Oracle recently announced its new autonomous AI lakehouse at the AI World Conference, enhancing its existing services such as Autonomous Database and Autonomous Data Warehouse. This lakehouse model combines the scalability and economy of data lakes with the analytic capabilities of data warehouses. Key features include support for Apache Iceberg, a query accelerator, and a new Autonomous AI Data Catalog, which simplifies metadata management and enhances data exploration across multiple cloud platforms.
Analysts suggest that Oracle’s introduction of these features reflects a strategic shift towards a more open interoperability model, comparable to offerings from competitors like Microsoft and Google. This could provide tangible benefits to existing customers, especially those seeking to adopt lakehouse analytics without discarding existing autonomous services. However, some experts caution that Oracle must differentiate its offerings in a market where similar interoperability features are already present.
The efficiency improvements and cost reduction potential of the new lakehouse are expected to attract new customers focused on operational effectiveness. Dynamic resource scaling to meet query demand and effective caching strategies could also significantly enhance workload efficiency, paralleling competitive solutions from Databricks and Snowflake while maintaining performance without data movement.
👉 Pročitaj original: CIO Magazine
