DBX-DEP study notes

• Choosing Databricks compute for a workload

  Which compute fits each kind of workload, and the constraint that forces a move away from serverless.

• Where each Databricks environment takes its libraries

  How serverless, pipelines, classic jobs and bundles each get their Python libraries.

• Structured Streaming checkpoints and restart compatibility

  What a checkpoint holds and which query changes survive a restart.

• Streaming table or materialized view?

  How latency, cost and refresh decide between the two pipeline dataset types.

• Lakeflow Pipelines expectations: warn, drop, fail and quarantine

  What each expectation action does to a bad record, and how quarantine is built.

• Liquid Clustering against partitioning and ZORDER

  When each table layout fits, and what existing tables need.

• Unity Catalog privilege inheritance

  How grants flow down from catalogs and schemas, and where they stop.

• Fulfilling a right-to-erasure request on Delta Lake

  The steps that really remove a person's data, and why they can't run back to back.

About the book · DBX-DEP terms in the glossary