Finally, CDC Explained: How Real-Time Data Capture Powers Modern Systems

What CDC Is

CDC (Change Data Capture) continuously detects INSERT, UPDATE, and DELETE operations at the database layer and propagates the changes downstream in real time. Traditional synchronization methods—full‑table copy, timestamp‑based incremental sync (which cannot capture deletions), and application‑level dual writes (invasive and error‑prone)—are passive, lagging, and often miss delete events.

How CDC Works

The most reliable implementations read the database transaction log, parse change events, and emit standardized records. The core workflow is Read log → Parse change → Output standard event .

MySQL (Binlog)

MySQL records every data modification in the binary log (Binlog), originally for master‑slave replication. CDC tools act as a replica, subscribing to the Binlog and extracting change events. Binlog supports three formats: STATEMENT, ROW, and MIXED. CDC requires the ROW format because it records before‑ and after‑images of each row, enabling precise reconstruction of changes.

PostgreSQL (WAL)

PostgreSQL writes changes to the Write‑Ahead Log (WAL). CDC uses logical replication slots to consume WAL entries without interfering with normal replication. Slots retain the consumption position; if a slot remains idle for too long, WAL files accumulate and can fill the disk, so monitoring and cleanup procedures are required.

Oracle (LogMiner)

Oracle CDC relies on the LogMiner tool to read online redo logs and archived logs. The database must run in ARCHIVELOG mode. Configuration is complex and incurs licensing costs, making it suitable mainly for regulated industries.

SQL Server (Built‑in CDC)

SQL Server provides a native CDC feature that writes changes to dedicated change tables. This approach is easier to configure but offers slightly lower performance and real‑time guarantees compared to log‑based CDC.

Typical CDC Tools

FineDataLink

Enterprise‑grade data integration platform with out‑of‑the‑box CDC for MySQL, Oracle, PostgreSQL, and SQL Server.

Visual configuration, seamless transition from full snapshot to incremental capture.

Precise INSERT/UPDATE/DELETE capture, built‑in monitoring, end‑to‑end data flow without extra components.

Debezium

Most active open‑source CDC framework; supports MySQL, PostgreSQL, MongoDB, Oracle, SQL Server, Db2, etc.

Runs on Kafka Connect, emitting change events to Kafka topics.

Features automatic snapshot‑incremental handoff and Schema Registry integration.

Requires a Kafka stack, adding adoption overhead for teams without Kafka.

Canal

Alibaba’s open‑source MySQL‑specific CDC tool.

Architecture: Canal Server reads Binlog; Canal Client consumes events; optional Canal Adapter syncs to MySQL, Elasticsearch, HBase, etc.

Lightweight and easy to deploy but limited to MySQL and has weaker schema management than Debezium.

Flink CDC

Embedded as a source in Apache Flink, enabling a closed‑loop of capture, compute, and sink without a separate Kafka layer.

Benefits: lower latency, deep Flink integration, lock‑free parallel snapshots (2.x+), SQL‑based source definition.

Limitations: requires primary keys, manual schema‑change handling, and a Flink cluster.

Maxwell

Lightweight MySQL CDC tool that reads Binlog and outputs JSON to Kafka, RabbitMQ, Redis, or files.

Simple configuration, suitable for lightweight incremental scenarios.

Only supports MySQL, lacks full‑snapshot capability, and has limited community resources for complex use cases.

Common CDC Use Cases

Real‑time synchronization from operational databases to data warehouses or lakes, reducing latency from hours to seconds or milliseconds.

Cache invalidation or update (e.g., Redis) by listening to database changes.

Cross‑service data sync in micro‑service architectures, decoupling services via event streams.

Audit logging to retain a complete history of data modifications.

Search engine synchronization (e.g., Elasticsearch) to keep indexes up‑to‑date.

Reference CDC Pipeline Architecture

Typical production‑grade pipeline:

Business DB → CDC tool → Message queue → Stream processing engine → Target storage

CDC tool : Captures changes and emits standardized events.

Message queue : Buffers data, smooths spikes, and decouples systems.

Stream processing engine : Performs cleansing, transformation, joins, and aggregations.

Target storage : Persists data in warehouses, lakes, or search indexes.

Code example

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