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

Rhea-AI Summary

Oracle (NYSE: ORCL) introduced Autonomous AI Lakehouse on Oct 14, 2025, an open, interoperable data platform that combines Oracle Autonomous AI Database with native support for Apache Iceberg to enable AI and analytics across multi‑platform, multicloud environments.

Key highlights: available on OCI, AWS, Azure, Google Cloud, and Exadata Cloud@Customer; integrates with catalogs including Databricks Unity, AWS Glue, and Snowflake Polaris; claims the Autonomous AI Database executes in excess of 48 billion queries per hour. New features include Autonomous AI Database Catalog, Data Lake Accelerator, Select AI Agent, Data Science Agent, Exadata Table Cache, GoldenGate for Iceberg, and Table Hyperlink for sharing.

Positive

• Multi‑cloud availability on OCI, AWS, Azure, Google Cloud, Exadata Cloud@Customer
• Native Apache Iceberg support enabling vendor‑independent data access
• Autonomous AI Database executes in excess of 48 billion queries per hour
• Integration with Databricks Unity, AWS Glue, and Snowflake Polaris catalogs
• Data Lake Accelerator dynamically scales compute and bills only for used resources

Negative

• None.

Insights

Enterprise Data Architect

Oracle unveils an open, multi-cloud lakehouse combining Autonomous AI Database with Apache Iceberg to simplify cross-platform AI and analytics.

Oracle’s Autonomous AI Lakehouse pairs the company’s Autonomous AI Database with the vendor-independent Apache Iceberg format to enable direct, high-performance queries across multiple catalogs and clouds. The platform lists native integrations with catalogs including Databricks Unity, AWS Glue, and Snowflake Polaris, and it extends Exadata’s performance features such as Exadata Table Cache while offering new capabilities like Select AI, Data Lake Accelerator, GoldenGate for Iceberg, and an Autonomous AI Database Catalog that unifies metadata across systems.

The announcement emphasizes cross-cloud availability (OCI, AWS, Azure, Google Cloud, and Exadata Cloud@Customer) and third-party cooperation, including a quoted endorsement from Databricks and a customer pilot from SKY Brazil under a Limited Availability program. Dependencies and risks are factual and narrow: adoption depends on customers enabling catalog integrations and on the technical maturity of features like Data Lake Accelerator and Select AI. Operational complexity may persist until the advertised "catalog of catalogs" and plug-and-play SQL work seamlessly across varied deployments.

Watch for concrete milestones and signals over the next 6–12 months: general availability timing and billing details for Data Lake Accelerator, broader customer adoption beyond Limited Availability, performance benchmarks for Iceberg queries when cached on Exadata, and native integration depth with Unity Catalog and other catalogs. The press release date is October 14, 2025, which frames the initial Limited Availability statements and partner quotes as early evidence of market outreach rather than confirmed enterprise-wide adoption.

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

LAS VEGAS, Oct. 14, 2025 /PRNewswire/ -- Oracle AI World -- Oracle today introduced 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."

Additional Resources

• Read the technical blog about Oracle Autonomous AI Lakehouse
• Read what industry analysts are saying about Oracle Autonomous AI Lakehouse
• Learn more about Oracle Autonomous AI Database

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Future Product Disclaimer
The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, timing, and pricing of any features or functionality described for Oracle's products may change and remains at the sole discretion of Oracle Corporation.

Forward-Looking Statements Disclaimer
Statements in this article relating to Oracle's future plans, expectations, beliefs, and intentions are "forward-looking statements" and are subject to material risks and uncertainties. Many factors could affect Oracle's current expectations and actual results, and could cause actual results to differ materially. A discussion of such factors and other risks that affect Oracle's business is contained in Oracle's Securities and Exchange Commission (SEC) filings, including Oracle's most recent reports on Form 10-K and Form 10-Q under the heading "Risk Factors." These filings are available on the SEC's website or on Oracle's website at oracle.com/investor . All information in this article is current as of October 14, 2025 and Oracle undertakes no duty to update any statement in light of new information or future events

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SOURCE Oracle

FAQ

What is Oracle Autonomous AI Lakehouse (ORCL) announced Oct 14, 2025?

It is an open lakehouse platform combining Oracle Autonomous AI Database with native Apache Iceberg support to enable AI and analytics across clouds and platforms.

On which clouds is Oracle Autonomous AI Lakehouse (ORCL) available?

It is available on OCI, AWS, Microsoft Azure, Google Cloud, and Exadata Cloud@Customer.

How does Oracle Autonomous AI Lakehouse (ORCL) support catalog interoperability?

It integrates with catalogs such as Databricks Unity, AWS Glue, and Snowflake Polaris and adds an Autonomous AI Database Catalog as a 'catalog of catalogs.'

What performance claim did Oracle make for Autonomous AI Database (ORCL)?

Oracle said the Autonomous AI Database executes in excess of 48 billion queries per hour.

What does Data Lake Accelerator do in Oracle Autonomous AI Lakehouse (ORCL)?

Data Lake Accelerator dynamically scales network and compute to speed large queries on Iceberg tables and bills only for resources used.

What agent features are included with Oracle Autonomous AI Lakehouse (ORCL)?

It includes Select AI Agent for in‑database agentic workflows and a planned Data Science Agent for zero‑code data discovery and exploration.