multi-tenancy. We will also dive into any known pitfalls and concerns you may need to know and how to debug specific problems. And finally, we’ll talk about performance and monitoring to maintain a healthy connector deployment.

Throughout this exercise, we hope that this will show you just how simple it is to deploy Debezium for Oracle. This installation and setup portion of the series may seem quite complicated, but many of these steps likely already exist in a pre-existing environment. We will dive into each step, explaining it is essential should you use a container image deployment.

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.