There was a moment when Databricks announced support for temporary tables, and a certain segment of the data world collectively blinked and did a double-take. If you came up through the ranks of SQL Server, Oracle, or any traditional warehouse, your first reaction may have been somewhere between nostalgia and disbelief. “Wait… we’re celebrating temp tables now?” They’ve been around and used for literally decades. Did Databricks just steal an old idea??
Because while there may be nothing new under the sun, context changes everything. And in the world of distributed data platforms, abstractions that reduce friction matter more than novelty.
Temporary tables in Databricks Spark SQL aren’t a revolutionary technical breakthrough. They don’t redefine distributed computing. and they don’t introduce a new storage paradigm. What they do offer is something much more practical: a familiar, structured way for SQL-heavy teams to express intermediate logic in complex pipelines — without cluttering their lakehouse with permanent staging artifacts.
This is key. A new way for Spark SQL data users and pipelines to conceptualize, logically design, and think about pipelines.
