Debezium Blog

This post originally appeared on the Bolt Labs Engineering blog.

Traditionally, MySQL has been used to power most of the backend services at Bolt. We’ve designed our schemas in a way that they’re sharded into different MySQL clusters. Each MySQL cluster contains a subset of data and consists of one primary and multiple replication nodes.

Once data is persisted to the database, we use the Debezium MySQL Connector to capture data change events and send them to Kafka. This gives us an easy and reliable way to communicate changes between back-end microservices.

Hello everyone, my name is Anisha Mohanty and I recently joined Red Hat and the Debezium team.

I started my journey with Red Hat in April 2020 after completing my graduation. I was introduced to open source in my early college days, but I wasn’t aware of how organizations work and wanted to get the essence of open source ethics and values. That is something that I am fascinated to learn as I joined Red Hat.

My work started under the Data Virtualization team with Teiid and then under the GRAPHQLCRUD project which is a standard for a generic query interface on top of GraphQL. The project has started well and is in great shape right now. We have successfully added CRUD capabilities, paging, and filtering specifications.

Coming to Debezium, I first heard about it as some DV members started contributing here, well back then it was a completely new thing for me. I started exploring more, and it was not long when I had my first interaction with Gunnar and Jiri. With a warm welcome and great team here, I am really excited to work with the Debezium Community.

I am excited to announce the release of Debezium 1.4.0.Alpha1!

This first pass of the 1.4 release line provides a few useful new features:

Overall, the community fixed 41 issues for this release. Let’s take a closer look at some of the highlights.

Over the last five years, Debezium has become a leading open-source solution for change data capture for a variety of databases. Users from all kinds of industries work with Debezium for use cases like replication of data from operational databases into data warehouses, updating caches and search indexes, driving streaming queries via Kafka Streams or Apache Flink, synchronizing data between microservices, and many more.

When talking to Debezium users, we generally receive very good feedback on the range of applications enabled by Debezium and its flexibility: e.g. each connector can be configured and fine-tuned in many ways, depending on your specific requirements. A large number of metrics provide deep insight into the state of running Debezium connectors, allowing to safely operate CDC pipelines also in huge installations with thousands of connectors.

All this comes at the cost of a learning curve, though: users new to Debezium need to understand the different options and settings as well as learn about best practices for running Debezium in production. We’re therefore constantly exploring how the user experience of Debezium can be further improved, allowing people to set up and operate its connectors more easily.

Welcome to the first edition of "Debezium Community Stories With…​", a new series of interviews with members of the Debezium and change data capture community, such as users, contributors or integrators. We’re planning to publish more parts of this series in a loose rhythm, so if you’d like to be part of it, please let us know. In today’s edition it’s my pleasure to talk to Renato Mefi, a long-time Debezium user and contributor.