Modernize Your Data Warehouse
summary
Covers migrating on-premises SQL-based data warehouses to AWS cloud using a lake house architecture that blends data warehouse and data lake capabilities. Describes leveraging existing SQL skills for an easy migration to AWS services to improve performance, add analytics functionality, and reduce costs.
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chapters (21)
- 0:00Intro
- 0:30Data warehousing trends
- 2:15Why Amazon Redshift
- 9:08Features delivered to meet customer needs
- 10:09Amazon Redshift architecture
- 12:15Amazon Redshift evolving architecture
- 12:32AQUA: Advanced Query Accelerator (preview)
- 13:29Terminology and concepts: Node types
- 14:17Terminology and concepts: Columnar Amazon Redshift uses a columnar architecture for storing data on disk
- 14:45Example: Columnar architecture
- 15:19Terminology and concepts: Compression Goals
- 16:08Compression example
- 17:20Terminology and concepts: Data sorting
- 18:12Example: Zone maps and sorting
- 19:01Terminology and concepts: Slices
- 20:26Data distribution example
- 20:42Data distribution, KEY example #1
- 21:39Data distribution summary DISTSTYLE KEY
- 22:41AWS migration tooling
- 25:16Legacy data warehouse migration tips When moving from legacy row-based data warehouses
- 26:46AWS Big Data Blog - Amazon Redshift Amazon Redshift Engineering's Advanced Table Design Playbook
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