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Debezium

By Red Hat

AdvancedTool7.4K learners

Debezium is an open-source distributed platform for change data capture (CDC) that captures row-level changes in databases and streams them as event logs, typically via Apache Kafka.

Definition

Debezium is an open-source distributed platform for change data capture (CDC) that captures row-level changes in databases and streams them as event logs, typically via Apache Kafka.

Overview

Debezium watches a database's transaction or replication log directly, rather than repeatedly polling tables for changes, which lets it capture inserts, updates, and deletes as an ordered, low-latency stream of events as they happen. It's built as a set of source connectors for Apache Kafka Connect, with connectors available for databases including PostgreSQL, MySQL, MongoDB, SQL Server, and Oracle. Each connector reads the database's native replication mechanism — for example, the binlog in MySQL or the write-ahead log (WAL) in PostgreSQL — and can also perform an initial snapshot of existing data before switching to streaming ongoing changes. Debezium is a common building block for event-driven architectures: streaming changes into Kafka to keep search indexes like Elasticsearch in sync, feeding real-time analytics pipelines, or replicating data between systems without batch ETL delays. Understanding this kind of streaming pipeline is a natural extension of the concepts in SkillVeris's Apache Kafka & Messaging course.

Key Features

  • Log-based change data capture — reads transaction/replication logs rather than polling
  • Kafka Connect source connectors for MySQL, PostgreSQL, MongoDB, SQL Server, Oracle, and more
  • Captures inserts, updates, and deletes as ordered, structured events
  • Initial snapshotting of existing data before streaming ongoing changes
  • Schema change tracking alongside data changes
  • Configurable delivery semantics depending on connector and setup
  • Open source and vendor-neutral, deployable via Kafka Connect or as an embedded engine

Use Cases

Streaming database changes into Apache Kafka for event-driven architectures
Keeping search indexes like Elasticsearch in sync with a primary database
Feeding real-time data pipelines and data lakes without batch ETL delays
Cache invalidation and microservice data synchronization
Auditing and replicating data across heterogeneous database systems

Frequently Asked Questions