Change data capture specialists for your dedicated team.

Where Change data capture fits in a long-term product team.

Reading the transaction log is what makes CDC different from a scheduled extract. There is no query load on the source, deletes are captured directly, and latency drops from hours to seconds. For keeping an analytical store current without affecting the transactional one, it is the correct mechanism.

It brings distributed-systems problems with it. Ordering has to be preserved for the result to be correct, the initial snapshot has to join the ongoing stream without gaps or duplicates, and a source schema change has to flow through without breaking the pipeline. Each is solvable and none is automatic.

What your dedicated team does with Change data capture.

CDC failures are quiet. A stopped connector does not raise an error in the application; it simply means the downstream data stops advancing, and the first symptom is usually someone noticing a number looks old.

Monitoring replication lag as a first-class metric is what makes it dependable, and that operational ownership sits with the assigned data capacity in a dedicated data engineering team.

Common Change data capture use cases.

• Analytics current without loading the source Log-based replication, so reporting freshness does not cost transactional performance.
• Deletes captured properly Deletions propagated directly, which query-based extracts cannot do.
• Lag monitored as a real metric Replication delay alerted on, because a stalled stream fails silently.

Adding Change data capture skills to your team.

CDC work is assigned inside data engineering, with lag monitoring and schema-change handling designed before the pipeline is depended on.

Change data capture specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.