Reinventing Amazon Redshift

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" summary

Covers how Amazon Redshift has been redesigned to improve performance and scalability by changing its core architecture and execution engine. Describes specific technologies and techniques used in the redesign (new storage, query processing, and resource management components) and explains how those components work together to address bottlenecks in the original Redshift system.

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" chapters (22)

1. 0:00Intro
2. 2:36Challenges of data analytics at scale
3. 3:12The Dark Data Problem
4. 4:04Amazon Redshift-most popular & fastest cloud data warehouse
5. 5:19The Amazon Redshift architecture - 2021
6. 7:32Executing a query in Amazon Redshift
7. 10:48Code generation, Prefetching, Vectorization
8. 14:12Continuous telemetry and benchmarking
9. 15:27Compilation-as-a-Service
10. 20:58Linear scalability to the P8 scale
11. 22:14Diverging speeds in hardware
12. 22:49AQUA - A push-down & scale-out computation layer
13. 24:23Redshift Managed Storage
14. 24:53Cross-AZ cluster recovery
15. 28:26Compute elasticity improvements
16. 34:08Detecting Errors
17. 34:29Detecting Gray Errors
18. 37:32Automatic Table Optimization
19. 39:04Automated workload management
20. 46:13Ingesting and Querying Semistructured Data
21. 47:44Amazon Redshift Spectrum
22. 49:00The evolution of Amazon Redshift