Customers are pulling data back into the Oracle database as agents demand real-time access
A shared semantic layer is fast becoming the dividing line between lakehouses built for reporting and those built for artificial intelligence. As AI agents join human analysts in querying corporate data, enterprises need platforms that make sense of information scattered across clouds, formats and rival systems. Oracle Corp. has spent the past year rebuilding its […] The post Customers are…
As enterprises increasingly rely on AI agents for data queries, Oracle is modernizing its data stack to meet the growing demand for real-time access to information scattered across clouds, formats, and rival systems. Nipun Agarwal, senior vice president of software engineering at Oracle, explained that lakehouses have evolved from a platform for analytics to a decision backbone for AI.
Oracle's Autonomous AI Lakehouse offers a shared semantic layer that enables fast, real-time access to data across platforms, eliminating the need to copy data. The company's approach combines Oracle's catalog with other catalogs like Databricks' Unity Catalog and supports Postgres and MySQL databases. This allows for querying data in place, reducing latency and improving efficiency.
Oracle also announced enhanced business semantic layer capabilities and broader Apache Iceberg support during an event, with the system generating table and column descriptions that apply to non-Oracle sources. To further optimize query performance, Oracle's Lake Cache stores the most-used Iceberg tables in Exadata's flash cache.
Real-time access is particularly crucial as AI agents are the primary consumers of data, requiring on-demand access rather than scheduled refreshes, which may fall short in meeting these demands. Oracle does not view its offering as competing with other lakehouses, emphasizing that enterprises will likely use multiple data sources and lakehouses, showcasing the flexibility and adaptability of its platform.
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