Introducing Oracle Autonomous AI Lakehouse Open Multicloud Data Platform
summary
Oracle unveils an enterprise Lakehouse that combines Apache Iceberg openness with Exadata performance, claiming plug‑and‑play SQL across clouds via a catalog of catalogs, serverless scale for petabyte scans with Data Lake Accelerator, and persistent Exadata caching that shows up to 5x TPC‑DS improvements in internal tests. Demoed features include mounting AWS Glue/Databricks catalogs, built‑in Select AI agents for end‑to‑end data science, and a customer running 8 million records every 30 minutes across 48 pipelines.
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chapters (12)
- 0:00Introducing Oracle Autonomous AI Lakehouse & Open Data Vision
- 3:33Customer Story: Mars Veterinary Health—Real-Time Enterprise Analytics at Scale
- 8:16Why Oracle AI Lakehouse: Performance, Built-In Tools, Expansion Plans
- 12:28Data Access Everywhere: Catalog of Catalogs & Multicloud Interoperability
- 16:04Demo: Unified Multicloud Data Access, Analysis & AI Workflows
- 19:02High Performance Features: Data Lake Accelerator and Exadata Caching
- 22:02Unlocking Full Oracle AI/Analytics on Iceberg Tables
- 26:19Open, Plug-and-Play AI on Any Data (Including Mission-Critical)
- 28:28Connecting Iceberg, Core Systems, and Operational AI
- 29:38The Three Pillars: No Lock-In, No Compromise, AI-Powered Operations
- 30:47Oracle AI Data Platform: Open, Multi-Cloud, Unified Stack
- 31:54Closing & Vision for Autonomous AI Lakehouse
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