Data @Scale Seattle- Pawel Terlecki
summary
Tableau reduces dashboard latency by batching and optimizing worksheet requests into multi-query batching and running many requests in parallel, cutting a 25s interaction and some 2 min queries down to 5–10s in favorable backends. Version 9 opens a 16x connection multiplier, exposing connector limits like Redshift cursor errors and Vertica resource pools. A local Data Engine columnstore up to 1TB provides vectorized execution, compression-aware optimization, parallel plans and temporary-table materialization to speed common visualization workloads.
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chapters (18)
- 0:00Intro
- 0:37What is Tableau
- 1:03Polaris
- 4:22Data Exploration
- 7:46Query Execution
- 9:18Running in Parallel
- 11:03Cross Square
- 11:31Parallel
- 12:27Peril
- 15:44Query Quality
- 17:27Caching
- 18:36Data Engine
- 19:50Architecture
- 20:50Strategy
- 22:33Tableau Server
- 23:17Data Server
- 25:45Tableau Public
- 26:49Questions
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