New capabilities to power agents at scale with Google's Agentic Data Cloud
Google Cloud unveiled updates to its Agentic Data Cloud, including new Lakehouse and Knowledge Catalog features, to help enterprises scale AI agents with accurate, governed data access across clouds and SaaS platforms.
Google Cloud introduced the Agentic Data Cloud to help enterprises activate data across walled gardens, improve agent accuracy, and reduce costs. The system integrates with multi-cloud environments, on-premises systems, and SaaS platforms like SAP, Salesforce, and Workday. The borderless Lakehouse, built on Apache Iceberg, enables agents to query data across clouds without moving it or incurring egress fees, while caching remote data locally. Apache Iceberg data under management has more than tripled over the past year, reflecting growing adoption of the open format.
Gemini now operates directly on the borderless Lakehouse, allowing business users to query records across clouds using natural language. Cross-cloud interconnect and caching for AWS and Azure are generally available, enabling private, secure queries with zero variable egress fees. Customers can also use open Iceberg catalog federation with Databricks Unity, Snowflake Horizon, and AWS Glue, alongside zero-copy integrations with SAP, Workday, and Salesforce. Delivery Hero's talabat reduced data-movement costs by approximately 40% by federating Apache Iceberg data from Amazon S3 to BigQuery.
New AlloyDB integrations with the borderless Lakehouse enable direct Iceberg table queries without ETL or copies, combining transactional and analytical data in a single PostgreSQL query. AlloyDB direct Iceberg access is up to 7x faster on aggregation queries than Amazon’s comparable PostgreSQL-compatible service. Spanner Omni, generally available, extends Google-grade consistency and scale to hybrid agentic applications across on-premises, edge nodes, and multi-cloud environments.
Knowledge Catalog, Google Cloud’s universal context engine, now includes self-learning context sets and a reimagined user experience to automate context curation. Based on internal benchmarks, Knowledge Catalog makes AI agents nearly 4x more accurate, with up to 33% lower latency and using up to 75% fewer tokens. It integrates with semantic models from BigQuery, Looker, Databricks, Snowflake, dbt, and SAP, and extends metadata discovery to Oracle AI Database@Google Cloud, Snowflake, Databricks Unity Catalog, and third-party catalogs like Atlan and Collibra.