Compare Tableau live vs extract and filters
Overview
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.
Scenario
You are building an interactive dashboard over a 100M-row fact table. Compare Tableau connection options and performance behaviors for this scale, and map the concepts to Amazon QuickSight for a potential migration.
Part A — Tableau: Live vs Extract for 100M rows
Discuss the trade-offs between Live and Extract connections, specifically addressing:
- Refresh cadence and data freshness.
- Query pushdown to the database vs Tableau Hyper.
- Extract size and storage considerations.
- Incremental refresh design (late-arriving data, updates vs inserts).
- Row-level security (RLS) enforcement and data leakage risks.
- When each option is preferable for a 100M-row fact table.
Part B — Tableau Order of Operations and Effects
Explain Tableau's order of operations and how it affects the following:
- Data source filters
- Context filters
- Dimension filters
- Measure filters
- TOP/N filters
- Table calculations
- LOD expressions: FIXED, INCLUDE, EXCLUDE
Part C — Tableau Performance Tuning Tactics
Explain how to tune performance for this dashboard by:
- Selecting high-selectivity dimensions for context filters.
- Using extract filters and pre-aggregation.
- Avoiding expensive table calculations when possible.
- Choosing denormalization vs joins/relationships appropriately.
Part D — Mapping to Amazon QuickSight
Map the above ideas to Amazon QuickSight analogs and note one migration pitfall:
- SPICE vs Direct Query (including refresh/incremental, pushdown, size).
- Row-level security approaches.
- Filters and calculation analogs.
- One common pitfall when migrating a Tableau workbook to QuickSight.
Solution
Login required