Oracle Advances Autonomous AI Lakehouse with Business Semantic Layer and Expanded Support for Iceberg Data

Oracle AI World, LAS VEGAS-Oct 26, 2026

Oracle today announced new capabilities for Oracle Autonomous AI Lakehouse that help organizations give users and AI agents business context, perform multidimensional simulations, and strengthen data governance across the enterprise data estate. Building on Autonomous AI Lakehouse's open, high-performance foundation, the latest updates let customers work with real-time operational data alongside data in Apache Iceberg tables, while preserving business context, applying unified data governance, and avoiding unnecessary data movement.

"Giving AI access to enterprise data is not enough. To make informed decisions and take trusted action, AI agents need to understand the business context behind that data and operate within an organization's rules and policies," said Çetin Özbütün, executive vice president, Autonomous AI Database Technologies, Oracle. "By bringing business semantics and multidimensional modeling together with open data access and unified data governance, the latest updates to Autonomous AI Lakehouse help organizations give AI agents the context and controls to interpret enterprise data, apply business rules, and support trusted actions."

Liberty Energy, a North American energy services company serving oil and natural gas producers, is using Oracle Autonomous AI Lakehouse to unify financial, operational, supply chain, and workforce data across its multicloud environment. This gives Liberty a unified data foundation for AI, analytics, and business simulations.

"Oracle Autonomous AI Lakehouse gives decision-makers real-time visibility into field revenue, inventory usage, costs, and profitability," said Rhett Rowan, vice president of IT, Liberty Energy. "It seamlessly brings together Apache Iceberg tables with data from Amazon S3, Amazon databases, Microsoft SQL Server on Azure, MongoDB, and PostgreSQL, making it easy to add new data sources to power AI, analytics, and business simulations."

Autonomous AI Lakehouse provides an open, AI-optimized lakehouse with access to live enterprise data wherever it resides. It is available in Oracle Cloud Infrastructure (OCI), Amazon Web Services (AWS), Microsoft Azure, Google Cloud, Oracle Exadata Cloud@Customer, and OCI Dedicated Region. New and expanded Autonomous AI Lakehouse capabilities include:

• AI Data Catalog: Provides customers a holistic view of their enterprise data by allowing them to mount external catalogs, Oracle and non-Oracle databases, Iceberg tables, applications and data in the object store without duplicating data. These data sources can be queried live via the AI Data Catalog using the full power of the Oracle engine. The catalog's agentic capabilities can continuously enrich metadata with business semantics, lineage, workload patterns, relationships, and retrieval signals to improve the performance, quality, and cost of discovery, AI retrieval, natural-language analytics, and agentic workflows across lakehouse data. These capabilities will be available within the next 12 months.
• Business Semantic Layer: Helps users and AI agents gain consistent business context to accurately query, reason over, and derive insights from enterprise data. It can transform raw data into business-aware information using catalog annotations, imported Oracle AI Database SQL annotations, and AI-suggested analytic views. Reusable analytic views capture dimensions, measures, metrics, hierarchies, calculations, and KPIs to improve the accuracy of Select AI, NL2SQL, AI retrieval, and analytic insights. These capabilities will be available within the next 12 months.
• Expanded Support for Iceberg Data: Helps customers create, populate, update, and delete Iceberg tables using familiar SQL, providing a consistent analytics model across Oracle and Iceberg data. Organizations can then use Iceberg tables as a foundation for AI-powered analytics and agent workflows and create reusable Iceberg data products with time-travel capabilities for auditability and recovery. Enhancements to native Apache Iceberg support improve performance and expand functionality for customers accessing Iceberg tables through external catalogs or Oracle AI Data Catalog.
• Data Studio: Enables teams to discover, access, prepare, analyze, and manage lakehouse data, streamline integration, build transformation pipelines, and uncover AI-powered insights. The unified, AI-assisted workspace can be accessed through Oracle Data Studio without first provisioning a database, helping simplify how teams work with data across analytics, AI, data engineering, operations, and business simulations. Teams can connect to more than 100 data sources and work with data using natural language, SQL, or Python.
• Centralized Security and Controls: Helps ensure that both users and AI agents access only authorized information while applying consistent data governance across cloud and on-premises environments. A single control plane in the AI Data Catalog enables customers to apply role-based, object-level access controls across Oracle, non-Oracle, and Iceberg data.
• Data Lake Accelerator (DLA): Provides customers with improved performance and scalability by helping accelerate large-scale queries on Iceberg data in object storage. In benchmark tests on 100 TB TPC-DS data sets, DLA enhancements delivered up to 3X faster query execution. The maximum DLA cluster size has increased 3X, enabling greater scale and faster query performance.
• Business Simulation Engine: Helps customers make more informed planning and forecasting decisions by supporting multidimensional simulations and what-if analyses on lakehouse data. Built by integrating Oracle Essbase natively inside Autonomous AI Lakehouse, it enables teams to forecast outcomes and assess the impact of changing business assumptions, with support for write-back, dynamic hierarchy rollups, advanced calculations, and allocations. Customers can ask questions in natural language, generate simulation reports, and perform Excel-based analysis on data in Oracle databases or on files in the object store.

"We believe the next phase of enterprise AI will depend on connecting data access with the business context needed to make useful decisions," said Dave Vellante, co-founder and chief analyst, theCUBE. "Oracle's Autonomous AI Lakehouse takes a significant step in that direction by bringing together governed access to Oracle and non-Oracle data, shared business definitions, AI-assisted analytics and multidimensional what-if planning. This combination can help enterprises ground agent workflows in the metrics and relationships that matter to their business and evaluate options using a more consistent analytical foundation. Realizing that value requires accurate business definitions and clear controls over execution. Oracle is strengthening the lakehouse as a practical foundation for planning, informed decisions and agentic workflows."

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