Database Change Data Capture Specialist

Change Data Capture is one of the most powerful mechanisms available for database auditing — it records every INSERT, UPDATE, and DELETE at the row level directly from the transaction log, with minimal application impact and near-complete fidelity. Yet configuring CDC correctly for audit purposes, as opposed to data integration purposes, requires a specific set of design decisions around capture scope, log retention, data masking for sensitive columns, and downstream delivery that most documentation does not address. The Database Change Data Capture Specialist is an AI assistant built for this precise technical challenge.

This assistant helps DBAs and data engineers implement and configure CDC pipelines specifically for audit, compliance, and data lineage tracking purposes. It covers native CDC mechanisms — SQL Server CDC and Change Tracking, Oracle LogMiner and GoldenGate, PostgreSQL logical replication and pglogical, MySQL binlog-based CDC — as well as CDC tools and platforms including Debezium, AWS DMS, and Azure Data Factory change tracking.

Users describe their database environment, the tables and columns they need to track, the downstream systems where change records should be delivered (audit databases, SIEM platforms, data lakes, compliance repositories), and any masking or filtering requirements for sensitive column data. The assistant then designs the CDC configuration, produces implementation scripts and configuration files, and documents the resulting audit data schema.

A key focus is the difference between CDC for data integration (which prioritizes throughput and freshness) and CDC for audit (which prioritizes completeness, before-and-after value capture, actor attribution, and tamper-resistant downstream storage). The assistant helps users configure CDC for audit-quality output: capturing the old and new values of changed rows, preserving the database user identity responsible for each change, and ensuring no changes are lost due to log truncation or capture gaps.

Ideal users include DBAs implementing row-level change audit trails for regulatory compliance, data engineers building audit pipelines for sensitive data environments, and security engineers extending database activity monitoring to include DML-level change tracking.