As enterprises shift their AI usage from experimental chatbots to autonomous agents, teams are hitting a critical bottleneck: accessing and reasoning over data scattered across multiple clouds, SaaS platforms, and on-premises systems. To turn AI agents into true systems of action, organizations need a foundation that can handle petabyte-scale reasoning without the security risks of data movement. Last week at our Gemini at Work event, we announced how Gemini can help you transform business workflows. To help power your agents with your enterprise data, we will dive deeper into the new Gemini integration with borderless Lakehouse to help you build, secure, and scale agents that work across your data estate.
The ultimate value of the borderless Lakehouse is realized through the agents you build to transform your organization, with Gemini as the front door. With the new Gemini integration, data that’s either preconfigured or stored in your borderless Lakehouse is now directly integrated into Gemini. Currently in preview, this means you can set up access to new data sources, such as Databricks Unity Catalog or Snowflake Horizon, through Lakehouse catalog federation directly from Gemini.
Now your business users can use natural language to query records that live across clouds while your AI agents can ground their reasoning and generate code from the same data — all without moving it. By directly contextualizing AI agents across your borderless Lakehouse, you can help accelerate your organization’s AI adoption while simplifying access to critical information.
In addition to this new, native integration with Gemini, we’re sharing that several features we introduced earlier this year to power your data foundation with the borderless Lakehouse are now generally available, including:
Support for Cross-Cloud Interconnect and cross-cloud caching for AWS and Azure
BigQuery support for Apache Iceberg V3 specifications
Read-write interoperability on Apache Iceberg tables for both BigQuery and Managed Service for Apache Spark
Support for AlloyDB lakehouse federation
Bi-directional caching with AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog using Lakehouse runtime federation
Integration with Knowledge Catalog
Zero-copy data integration with Salesforce, SAP and Workday, and now, Confluent Tableflow
Synchronization of Databricks Delta Lake assets through the Lakehouse console using Dataflow
Let’s explore.
While agent context windows are typically limited to ingesting hundreds of kilobytes at a time, enterprise workflows sometimes join petabytes of data across clouds. An agent-native data platform that needs to combine data across sources, like product and customer data, to your CRM cloud and warehouse telemetry needs to be able to process data at scale. When you do this over MCP, you are limited to megabyte scale because of context windows and payload size. By integrating at every layer of the data and AI stack, the borderless Lakehouse provides enterprise-grade scalability, enabling agents to reason over petabytes of data with similar performance to it being stored locally. At the networking layer, Cross-Cloud Interconnect and cross-cloud caching provide high-throughput, low-latency paths to AWS and Azure, eliminating variable egress fees. When an agent joins Iceberg tables in Amazon Azure Data Lake Storage with data in BigQuery, every uncached byte it scans crosses a cloud boundary, so the network sets the pace. Cross-Cloud Interconnect gives the Lakehouse a dedicated, private connection between Google Cloud and AWS or Azure, at 10 Gbps or 100 Gbps per connection. Lakehouse queries travel over that connection rather than the public internet, so latency stays low and predictable. Cross-cloud caching serves repeat reads from inside Google Cloud, so agents can query data in other clouds without migrating it.
The borderless Lakehouse includes several key optimizations to integrate Iceberg metadata within the query engine. BigQuery now supports Apache Iceberg V3 specifications, including binary deletion vectors and sort orders. These features allow the engine to handle row-level updates and deletes more efficiently by eliminating the need to rewrite entire data files. We also upgraded BigQuery’s execution engine with a new FlatBuffers-based Parquet reader and parallelized I/O to help reduce CPU overhead and serialization bottlenecks and narrow the performance gap between open formats and native storage.
This builds upon recent performance innovations to the query engine designed to support agent workloads, which often come in herds and need to process small queries quickly and cost effectively. With BigQuery fluid scaling and history based optimizations, you can run scalable and efficient agent workloads while maintaining openness with Apache Iceberg.
Meanwhile, read-write interoperability on Apache Iceberg tables for both BigQuery and Managed Service for Apache Spark makes it easy for all of your data teams to work off the same Iceberg tables in Google Cloud Storage. This vertically optimized stack helps make your agents faster and more cost-effective, leveraging the power of BigQuery and Lightning Engine to activate data without moving it. To expand the analytical depth of the lakehouse, graph support for Iceberg in BigQuery lets you analyze relationship networks, such as supply chains, directly on open table datasets.
Then, for operational data, AlloyDB lakehouse federation lets you query live database records and historical warehouse data together from a single connection without copying any information. This simplifies your system and lowers costs while giving agents instant access to both real-time updates and deep background history with the borderless Lakehouse.
An AI agent is only as smart as the context it can access. To help prevent hallucinations and improve accuracy at the metadata layer, the borderless Lakehouse unifies your enterprise data using the Lakehouse runtime catalog. Based on the Apache Iceberg REST Catalog, it provides bi-directional catalog federation with AWS Glue, Databricks Unity Catalog, and Snowflake Horizon Catalog. The Lakehouse runtime catalog also includes a new standard Hive Metastore-compatible endpoint, allowing engines like Apache Spark, Trino, and Presto to discover metadata without architectural changes.
The borderless Lakehouse also integrates directly with Knowledge Catalog, which automatically indexes your distributed data assets, to provide the precise business metadata and semantic definitions agents need to reason over your entire enterprise corpus — including unstructured data in Cloud Storage. This gives agents just enough context to be accurate while controlling token costs, helping you develop an intelligent and sustainable agentic strategy.
The borderless Lakehouse provides zero-copy data integration with your SaaS applications like Salesforce, SAP and Workday and integrates with a larger partner ecosystem, capturing enterprise data in Knowledge Catalog and acting as a single context engine for your agents. Today, we’re expanding the partner ecosystem with new integrations for Confluent Tableflow, now generally available, which syncs to Lakehouse runtime catalog so the Iceberg tables it produces show up directly in BigQuery, with no copying and no extra ETL.
Finally, organizations with Databricks-centric repositories can now easily import, transform and synchronize their Delta Lake assets into Lakehouse through the Lakehouse console using Dataflow.
Customer use of Apache Iceberg has more than tripled since the beginning of 2025, and enterprises building with Google Cloud’s borderless Lakehouse are seeing significant impact from it:
Etsy unified three petabytes with borderless Lakehouse, joining data in place to accelerate pipeline queries by 60%.
Commerzbank transforms raw distributed data into governed, agent-ready access. Hightouch integrates directly with borderless Lakehouse so teams can power their marketing directly on an open Iceberg lakehouse and activate that data across the Google Cloud ecosystem.
Roundel modernized its Target Product Ads technology stack from the ground up with borderless Lakehouse, completely rebuilding its data ingestion, processing, and measurement pipelines.
If you are ready to get started with the transition to borderless Lakehouse for your organization, it doesn’t need to be difficult. For organizations currently using legacy Dataproc Metastore, we now offer an automated, zero-downtime migration path to the serverless Lakehouse runtime catalog. This preserves your existing schemas and lineage while unlocking the AI-ready features of the borderless Lakehouse, so you can stop managing infrastructure and start activating agents. Get started today with the Build a borderless Lakehouse codelab or visit our website to learn more.