Driving business with autonomous analytics and data democratization
summary
Demonstrates a concrete pipeline: raw CSV in S3 is profiled and cleaned with AWS Glue DataBrew (fills missing regions, removes duplicates, drops outliers) using 300+ built-in functions and saved recipes, output back to S3, then visualized in Amazon QuickSight with anomaly detection (Random Cut Forest) and forecasts; QuickSight Q (launched Oct 2021) indexes unique strings and answers natural‑language questions in seconds, mapping synonyms and returning charts — all no-code/low-code and serverless.
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chapters (10)
- 0:00Introduction and agenda
- 1:05Challenges in data prep and analytics
- 3:17DataBrew visual data preparation
- 7:56QuickSight BI service overview
- 10:49QuickSight Q natural language queries
- 13:22Demo use case setup
- 15:14DataBrew data profiling and cleaning
- 34:14QuickSight dashboard creation
- 51:09QuickSight Q topic setup and demo
- 1:09:30Resources and closing
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