How smava boosts business agility and innovation w/Amazon Redshift
summary
smava describes migrating from one large cluster to a mix of purpose-built warehouses using data sharing and Redshift serverless, ingesting into S3 then Redshift with DBT, Athena and Glue for transforms, and adding zero-ETL integration from Aurora MySQL. Platform serves ~60k queries/day on double‑digit TB with 53% annual growth, a 25‑person data team, and machine‑learning scoring that shortens insight cycles from days to hours. Outcome: up to 5x query performance and roughly 50% cost reduction.
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chapters (20)
- 0:00Introduction and welcome
- 1:06Generative AI and data foundation
- 3:17Amazon Redshift overview
- 5:27Price performance and features
- 8:43Zero ETL integration
- 11:16Empowering different personas
- 13:48Customer needs and innovations
- 14:09Smava’s mission and data platform
- 20:38Evolution of data architecture
- 22:27Modernizing with data sharing
- 25:18Business impact and use cases
- 29:54Future plans and generative AI
- 30:15Demo introduction
- 31:19Business use case setup
- 33:28Zero ETL configuration demo
- 36:58Real-time data replication demo
- 39:28Data sharing between warehouses
- 41:59Amazon Q generative SQL demo
- 46:15Visualization with QuickSight
- 47:16Resources and closing
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