Temporary Tables in Databricks SQL | Do You Actually Need Them? скачать в хорошем качестве

Temporary Tables in Databricks SQL | Do You Actually Need Them?

Are Temporary Tables in Databricks actually a big deal… or just a nostalgic nod to the SQL Server days? In this video, we break down Temporary Tables in Databricks Spark SQL — what they are, how they work, and why they matter for modern data pipelines. If you come from SQL Server, Oracle, or legacy warehouse systems, this feature probably made you do a double take. Temp tables? In Databricks? In 2026? Yep. We’ll cover: What a Databricks SQL Temporary Table actually is How it differs from classic Delta tables Why it’s session-scoped and auto-cleaned When you should use it (and when you probably shouldn’t) The performance and migration implications Potential cost and governance risks Databricks defines them as: “Session-scoped, physical Delta tables stored in Unity Catalog, automatically cleaned up when the session ends.” Translation? They behave like real Delta tables — INSERT, MERGE, UPDATE, DELETE — but only live for the duration of your Spark SQL session. For teams migrating from legacy warehouses, this lowers the conceptual barrier dramatically. For platform teams… well… it introduces some interesting governance and cost considerations. With great power comes great responsibility. If you're building SQL-based pipelines in Databricks — or leading a migration from traditional warehouse systems — this is a feature you should understand before your analysts start spinning up terabyte-sized temp tables. 🔔 Subscribe for more deep dives into Databricks, Spark, Delta Lake, and modern data engineering. 📬 Follow Data Engineering Central for practical, opinionated takes on the data world. #Databricks #SparkSQL #DeltaLake #DataEngineering #Lakehouse #SQL #UnityCatalog