Best of Change Data Capture — 2025

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   Article

   Foojay.io·1y

   Event-Driven Architecture and Change Data Capture Made Easy

   Event-Driven Architecture (EDA) and Change Data Capture (CDC) are key techniques in modern software systems. EDA relies on components producing and consuming events to trigger actions, making systems flexible and scalable. CDC tracks database changes and converts them into events for other systems. EDA is used for decoupling services and real-time communication, while CDC synchronizes data and powers analytics. They can be used together for combining decoupled workflows with real-time data tracking. Understanding when to use each can help build efficient and maintainable systems.

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2. 2
   Article

   ByteByteGo·31w

   How Pinterest Transfers Hundreds of Terabytes of Data With CDC

   Pinterest built a unified Change Data Capture platform to handle thousands of database shards and millions of queries per second. The system uses Debezium and Apache Kafka with a two-layer architecture: a control plane that manages connector configurations and a data plane that streams database changes. Key challenges included out-of-memory errors from large backlogs, frequent task rebalancing causing instability, slow failover recovery taking over two hours, and duplicate tasks from a Kafka bug. Solutions involved bootstrapping from latest offsets, increasing rebalance timeouts to 10 minutes, enabling worker-level shard discovery, and upgrading to Kafka 2.8.2 version 3.6, which reduced CPU usage from 99% to 45% and stabilized the system to run 3,000 tasks reliably.

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3. 3
   Article

   PlanetScale·35w

   Postgres High Availability with CDC — PlanetScale

   Postgres high availability faces operational challenges when using Change Data Capture (CDC) due to logical replication slots being primary-local objects that can prevent failover. When CDC clients lag or poll infrequently, standby replicas may become ineligible for promotion, forcing administrators to choose between waiting for CDC advancement or breaking the CDC stream. MySQL's GTID-based approach avoids this coupling by embedding replication progress in the binary log itself, allowing seamless failover regardless of CDC client behavior.

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4. 4
   Article

   Debezium·34w

   Debezium 3.3.0.Final Released

   Debezium 3.3.0.Final introduces major enhancements including a new Quarkus extension for PostgreSQL integration, a CockroachDB connector, Apache Kafka 4.1 support, and exactly-once semantics for all core connectors. The release includes OpenLineage support for MongoDB and JDBC sink connectors, improved performance optimizations across Oracle, PostgreSQL, and MySQL connectors, and enhanced Debezium Platform features like smart editor and connection management. Breaking changes include removal of deprecated snapshot modes and updates to JDBC sink data type precision handling.

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5. 5
   Article

   Debezium·22w

   Debezium 3.4.0.Final Released

   Debezium 3.4.0.Final has been released with over 125 new features, improvements, and fixes. Key updates include Kafka 4.1.1 support, PostgreSQL 18 compatibility, new geometry transformations, Quarkus DevService extensions for native CDC applications, and improved Oracle LogMiner metrics. Breaking changes affect IBMi string trimming, Oracle XML dependencies, PostgreSQL 13 support ending, and SQL Server streaming query modes. The release adds incremental snapshots for IBMi, AWS IAM authentication for PostgreSQL, multiple DevService support in Quarkus, and geometry data type handling in the JDBC sink.

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