What Is Debezium? Change Data Capture for Modern Data Platforms

Written By:
Founder & CTO
June 17, 2025
Understanding the need for modern data synchronization

In today’s distributed, real-time world, data doesn’t just live in one place, it moves between services, platforms, and systems continuously. As companies embrace microservices, real-time dashboards, hybrid clouds, and decentralized architectures, the need to synchronize data changes in real time becomes critical. This is where Debezium, a powerful Change Data Capture (CDC) tool, comes into play.

Debezium acts as a bridge between traditional databases and real-time event-driven platforms by capturing and broadcasting every row-level change in your databases. Instead of polling or periodically dumping the entire database, Debezium continuously monitors database logs and streams only the changes, helping your applications stay responsive, your data platforms stay consistent, and your microservices stay decoupled yet in sync.

What Exactly Is Debezium?
The open-source CDC engine for developers and data platforms

Debezium is a distributed platform for Change Data Capture that transforms changes in your databases, such as inserts, updates, and deletes, into streaming events. It runs on Apache Kafka Connect, a core part of the Apache Kafka ecosystem, and supports a variety of popular databases including MySQL, PostgreSQL, MongoDB, Oracle, SQL Server, Db2, and more.

At its core, Debezium allows developers to subscribe to database changes as they happen, turning the database into an event stream that other systems can consume in near real time. This is not just about sending updates, it’s about making your database part of your event architecture without modifying application code or adding triggers that increase complexity or reduce performance.

Whether you're building event-driven architectures, modern data pipelines, or implementing real-time analytics, Debezium enables your systems to react to data changes with minimal latency and maximum reliability.

Why Developers Love Debezium
1. Log-Based Change Data Capture with Minimal Overhead

Traditional change-tracking systems rely on queries, batch jobs, or triggers that can slow down your database. Debezium avoids this entirely by using log-based CDC, reading directly from the database’s transaction log.

For example:

  • MySQL: Debezium reads from the binary log (binlog).

  • PostgreSQL: It reads from the Write-Ahead Log (WAL).

  • MongoDB: It listens to the oplog.

These logs are already generated as part of normal DB operation, so Debezium introduces negligible performance impact. This log-based design enables scalable, production-grade CDC suitable for mission-critical systems.

2. Low Latency, High Consistency

Debezium captures changes as soon as they are committed to the database. The latency is often measured in milliseconds, which makes it a great fit for use cases requiring near real-time synchronization between services or systems. This ensures your consuming applications or services always have the latest snapshot of what’s happening, whether it's an order being placed, an account updated, or an inventory adjustment.

By integrating with Kafka topics, Debezium also supports replayability and exact-once delivery semantics when configured correctly, so you can recover from crashes or redeploy services without missing a beat.

3. Fault Tolerance and Durability via Kafka

Debezium is not a standalone CDC engine, it is designed to run on Kafka Connect, inheriting its scalability and fault-tolerant design. All events are persisted in Kafka topics, meaning consumers can:

  • Restart from their last offset

  • Reprocess historical data

  • Scale out horizontally with parallel processing

This makes Debezium incredibly robust, even in the face of network failures or system outages. Developers no longer need to write custom retry logic or manage temporary file queues, Kafka and Debezium handle it all.

4. Schema Evolution and Compatibility

Debezium supports schema change detection. When your database schema changes, say, a column is added or renamed, Debezium can detect and propagate that information in the change events it emits.

When paired with Kafka Schema Registry, the events can be serialized in formats like Avro or Protobuf to ensure compatibility and validation across services. This is vital for enterprises where schemas are always evolving, but systems still need to remain backward-compatible and robust to changes.

Under the Hood: How Debezium Works
Monitoring database transaction logs

Debezium connectors monitor the underlying database transaction logs directly, so instead of checking for changes via SELECT queries, they see what the database itself is committing. This guarantees:

  • Full fidelity of changes (before and after values)

  • Ordering of changes is preserved

  • Transactional boundaries are honored

Each event contains:

  • Operation type (insert, update, delete)

  • Timestamp

  • Table and schema

  • Row data before and after the change

  • Metadata (transaction ID, offset, etc.)

These events are published to Kafka topics, such as:
inventory.customers → changes to the customers table in the inventory DB.

Kafka Connect + Debezium Connectors

Debezium leverages Kafka Connect’s plugin architecture. You deploy a specific connector based on your database (e.g., MySqlConnector, PostgresConnector). Each connector has its own configuration file, specifying connection settings, topic names, snapshot settings, and more.

Once registered, the connector performs:

  1. Snapshot phase: Captures the current state of all selected tables.

  2. Streaming phase: Continuously emits changes as they are recorded in the logs.

This two-phase approach ensures that new consumers can immediately work with the most up-to-date snapshot, while also catching all live updates thereafter.

Use Cases of Debezium in Real-World Applications
Event-Driven Microservices

Modern microservices architectures often rely on messaging and events for communication. With Debezium, database updates can become events, without requiring services to poll or call APIs.

For instance:

  • Order Service updates orders table → Debezium emits event → Notification Service triggers email

  • Payment Service logs transaction → CDC → Fraud Detection System consumes and evaluates in real time

This decouples service logic from database logic and enables asynchronous communication, which scales more effectively.

Cache Invalidation and Real-Time Indexing

Debezium is often used to invalidate or update cache layers such as Redis or Memcached whenever a change happens in the source database. This prevents the need for hard time-based expiry policies.

Similarly, search indexes like Elasticsearch can be updated with new content on the fly:

  • Blog edited in PostgreSQL → Debezium triggers update → Search engine stays fresh
    This keeps your search systems synchronized and always relevant.

Real-Time ETL and Analytics

Traditional ETL (Extract, Transform, Load) pipelines run on a schedule, every few hours or nightly. In contrast, Debezium enables streaming ETL, where data is extracted and transformed in real time using tools like:

  • Apache Flink

  • Apache Beam

  • ksqlDB

  • Spark Structured Streaming

This is especially useful for:

  • Real-time dashboards

  • Streaming analytics

  • Predictive models with fresh data

No more waiting for stale reports, data is always live.

Cross-Database Replication

Debezium can help you synchronize different databases in real time, whether for cloud migration, backup, or data warehouse ingestion.

For example:

  • Sync MySQL → PostgreSQL with custom consumers

  • Stream MongoDB into Snowflake via Kafka

Since Debezium emits events that describe the raw data change, the destination system can transform and store it as needed, offering complete flexibility and minimizing downtime.

Auditing and Compliance

Because every change is recorded with metadata and timestamps, Debezium effectively serves as a passive audit trail. You can track:

  • Who changed what

  • When the change occurred

  • What the previous values were

This is particularly useful for:

  • GDPR/CCPA compliance

  • Financial regulations

  • Healthcare data integrity

Advantages Over Traditional Approaches
Lightweight and Real-Time

Debezium doesn’t scan tables or lock rows. It listens to logs that the database already writes for durability, so there's no additional cost to performance and latency remains extremely low.

Built for Scale

Traditional tools often struggle with scaling. With Kafka and Debezium:

  • Add more Kafka consumers to parallelize processing

  • Horizontally scale Kafka Connect worker nodes

  • Replay events from any point using offset management

Event-Driven by Design

Debezium promotes an event-first mentality where every data change is a first-class citizen. This helps developers move away from request/response models to asynchronous architectures, which are more scalable and resilient.

Decoupled and Future-Proof

Producers (Debezium connectors) and consumers (services, processors, sinks) are completely decoupled. This means you can change consumers or add new downstream systems without touching the source database.

Getting Started with Debezium: Developer Guide
Tools and Prerequisites

To get Debezium running, you’ll typically need:

  • Apache Kafka

  • Kafka Connect

  • Debezium Connector plugin

  • A supported source database with CDC features enabled

  • Optional: Kafka UI (like Confluent Control Center or Redpanda Console)

Dockerized quick-starts are available on Debezium’s official GitHub repo for MySQL, Postgres, and MongoDB.

Configuration Steps
  1. Deploy Kafka and Kafka Connect

  2. Enable logging on your database (e.g., binlog_format=ROW for MySQL)

  3. Add the Debezium connector to Kafka Connect

  4. Register the connector configuration JSON

  5. Watch change events stream to Kafka topics

  6. Consume using custom services, Kafka Streams, or sink connectors

The Ecosystem Around Debezium
  • Apache Kafka: Durable, scalable event streaming platform

  • Kafka Streams / ksqlDB: Transform data in-stream using SQL-like syntax

  • Apache Flink / Spark: Complex transformations and aggregations

  • Schema Registry: Enforces backward/forward compatibility

  • Sink Connectors: Push data to Elasticsearch, S3, JDBC, BigQuery, etc.

Debezium fits seamlessly into any data mesh or modern data architecture and continues to expand with new connectors, community contributions, and foundation support.

Final Thoughts: Should You Use Debezium?

If you're working on:

  • Microservices needing synchronization

  • Building real-time analytics pipelines

  • Keeping cache layers or search indexes fresh

  • Cross-database replication

  • Auditing systems

Then Debezium is one of the best CDC solutions available, low-latency, high-throughput, developer-friendly, and enterprise-ready. It simplifies architectural complexity while enabling new real-time capabilities that were previously impossible or painful to build.