Skip the custom app and Lakebase sync. On classic compute, compute policies with pinned libraries, scoped per team or persona, cover this at the platform level, and they don't depend on serverless.
Why not a single init script
Databricks recommends ...
Short version: your Table1 is really a materialized view (batch read, fully recomputed each update), and Table2 needs to be a streaming table fed by an incremental source, not by Table1. The fix is to change how you ingest the CSV for the history ta...
The cleanest fix I know is to stop treating this as two overlapping SCD2 loads. Make the two flows idempotent against each other by giving them a shared ordering domain, so a full refresh can replay both without producing duplicates.
Why you get dupl...
Short version: the SQL is logically fine. The cost comes from scanning the 1.4B-row fact table twice and pushing every fact row through the customer left join before any aggregation happens. Aggregate first, join the small result afterwards, and t...
You're hitting that error because the create-endpoint flow is trying to build a provisioned throughput endpoint, and that isn't how you consume this model. system.ai.databricks-gemini-3-8-flash is a Databricks-hosted foundation model, and for propri...
