While working with Connect CDC, I ran into something that changed how I look at capture. CDC itself is not the full story. Its correctness depends heavily on how the database records changes.
What CDC really depends on
CDC does not invent change data. It reads what the database logs.
That means the quality of replication depends on the quality of logging.
A good example is IBM i / DB2 journaling. For CDC to work correctly, tables need to be journaled with BOTH images enabled. That means both before and after values are recorded.
What happens when logging changes
If journaling is changed to AFTER-only:
- Update operations no longer have before images
- CDC loses part of the change information
- Capture can become incomplete or inconsistent
At that point, the issue is not in CDC configuration. It is in how the database is recording history.
Why this matters more than it seems
With proper logging:
- CDC has full visibility into changes
- Updates and deletes can be handled accurately
- Downstream systems stay consistent
Without it, even a perfectly configured CDC pipeline can produce unexpected results.
What this unlocked for me
Once I understood this, a lot of behavior started to make sense.
CDC is not just moving data.
It is replaying what the database recorded.
That means:
- Good logging → reliable replication
- Incomplete logging → gaps in behavior
Where this shows up in practice
This becomes critical when using:
- Before-value capture
- Auditing or history tracking
- Complex transformations
- High integrity systems
All of these rely on having complete change information from the source.
The takeaway
CDC is not just configured in the replication tool. It is also shaped by database logging settings.
It behaves like a log-powered pipeline, not just a data mover.
Once I started looking at it that way, it became much easier to debug issues and design more reliable models.
Have you ever traced a CDC issue back to database logging settings rather than the replication model itself?
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Adhitya Maya
*Precisely Software Inc.
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