American multinational cloud computing corporation Oracle has made some substantial developments in its Oracle Autonomous AI Lakehouse solution.
Specifically, this will be accompanied by an indigenous Business Semantic Layer in addition to improved read and write functionalities for Apache Iceberg tables. The Oracle autonomous data platform will enable the integration of business logic, multidimensional modeling, and open source data interoperability within the data platform architecture, making it possible for the Oracle AI workloads to effectively understand and apply the business logic.
Making AI Agents Understand the Real Business of Enterprises
With the growth of the autonomous and agentic AI systems in enterprises, a rather important technical problem arises: oftentimes, the answers given by the models can be technically correct but do not consider the real business logic, metrics, and organizational hierarchies of the enterprise. The more an agent needs to understand the business context of the enterprise from unstructured or raw database schema, the higher risks become.
In addition, enterprise data estates continue to be siloed in disparate stores, object storage, and multi-cloud environments. Legacy systems demand that data sets be converted to proprietary formats, increasing storage costs and computation delays.
Oracle addresses these issues with the convergence of open-table data storage and semantic awareness built right into the database layer. The new Autonomous AI Lakehouse allows businesses to keep their data sets in place within the object stores while enabling AI agents, data scientists, and business executives to query the live data using a consistent organizational definition.
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Some of the most important technical features and capabilities offered by the release are:
Unified Business Semantic Layer: Offers standard definitions of critical business metrics, financial calculations, and organizational structures to autonomous AI agents and business intelligence applications, helping remove any inconsistencies between departments.
SQL Operations Against Apache Iceberg Tables: Allows performing SQL operations such as creation, querying, update, and deletion of Apache Iceberg tables without moving any data around and without being restricted to any specific vendors.
Simulation Modeling Capabilities: Offers an embedded capability for executing multidimensional simulation models (based on Oracle Essbase technology).
Accelerated Lakehouse Performance: Employs Oracle’s Data Lake Accelerator and Lake Cache technology on Exadata flash storage, significantly reducing query response times on massive petabyte-scale Iceberg datasets.
Multi-Cloud Data Governance: Leverages the AI Data Catalog to enforce customer-defined, role-based access policies and connect across third-party environments including AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog.
“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.”
“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.”
Availability of Oracle Autonomous AI Lakehouse on Multi-Cloud Platforms
Oracle Autonomous AI Lakehouse can be found on Oracle Cloud Infrastructure (OCI), AWS, Microsoft Azure, Google Cloud, Oracle Exadata Cloud@Customer, and OCI Dedicated Regions. The enhanced features enable enterprises to have a common, vendor-agnostic data foundation that integrates business intelligence, AI development, and decision automation for all popular cloud infrastructure.