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Compare Tableau live vs extract and filters

AmazonData Scientist · Onsite
HardData ScientistOnsiteAnalytics & Experimentation

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This question evaluates competency in BI data architecture and large-scale dashboard performance, covering extract vs live connections, refresh and incremental strategies, query pushdown versus in-tool processing, row-level security, filter order-of-operations, performance tuning, and platform migration mapping within the Analytics & Experimentation domain for a Data Scientist role. It is commonly asked to assess trade-off reasoning across data freshness, storage, query performance and security for a 100M-row fact table, combining practical application-level decisions (implementation and tuning) with conceptual understanding (filter ordering, calculations, and migration pitfalls) when moving between BI platforms.

In Tableau, compare Live vs Extract connections for a dashboard over a 100M-row fact table. Discuss refresh cadence, query pushdown, extract size, incremental refresh, row-level security, and when each is preferable. Explain the order of operations and how it affects:

  • Data source filters, context filters, dimension/measure filters, TOP/N, table calculations, and FIXED/INCLUDE/EXCLUDE LODs.
  • Performance tuning: selecting dimensions for context, using extract filters, avoiding expensive table calcs, and denormalizing vs joins/relationships. Map these ideas to Amazon QuickSight analogs (SPICE vs direct query, row-level security, filters) and note one pitfall when migrating a Tableau workbook to QuickSight.