Streams describe what changed
A stream gives a consumer a change-tracking offset over a table. It is best understood as pipeline state, not as a permanent event archive. The downstream transaction advances that state when changes are consumed.
That model makes small incremental transformations possible without repeatedly scanning the full source table.
Tasks make the dependency graph explicit
Use one task for one clear transformation boundary. A root task can run on a schedule or condition, while child tasks express dependencies between stages.
- Keep transformation SQL idempotent where practical.
- Separate ingestion, normalization, and serving-table updates.
- Log batch identifiers and row counts at each boundary.
Design recovery before scheduling
A pipeline is dependable when an operator can answer what ran, what was consumed, and how to retry safely. Define those answers before choosing a frequent schedule.
For critical data, keep a durable raw source outside the stream so a failed or incorrect transformation can be replayed from a known boundary.