Turn Data Into Insights
summary
AWS outlines a practical lake house built on Amazon S3 with Lake Formation for governance, AWS Glue (Studio, DataBrew) for ETL, Kinesis/MSK for streaming, and purpose-built analytics like EMR and Redshift (RA3, AQUA). It claims governed tables add ACID-style transactions, automatic compaction and time travel for reproducible ML, and cites customers scaling from 30 ventilators/week to 600/day and Moderna using Redshift as a single source of truth. Also highlights self-service transforms via AWS Glue Elastic Views and natural-language answers with QuickSight Q.
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chapters (20)
- 0:00Intro
- 2:45Challenges to turn data to insights
- 4:08Traditional data platforms didn't scale, so you built a data lake
- 4:30If users don't trust the data, they won't use it
- 5:15Need to serve many types of users
- 8:06Accelerate adoption of lake house architecture
- 10:13Amazon S3 is the most popular choice for data lakes
- 11:00Governed tables - A better way
- 12:09Introducing governed tables
- 15:11Central catalog - Audit
- 15:46Fine-grained access control
- 16:42Enables flexible architecture design patterns
- 19:16AWS Glue Studio
- 19:35AWS Glue Elastic Views
- 20:39AWS Glue DataBrew
- 22:26Amazon EMR Studio integrated development environment
- 22:53Amazon EMR flexible deployment options
- 23:53Amazon Redshift innovates to meet your needs
- 26:38Have a strategy and need help executing it?
- 27:28Analytics sessions at AWS Global Summit Online Spring 2021
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