Mastering Change Data Capture: Open‑Source Tools and How to Choose the Right One

What is CDC?

Change Data Capture (CDC) records every data modification—INSERT, UPDATE, DELETE—in a source system and streams those changes to downstream consumers for real‑time analytics, ETL, data synchronization, and related use cases.

Typical CDC use cases

Real‑time data warehousing

ETL pipelines

Read/write separation

Application data merging

One‑to‑many data distribution

Cross‑platform disaster‑recovery replication

Rolling upgrades of data platforms

Technical approaches to CDC

Table‑level timestamps or version columns Some applications add columns such as last_update , date_modified , or a deleted flag. Queries on these columns can identify changed rows, but the approach requires schema changes and can cause tables to grow indefinitely because deletions are logical.

Data‑diff comparison Full‑table snapshots are compared to detect changes. This method is CPU‑ and I/O‑intensive, captures only the final state within a window, and cannot emit intermediate change events.

Table‑level triggers INSERT/UPDATE/DELETE triggers write each change to a history table, enabling precise incremental extraction. Triggers add write‑path overhead and increase storage consumption.

Log‑based change extraction Parsing the database’s transaction log (binlog, redo log, WAL, etc.) provides low‑latency, non‑intrusive capture and works across heterogeneous databases.

Database‑specific log‑based CDC requirements

MySQL

CDC relies on the binary log (binlog). Required MySQL version is 5.7 or higher. Binlog formats:

Statement‑Based Replication (SBR)

Row‑Based Replication (RBR) – recommended for reliable change capture

Mixed‑Based Replication (MBR)

Note: Binary columns such as TINYBLOB, BLOB, MEDIUMBLOB, LONGBLOB are not captured by some CDC tools.

Oracle

Oracle CDC uses redo logs. Archive mode must be enabled and supplemental (additional) logs must be turned on to guarantee capture. Oracle 19c disables continuous_mine; Oracle GoldenGate is the recommended alternative for continuous log mining.

PostgreSQL

Logical decoding (available from PostgreSQL 10) reads WAL entries and streams changes via logical replication slots. Consumers can use pg_recvlogical to read the slots.

MongoDB

Change Streams (available from MongoDB 3.6) listen to the oplog and emit real‑time change events for collections, databases, or entire clusters. This feature requires a replica‑set deployment.

Popular open‑source CDC tools

Debezium

Debezium captures changes via Kafka Connect, turning each table’s changes into a dedicated Kafka topic. It supports exactly‑once or at‑least‑once delivery semantics. Project URL:

https://github.com/debezium/debezium

Canal

Canal, an Alibaba project, mimics a MySQL slave to pull binlog events. It supports MySQL 5.1‑8.0 and MariaDB. Project URL:

https://github.com/alibaba/canal

Maxwell

Maxwell reads MySQL binlog and emits JSON messages to Kafka, Kinesis, RabbitMQ, Redis, Google Cloud Pub/Sub, files, etc. It supports full‑table initialization and automatic GTID recovery after failover. Project URL:

https://github.com/zendesk/maxwell

Flink CDC

Flink CDC provides source connectors for MySQL, PostgreSQL, Oracle, MongoDB and others, enabling both snapshot and incremental change ingestion within Flink jobs. It leverages Flink’s parallelism, state backends, and ecosystem. Project URL:

https://github.com/ververica/flink-cdc-connectors

TapData

TapData is an open‑source real‑time data service platform that offers non‑intrusive CDC‑based data collection, automatic schema inference, unified streaming‑batch processing, and model publishing. Documentation:

https://tapdata.github.io/

Conclusion

Log‑based CDC has become the de‑facto method for real‑time data pipelines because it imposes minimal load on source systems and works across diverse databases. Selecting a tool depends on the target ecosystem (Kafka, Flink, etc.), required delivery guarantees, and operational constraints such as supported database versions.