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Debezium

🔄Debezium

NEW

Open-source distributed change data capture platform that streams database change events to Kafka and other sinks.

DESCRIPTION

Debezium is a community-driven CDC platform built on Kafka Connect that captures changes from databases by reading their transaction logs. It supports popular databases including MySQL, PostgreSQL, SQL Server, Oracle, and MongoDB. Debezium publishes change events to Kafka topics with schema information, enabling downstream consumers to react in near real-time. It helps implement event-driven architectures, maintain materialized views, and synchronize data across services. Patterns like the outbox ensure reliable event emission from transactional systems. Proper configuration of connectors, offsets, and error handling ensures resilient pipelines. Debezium is widely used to modernize legacy integration and reduce polling-based architectures.

ALTERNATIVE DEFINITIONS

Debezium

Debezium is an open source distributed platform for change data capture (CDC).

Highlights open-source nature and distributed CDC focus.

Source

Debezium (GitHub)

Debezium is a distributed platform for change data capture. It streams changes from databases to Kafka.

Emphasizes Kafka-based streaming architecture and database log capture.

Source

Confluent

Debezium connectors capture row-level changes in databases and publish change events to Kafka topics.

Vendor perspective focused on Kafka Connect integration and event publication.

Source

Change Data Capture Apache Kafka Kafka Connect Log-based CDC Outbox Pattern Event Streaming Data Integration Schema Registry PostgreSQL MySQL

COMPETING TERMS

Oracle GoldenGate AWS Database Migration Service StreamSets

KNOWLEDGE AREAS

Data Engineering Real-time & Streaming Data Data Integration & Interoperability Data Services

TARGET AUDIENCE

Data Engineer MLOps Engineer Data Architect Cloud Architect Solution Architect

EXTERNAL SOURCES

  • Wikipedia - Change data capture
  • Debezium - Official Debezium documentation
  • Confluent - Confluent Debezium connector guide
  • AWS - AWS DMS CDC alternative

TOP QUESTIONS

What is Debezium and why use it?

Debezium is an open-source CDC platform that streams database changes in real time. It reduces polling, keeps systems synchronized, and enables event-driven architectures. It integrates tightly with Kafka.

Category: Overview

How does Debezium capture changes reliably?

Debezium reads transaction logs (binlogs, WAL, etc.), ensuring consistent, ordered events. It relies on Kafka Connect for scalability, offset management, and fault tolerance.

Category: Requirements

Which databases does Debezium support?

Debezium supports MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, and more. Connector capabilities vary by engine and version; consult documentation before production use.

Category: Overview

What is a hot topic around Debezium today?

Patterns like the outbox and exactly-once semantics, and using Debezium with Kafka Streams or Flink for materialized views, are widely discussed.

Category: Topic

What are common mistakes when deploying Debezium?

Underprovisioning Kafka or Connect, ignoring schema evolution, and failing to plan rebalances and connector restarts can cause data loss or duplication.

Category: Mistakes

What are the risks of using Debezium?

Misconfiguration can lead to backfills, duplicate events, or lag. Security and access to transaction logs must be controlled to avoid exposure of sensitive changes.

Category: Risks

What are alternatives or competitors to Debezium?

Oracle GoldenGate, AWS Database Migration Service, and StreamSets provide CDC capabilities, often with managed or proprietary features.

Category: Alternatives

How is Debezium managed in production?

Operate through Kafka Connect with monitoring, scaling, offset backups, and schema registry integration. Use metrics, alerting, and change management procedures.

Category: Management

How do you start learning Debezium?

Begin with the Debezium tutorial, run a local Kafka Connect + sample database, and explore connectors, topics, and event payloads.

Category: Example