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Debezium Blog

Setting up change data capture (CDC) pipelines with Debezium typically is a matter of configuration, without any programming being involved. It’s still a very good idea to have automated tests for your CDC set-up, making sure that everything is configured correctly and that your Debezium connectors are set up as intended.

There’s two main components involved whose configuration need consideration:

  • The source database: it must be set up so that Debezium can connect to it and retrieve change events; details depend on the specific database, e.g. for MySQL the binlog must be in "row" mode, for Postgres, one of the supported logical decoding plug-ins must be installed, etc.

  • The Debezium connector: it must be configured using the right database host and credentials, possibly using SSL, applying table and column filters, potentially one or more single message transformations (SMTs), etc.

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It’s my pleasure to announce the release of Debezium 1.1.0.CR1!

This release brings a brand-new API module, including a facility for overriding the schema and value conversion of specific columns. The Postgres connector gained the ability to reconnect to the database after a connection loss, and the MongoDB connector supports the metrics known from other connectors now.

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We have developed a Debezium connector for usage with Db2 which is now available as part of the Debezium incubator. Here we describe the use case we have for Change Data Capture (CDC), the various approaches that already exist in the Db2 ecology, and how we came to Debezium. In addition, we motivate the approach we took to implementing the Db2 Debezium connector.

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In this blog post, we are going to discuss how Delhivery, the leading supply chain services company in India, is using Debezium to power a lot of different business use-cases ranging from driving event driven microservices, providing data integration and moving operational data to a data warehouse for real-time analytics and reporting. We will also take a look at the early mistakes we made when integrating Debezium and how we solved them so that any future users can avoid them, discuss one of the more challenging production incidents we faced and how Debezium helped ensure we could recover without any data loss. In closing, we discuss what value Debezium has provided us, areas where we believe there is a scope for improvement and how Debezium fits into our future goals.

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One of the typical Debezium uses cases is to use change data capture to integrate a legacy system with other systems in the organization. There are multiple ways how to achieve this goal

  • Write data to Kafka using Debezium and follow with a combination of Kafka Streams pipelines and Kafka Connect connectors to deliver the changes to other systems

  • Use Debezium Embedded engine in a Java standalone application and write the integration code using plain Java; that’s often used to send change events to alternative messaging infrastructure such as Amazon Kinesis, Google Pub/Sub etc.

  • Use an existing integration framework or service bus to express the pipeline logic

This article is focusing on the third option - a dedicated integration framework.

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