Moving analytics from archive to active business intelligence
summary
A new database announcement converges transactional and analytical workloads so one cluster handles high-read, high-write and in-place machine learning with integrated streams and global change data capture, claiming to break the usual trade-offs between consistency, performance and scale — the company notes there are roughly 400 database tools today that this aims to replace. It runs edge, cloud or on-prem, supports multi-tenant logical carving, and pushes real-time AI use cases like personalized web context, medical genomics and security forensics.
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chapters (6)
- 0:00Introduction
- 1:38Realtime AI applications
- 2:36Scale issues
- 3:25Machine learning
- 4:53Data
- 7:13Data for humans
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