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BigData / Tableau Interview Questions

What is the Tableau Hyper engine?

Hyper is Tableau's in-memory, columnar data engine that powers extracts, replacing the older .tde format with a faster, more scalable architecture.

  • Columnar storage lets analytical queries scan only the columns actually needed, rather than reading whole rows, which is a major speed advantage for the kind of aggregate queries Tableau typically runs.
  • Hyper supports much larger extracts than the legacy .tde format could practically handle, and generally builds/refreshes extracts faster too.
  • It uses dynamic code generation for query execution, compiling parts of a query into optimized machine code on the fly rather than relying purely on a generic interpreter.
Data source with an extract -> stored as .hyper
-> queried through Hyper's columnar engine at view-render time

Hyper is largely invisible to end users day-to-day — you don't interact with it directly — but it's the reason extracts became dramatically faster and able to handle much larger datasets after Tableau adopted it, replacing the older TDE-based extract engine used in earlier versions.

What storage approach does Hyper use that speeds up analytical queries?
What older extract format did Hyper replace?

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