Oracle 23ai Extends ADW with Autonomous AI Lakehouse

Oracle 23ai Extends ADW with Autonomous AI Lakehouse

Oracle introduced its Autonomous AI Lakehouse, during its Oracle AI World event in Las Vegas.

This release is a significant enhancement and rebranding of the existing Oracle Autonomous Data Warehouse (ADW), positioning it as the next-generation, self-managing data platform.

This evolution signals Oracle’s commitment to integrating the high performance and security of its core database with the openness and flexibility of modern data lake architectures.

What Was Announced?

The Oracle 23ai Autonomous AI Lakehouse is an evolution built on the foundation of the Oracle Autonomous AI Database (version 26ai). The key capabilities announced focus on bridging the gap between the structured world of the data warehouse and the vast, open-format world of the data lake.

• The most notable feature is the native, high-performance support for Apache Iceberg tables. This allows the Autonomous AI Database engine to treat Iceberg data stored in object storage as first-class tables.
• Another key feature, Oracle’s Autonomous AI Database Catalog is described as a “catalog of catalogs.” It is designed to unify enterprise metadata, enabling customers to discover, connect to, and unify metadata from disparate data catalogs and platforms.
• To address potential performance concerns associated with querying external data, Oracle also introduced the Data Lake Accelerator. This is an integrated feature that dynamically scales network and compute capacity within the Autonomous AI Database when large-scale queries against external Iceberg tables are submitted.

How is Oracle’s Approach Distinct?

The core distinction of Oracle’s implementation is that the data lake is supported as an external source for the Oracle Autonomous Data Warehouse engine.

This differs from other popular lakehouse architectures, which often build a new, separate SQL engine on top of the object storage layer. Oracle’s approach leverages its high-performance, optimized relational engine to read and process open-format data wherever it resides.

This means the entire suite of database features, including Select AI for natural language-to-SQL transformation, Property Graph Analytics, and JSON-Relational Duality, are now extended to operate on the external Iceberg tables.

Oracle 23ai Autonomous Data Lakehouse Strengths

Oracle’s external data lake approach presents significant architectural strengths but also introduces specific technical challenges.

As mentioned above, the primary technological strength is the convergence of enterprise-grade reliability and openness. Customers get the Oracle’s automated database engine, including the hardware optimizations of Exadata, applied directly to data in open formats like Iceberg.

The Data Lake Accelerator is designed to mitigate the inherent performance challenge of querying external object storage by dynamically allocating dedicated compute resources for those specific jobs, ensuring high-speed access to external data.

From a business perspective, the multi-cloud availability and native Iceberg support offer a powerful defense against vendor lock-in, making the platform a more compelling choice for organizations with a complex, distributed data footprint.

Oracle Autonomous Data Lakehouse Challenges

However, the external nature of the data lake also introduces key technological challenges.

While the Accelerator is designed to boost speed, querying data over a network will inherently face potential performance and consistency issues compared to accessing data on local, high-speed Exadata storage. Factors like network latency and the underlying performance of a third-party object store remain outside Oracle’s direct control, potentially leading to performance variability.

The “catalog of catalogs” concept, while powerful for data discovery, presents a formidable governance and technical challenge. Ensuring seamless, real-time metadata synchronization and consistent security policies across heterogeneous catalogs from different vendors is a complex, maintenance-intensive task that could become a pain point for administrators.

While the elimination of ETL/ELT pipelines and associated egress costs offers a clear path to reduced TCO, the cost management may become more intricate due to the introduction of the Data Lake Accelerator’s dynamic, pay-per-use billing model. Organizations considering this option must carefully monitoring to prevent unexpected cloud spend.

Bottom Line

Enterprises utilizing Oracle 23ai Autonomous Data Warehouse should begin to test the new Lakehouse architecture with real world complex analytic workloads. Make sure to include increased license fess and cloud usage costs in your analysis.

Understand that the lakehouse architectures, while built on existing technologies, presents a new implementation that requires rigorous testing as if it were a new product.