Oracle Autonomous AI Lakehouse Enables Open, Interoperable Data Access Across Multi-Platform, Multicloud Environments

• Combines Oracle Autonomous AI Database with vendor-independent Apache Iceberg enabling customers to run AI and analytics securely on all their data
• Now available on OCI, AWS, Azure, Google Cloud, and Exadata Cloud@Customer

Oracle announced Oracle Autonomous AI Lakehouse, an open, interoperable data platform. It combines Oracle Autonomous AI Database with the popular Apache Iceberg standard to avoid functionality tradeoffs, break down analytic silos, and accelerate how teams build AI and analytics solutions. Oracle also introduced Autonomous AI Database Catalog—a “catalog of catalogs” that unifies enterprise data and metadata from other catalogs and platforms—to simplify data discovery and access across multiple data platforms and clouds.

“With Oracle Autonomous AI Lakehouse, we are offering customers a lakehouse platform without compromise by combining the highly trusted and industry-hardened Autonomous AI Database—executing in excess of 48 billion queries per hour—with the openness of Apache Iceberg,” said Çetin Özbütün, executive vice president, Autonomous Database Technologies, Oracle. “Not only is Oracle breaking down the data silos between analytic systems with Iceberg, we are also enabling customers to access Iceberg data in any platform—operational or analytic—in the cloud or on-premises.”

With native support for Apache Iceberg, Autonomous AI Lakehouse provides simple, high-performance access to any Iceberg table by integrating with catalogs including Databricks Unity, AWS Glue, and Snowflake Polaris. Powerful capabilities such as Select AI—which provides natural language-to-SQL transformation and an agentic AI framework—along with JSON-Relational Duality, Property Graph Analytics, and AI Vector Search are available for processing data on Iceberg tables—all underpinned by the performance, availability, and security of Exadata but with vendor independence and while avoiding the operational drag of data movement. Autonomous AI Lakehouse is available on OCI, AWS, Microsoft Azure, Google Cloud, and Exadata Cloud@Customer.

“Customers consistently tell us they want to use the tools they already have on top of their data,” said Stephen Orban, senior vice president, Product Ecosystem & Partnerships, Databricks. “Databricks is committed to open and interoperable data access for analytics and AI, and Unity Catalog helps make that possible by providing a unified governance layer for formats like Apache Iceberg. We welcome Oracle Autonomous AI Lakehouse’s integration with Unity Catalog, giving joint customers seamless access to their data and the flexibility to use Oracle and Databricks together.”

In addition, Oracle unveiled Data Lake Accelerator, which speeds large-scale queries across Iceberg tables by dynamically scaling network and compute capacity to Autonomous AI Database and bills only for the resources used.

“As part of Oracle’s Limited Availability program for Data Lake Accelerator, SKY had the opportunity to test this new capability and was impressed by its performance,” said Rosiane Porto, Data Services Team - Big Data, SKY Brazil. “Data Lake Accelerator significantly improved query speeds on external data stored in object stores, enabling us to analyze large datasets faster and more efficiently—without moving data or changing our workflows. The ability to dynamically scale compute resources on demand gave us the flexibility to handle complex queries when needed, while keeping costs under control. We are excited about the potential of Data Lake Accelerator to simplify and accelerate external data processing for our business.”

Key features of Oracle Autonomous AI Lakehouse include:

• Autonomous AI Database Catalog: Provides customers with a unified view of enterprise data across multiple cloud and on-premises data assets. With simple connectivity to databases, data lakes, shares, and existing data catalogs, Autonomous AI Database Catalog enables data discovery, search, access, and metadata enrichment to extend and improve collaborative data science, data engineering, and analysis—as well as AI processes on data.
• Autonomous AI Database Data Lake Accelerator: Enables customers to accelerate large-scale queries across Iceberg tables by automatically allocating additional network and compute resources, as needed, based on query demands.
• Select AI Agent: Provides customers with a simple, secure, and scalable in-database framework to build, deploy and manage AI agents within Oracle Autonomous AI Database. It supports custom and pre-built PL/SQL tools, external tools via REST, and MCP servers, enabling the automation of multi-step agentic workflows, accelerating innovation, and helping customers keep their data safe.
• Data Science Agent: Is expected to provide customers with a pre-built AI assistant within Autonomous AI Database. It helps data workers seamlessly search across data catalogs, prepare and explore data interactively, uncover key insights, and transform them into actionable results—all through natural language with zero code.
• Plug and Play SQL Access: Provides customers with a new simplified SQL syntax to query data via catalog connections. Customers can connect to Iceberg and other data catalogs including AWS, Databricks, Snowflake, and Apache Gravitino, then immediately query data with no data movement.
• Exadata Table Cache: Enables customers to improve query performance of frequently accessed Iceberg data by caching data within Oracle Exadata flash storage to deliver the performance of native Exadata tables.
• GoldenGate for Iceberg: Enables customers to stream data from operational and analytic data sources to any Iceberg target in real time. Customers can quickly and efficiently integrate data from SaaS applications and hundreds of data sources into Iceberg, and then analyze it using Autonomous AI Database.
• Table Hyperlink for Easy Data Sharing: Helps customers to securely share up-to-date data internally and externally, without the need to manage complex permissions, by generating a temporary hyperlink that provides direct but isolated access to a particular table or query result.

“The days of trade-offs between enterprise-grade scalability and open source flexibility are over,” said Ron Westfall, vice president, Practice Lead, Networking and Infrastructure, HyperFRAME Research. “Oracle’ s support for Apache Iceberg with Autonomous AI Lakehouse means organizations get cutting-edge AI, high-octane analytics, and secure, open access—all in one shot—on the hyperscaler cloud of their choice. By wrapping a ‘catalog of catalogs’ around Iceberg and its Autonomous AI Database, Oracle is making it radically simpler for teams to discover, secure, and leverage data everywhere. It’s a game-changer for breaking down barriers in today’s fragmented data landscape.”