Google Cloud Updates Agentic Data Cloud: Borderless Lakehouse Opens Cross-Cloud Queries Across AWS and Azure as Knowledge Catalog Turns On by Default in Gemini
Google Cloud detailed a round of updates to its Agentic Data Cloud in a November 4 post on its official blog, centered on letting AI agents work directly with enterprise data spread across multi-cloud, on-premises and software-as-a-service environments. Google said organizations including PayPal, Siemens, Telus, Vodafone and Walmart are already using the platform.
The first set of updates enhances the borderless Lakehouse, built on the open Apache Iceberg format; Google said Iceberg data under management has more than tripled over the past year. Gemini now works directly on the Lakehouse, letting business users query records that live across clouds in natural language. Cross-cloud interconnect and caching for AWS and Azure are now generally available, allowing queries across clouds over a private, secure connection with zero variable egress fees, with retrieved data blocks automatically cached locally within Google Cloud. New AlloyDB integrations let AlloyDB query Iceberg tables stored in cloud storage directly, without ETL pipelines or copies, joining live operational data with historical lakehouse data in a single PostgreSQL query; Google said direct Iceberg access in AlloyDB is up to 7x faster on aggregation queries than Amazon's comparable PostgreSQL-compatible service. Spanner Omni is generally available, bringing consistency, scale and multi-model capabilities to on-premises, edge and multi-cloud environments.
The second set upgrades Knowledge Catalog, Google Cloud's context engine: self-learning enrichment, in preview, mines unstructured sources including Google Workspace, GitHub, Jira, Confluence and SharePoint to close context gaps; Knowledge Catalog is now on by default in Gemini, in preview; and grounding in trusted semantic models from BigQuery, Looker, Databricks, Snowflake, dbt and SAP is generally available. Based on its internal benchmarks, Google said Knowledge Catalog makes AI agents nearly 4x more accurate, with up to 33% lower latency and up to 75% fewer tokens. Harish Ramesh, a staff data engineer at Delivery Hero's talabat, said in the post that federating single-copy Apache Iceberg data from Amazon S3 tables to BigQuery reduced data-movement costs by approximately 40% while making streaming transactional events queryable across both clouds within minutes of production.
Other updates include general availability of the Data Agent Kit, which brings Google Data Cloud tools into development environments including VS Code, Claude Code and Antigravity; AlloyDB Express in preview, offering sub-2-second provisioning and scale-to-zero pause and resume for agent development and testing; BigQuery Graph in general availability; and Google Research's TimesFM 3.0 and TabFM models running natively in BigQuery in preview. Google said BigQuery queries have become over 40% faster in the past year while query processing costs have fallen by more than 50%.
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Source: Google Cloud and T-Systems (company
Published by GSM · USA (original Chinese edition)
Photo: 数据中心机房（资料图）。（Wikimedia Commons）|A data center (file photo). (Wikimedia Commons)
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