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Accepted answer
Document the grain decision, most BI bugs turn out to be grain bugs.
This worked in dev on sample data but fails at full volume. Details:
Context: CDC from OLTP to lakehouse, Debezium vs DMS
Happy to share schema snippets or metrics if useful.
Accepted answer
Document the grain decision, most BI bugs turn out to be grain bugs.
Check whether AQE is disabled in your Spark conf, skew join handling helped us a lot here.
Start with the execution plan, numbers beat guesses.
Idempotent writes with merge keys saved us during backfills.
Tried that, partial improvement but lag still spikes on redeploy.
We saw the same issue, fixing the partition filter dropped runtime 60%.
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No cluster. No install. Just the tab.