Oracle Announces Business Semantic Layer and AI Data Catalog for Autonomous AI Lakehouse

At Oracle AI World 2026 held in Las Vegas on October 27, Oracle announced significant enhancements to its Autonomous AI Lakehouse platform. The core of these updates is the introduction of a 'Business Semantic Layer' and an 'AI Data Catalog', aimed at enabling AI agents to better understand the context and meaning of enterprise data.

The Business Semantic Layer allows organizations to define metrics, calculation methods, hierarchies, and Key Performance Indicators (KPIs) directly within the data platform. This enables AI agents to move beyond simple numerical retrieval to generating analytical results relevant to specific business tasks. By leveraging existing database annotations and AI-suggested analytical views, this layer adds business context to raw data, improving the accuracy of natural language queries and AI-driven analysis.

The new AI Data Catalog connects data stored in Oracle databases, third-party databases, external catalogs, and Apache Iceberg tables without requiring replication or movement. This provides a unified view of enterprise data assets. Both the Business Semantic Layer and the AI Data Catalog are expected to be available within the next 12 months.

Oracle also expanded support for Apache Iceberg, allowing users to create, add, modify, and delete Iceberg tables using SQL. This includes consistent analytical models across Oracle and Iceberg data, along with change history management and recovery capabilities.

In terms of performance, Oracle reported that its 'Data Studio' now supports connecting over 100 data sources for analysis using natural language, SQL, and Python. Additionally, the 'Data Lake Accelerator (DLA)' achieved up to 3 times faster query execution in internal benchmarks involving 100 terabytes of data. These performance figures are self-reported by Oracle.