Key points
- Oracle announced a business semantic layer and broader Apache Iceberg support at its October event.
- The system can mount external catalogs, including Databricks Unity Catalog, Postgres and MySQL, to query data in place.
- Oracle Lake Cache moves frequently used Iceberg tables into Exadata flash cache to speed up queries.
Oracle Corp. has updated its data stack to support artificial intelligence agents that require immediate access to corporate information, according to a report by SiliconANGLE.reported The company introduced a shared semantic layer designed to sit across existing enterprise data estates rather than replacing them. Nipun Agarwal, senior vice president of software engineering for Oracle Database, described the shift during an exclusive broadcast on theCUBE at Oracle AI World. He stated that lakehouses are evolving from analytics platforms into the decision backbone for AI.
Evidence: reported
Customers are pulling data back into the Oracle database as agents demand real-time accessaifleet-siliconangle
The figures
The source material does not provide specific quantitative metrics, such as performance benchmarks, adoption rates or pricing details. It references the past year of rebuilding the data stack and the October event where announcements were made, but no numerical data regarding query speeds or customer counts are cited.
Behind the figures
The insights stem from an interview with Nipun Agarwal conducted by theCUBE’s Dave Vellante. The discussion focused on Oracle’s strategy to integrate a semantic layer with real-time data access capabilities. The reporting outlet, SiliconANGLE, published the account of this conversation, noting that Oracle pitches its Autonomous AI Lakehouse as an open layer. No independent measurement methodology or third-party verification of performance claims is included in the material.
What builders can do with it
Why it matters
- AI agents require real-time data access that scheduled refreshes of copied data cannot provide.
- A shared semantic layer distinguishes lakehouses designed for reporting from those built for artificial intelligence.
- Oracle’s approach allows querying data in place across multiple sources rather than replacing existing systems.
- Lakehouses are evolving from analytics platforms into the decision backbone for AI.
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- Oracle announced a business semantic layer and broader Apache Iceberg support at its October event.
- The system can mount external catalogs, including Databricks Unity Catalog, Postgres and MySQL, to query data in place.
- Oracle Lake Cache moves frequently used Iceberg tables into Exadata flash cache to speed up queries.