Activate your data, regardless where it lives, with the Google Cloud borderless Lakehouse
Activate your data, regardless where it lives, with the Google Cloud borderless Lakehouse
The reality is, your data lives everywhere—on Google, across other clouds like AWS and Azure, and in your SaaS applications. Built on open Apache Iceberg and connecting to your operational systems both on-premises and across clouds, the borderless Lakehouse lets you activate your data wherever it lives, without moving it.
Bi-directional catalog interoperability
Bi-directional catalog interoperability
Empower analysts and conversational agents to instantly query and act on multi-cloud data wherever it physically resides, removing the overhead of building and maintaining traditional ETL pipelines. Built on the open Iceberg REST catalog, this architecture delivers secure, bi-directional catalog federation (now in preview) across AWS Glue, Databricks Unity, and Snowflake Horizon—unlocking unified, real-time insights for BigQuery, Managed Service for Apache Spark, and any Iceberg-compatible engine.
Zero copy data from SaaS applications
Zero copy data from SaaS applications
Break down SaaS data silos and fuel your AI strategy without the risk, lag, or cost of data movement. By extending your lakehouse directly to the application layer, you achieve secure, zero-copy integration with core enterprise systems like SAP, Salesforce, and Workday. This allows BigQuery to query live transactional data in real time—eliminating complex ETL pipelines—while enabling you to run BigQuery’s powerful AI engines directly on your source data in-place. The result is a unified, secure foundation across finance, HR, and customer operations that dramatically accelerates time-to-decision and lowers total cost of ownership.
Get the openness of Apache Iceberg with enterprise-grade storage management
The future of enterprise data isn't just about storage—it's about taking immediate action. The borderless Lakehouse gives your analyst and agents the power to safely analyze and work with your data wherever it lives. Eliminate the need for slow, expensive data-copying projects so your business can confidently make faster, automated decisions in real time while keeping your cloud budgets under control.
Bring Google AI directly to your AWS and Azure data
Eliminate the multi-cloud tax and accelerate your data strategy. The borderless Lakehouse delivers predictable, flat-rate economics and private, SLA-backed Partner Cross-Cloud Interconnect connectivity that eliminates variable egress costs for your AWS data. By pairing intelligent cross-cloud caching with BigQuery’s vectorized processing, Spark’s Lightning Engine runtime, and native multimodal AI functions, you can securely analyze petabytes of remote data with sub-second performance—without repeated, costly transfers or performance trade-offs.
Bi-directional catalog interoperability
Empower analysts and conversational agents to instantly query and act on multi-cloud data wherever it physically resides, removing the overhead of building and maintaining traditional ETL pipelines. Built on the open Iceberg REST catalog, this architecture delivers secure, bi-directional catalog federation (now in preview) across AWS Glue, Databricks Unity, and Snowflake Horizon—unlocking unified, real-time insights for BigQuery, Managed Service for Apache Spark, and any Iceberg-compatible engine.
Zero copy data from SaaS applications
Break down SaaS data silos and fuel your AI strategy without the risk, lag, or cost of data movement. By extending your lakehouse directly to the application layer, you achieve secure, zero-copy integration with core enterprise systems like SAP, Salesforce, and Workday. This allows BigQuery to query live transactional data in real time—eliminating complex ETL pipelines—while enabling you to run BigQuery’s powerful AI engines directly on your source data in-place. The result is a unified, secure foundation across finance, HR, and customer operations that dramatically accelerates time-to-decision and lowers total cost of ownership.
Get the openness of Apache Iceberg with enterprise-grade storage management
The future of enterprise data isn't just about storage—it's about taking immediate action. The borderless Lakehouse gives your analyst and agents the power to safely analyze and work with your data wherever it lives. Eliminate the need for slow, expensive data-copying projects so your business can confidently make faster, automated decisions in real time while keeping your cloud budgets under control.
Bi-directional catalog interoperability
Bi-directional catalog interoperability
Empower analysts and conversational agents to instantly query and act on multi-cloud data wherever it physically resides, removing the overhead of building and maintaining traditional ETL pipelines. Built on the open Iceberg REST catalog, this architecture delivers secure, bi-directional catalog federation (now in preview) across AWS Glue, Databricks Unity, and Snowflake Horizon—unlocking unified, real-time insights for BigQuery, Managed Service for Apache Spark, and any Iceberg-compatible engine.
Zero copy data from SaaS applications
Zero copy data from SaaS applications
Break down SaaS data silos and fuel your AI strategy without the risk, lag, or cost of data movement. By extending your lakehouse directly to the application layer, you achieve secure, zero-copy integration with core enterprise systems like SAP, Salesforce, and Workday. This allows BigQuery to query live transactional data in real time—eliminating complex ETL pipelines—while enabling you to run BigQuery’s powerful AI engines directly on your source data in-place. The result is a unified, secure foundation across finance, HR, and customer operations that dramatically accelerates time-to-decision and lowers total cost of ownership.
Bring Google AI directly to your AWS and Azure data
Eliminate the multi-cloud tax and accelerate your data strategy. The borderless Lakehouse delivers predictable, flat-rate economics and private, SLA-backed Partner Cross-Cloud Interconnect connectivity that eliminates variable egress costs for your AWS data. By pairing intelligent cross-cloud caching with BigQuery’s vectorized processing, Spark’s Lightning Engine runtime, and native multimodal AI functions, you can securely analyze petabytes of remote data with sub-second performance—without repeated, costly transfers or performance trade-offs.
Bi-directional catalog interoperability
Empower analysts and conversational agents to instantly query and act on multi-cloud data wherever it physically resides, removing the overhead of building and maintaining traditional ETL pipelines. Built on the open Iceberg REST catalog, this architecture delivers secure, bi-directional catalog federation (now in preview) across AWS Glue, Databricks Unity, and Snowflake Horizon—unlocking unified, real-time insights for BigQuery, Managed Service for Apache Spark, and any Iceberg-compatible engine.
Zero copy data from SaaS applications
Break down SaaS data silos and fuel your AI strategy without the risk, lag, or cost of data movement. By extending your lakehouse directly to the application layer, you achieve secure, zero-copy integration with core enterprise systems like SAP, Salesforce, and Workday. This allows BigQuery to query live transactional data in real time—eliminating complex ETL pipelines—while enabling you to run BigQuery’s powerful AI engines directly on your source data in-place. The result is a unified, secure foundation across finance, HR, and customer operations that dramatically accelerates time-to-decision and lowers total cost of ownership.
Differentiated engines like BigQuery and Managed Spark
Google Cloud Lakehouse delivers managed Apache Iceberg with true read/write interoperability across BigQuery and Spark. Get the price-performance and scale of BigQuery and Google Managed Service for Apache Spark with flexible storage to accelerate your multimodal analytics, data science agentic workloads.
Advanced BigQuery workloads on Iceberg
Empower your teams to make split-second business decisions using a secure, unified platform that synchronizes live data in real-time without costly processing delays. By seamlessly bringing together structured business metrics and unstructured assets using BigQuery Object References, you unlock the advanced analytics and next-generation AI needed to drive immediate competitive advantage.
Accelerate data science with Managed Service for Apache Spark
Supercharge your data science initiatives by running Apache Spark directly on your lakehouse with up to 4.9x faster performance. Powered by the Lightning Engine, this managed service eliminates complex infrastructure management so your data scientists can train models and analyze massive datasets in record time. By combining lightning-fast processing with direct, secure access to your lakehouse data, you dramatically accelerate your team's time-to-insight and lower machine learning development costs.
Accelerate data science with Managed Service for Apache Spark
Accelerate data science with Managed Service for Apache Spark
Supercharge your data science initiatives by running Apache Spark directly on your lakehouse with up to 4.9x faster performance. Powered by the Lightning Engine, this managed service eliminates complex infrastructure management so your data scientists can train models and analyze massive datasets in record time. By combining lightning-fast processing with direct, secure access to your lakehouse data, you dramatically accelerate your team's time-to-insight and lower machine learning development costs.
Differentiated engines like BigQuery and Managed Spark
Google Cloud Lakehouse delivers managed Apache Iceberg with true read/write interoperability across BigQuery and Spark. Get the price-performance and scale of BigQuery and Google Managed Service for Apache Spark with flexible storage to accelerate your multimodal analytics, data science agentic workloads.
Accelerate data science with Managed Service for Apache Spark
Accelerate data science with Managed Service for Apache Spark
Supercharge your data science initiatives by running Apache Spark directly on your lakehouse with up to 4.9x faster performance. Powered by the Lightning Engine, this managed service eliminates complex infrastructure management so your data scientists can train models and analyze massive datasets in record time. By combining lightning-fast processing with direct, secure access to your lakehouse data, you dramatically accelerate your team's time-to-insight and lower machine learning development costs.
Advanced BigQuery workloads on Iceberg
Empower your teams to make split-second business decisions using a secure, unified platform that synchronizes live data in real-time without costly processing delays. By seamlessly bringing together structured business metrics and unstructured assets using BigQuery Object References, you unlock the advanced analytics and next-generation AI needed to drive immediate competitive advantage.
Accelerate data science with Managed Service for Apache Spark
Supercharge your data science initiatives by running Apache Spark directly on your lakehouse with up to 4.9x faster performance. Powered by the Lightning Engine, this managed service eliminates complex infrastructure management so your data scientists can train models and analyze massive datasets in record time. By combining lightning-fast processing with direct, secure access to your lakehouse data, you dramatically accelerate your team's time-to-insight and lower machine learning development costs.
Enrich data and generate meaning through continuous learning
Enrich data and generate meaning through continuous learning
Accelerate AI-readiness and eliminate manual curation by turning raw, unstructured data into a self-updating map of your business. By leveraging Gemini to automate data enrichment—mining schemas, files, and logs to auto-tag assets—you establish a trusted natural-language glossary of your enterprise operations. These built-in semantic guardrails and pre-validated logic eliminate costly query errors and AI hallucinations, enabling both your analysts and autonomous agents make decisions based on trusted data.
Power search and unleash agents with secure retrieval
Power search and unleash agents with secure retrieval
Give your AI assistants the exact business context they need in under a second—safely and securely. Our advanced search instantly connects your AI to the right information for accurate decision-making, while strict security permissions guarantee they only see data they are authorized to access. This allows you to easily build and deploy trustworthy AI tools using our pre-built Data Cloud Agents or flexible Data Agent Kit.
Open lakehouse governance with always on context for your agents
Knowledge Catalog is the AI-driven context engine for your lakehouse. It auto-catalogs all Google Cloud and third-party data, eliminating manual curation to deliver the grounded enterprise truth and data relationships essential for reliable agentic AI.
Aggregate context across your data estate
Knowledge Catalog eliminates data fragmentation by automatically unifying metadata from Google Cloud, partner platforms, and third-party catalogs (such as Atlan, Collibra, and Datahub) into a single, governed source of truth. By automating the discovery and indexing of your entire data footprint, you dramatically reduce compliance risk, eliminate manual data-mapping overhead, and help both your teams and AI systems can find and trust the data they need instantly.
Enrich data and generate meaning through continuous learning
Accelerate AI-readiness and eliminate manual curation by turning raw, unstructured data into a self-updating map of your business. By leveraging Gemini to automate data enrichment—mining schemas, files, and logs to auto-tag assets—you establish a trusted natural-language glossary of your enterprise operations. These built-in semantic guardrails and pre-validated logic eliminate costly query errors and AI hallucinations, enabling both your analysts and autonomous agents make decisions based on trusted data.
Power search and unleash agents with secure retrieval
Give your AI assistants the exact business context they need in under a second—safely and securely. Our advanced search instantly connects your AI to the right information for accurate decision-making, while strict security permissions guarantee they only see data they are authorized to access. This allows you to easily build and deploy trustworthy AI tools using our pre-built Data Cloud Agents or flexible Data Agent Kit.
Open lakehouse governance with always on context for your agents
Knowledge Catalog is the AI-driven context engine for your lakehouse. It auto-catalogs all Google Cloud and third-party data, eliminating manual curation to deliver the grounded enterprise truth and data relationships essential for reliable agentic AI.
Enrich data and generate meaning through continuous learning
Enrich data and generate meaning through continuous learning
Accelerate AI-readiness and eliminate manual curation by turning raw, unstructured data into a self-updating map of your business. By leveraging Gemini to automate data enrichment—mining schemas, files, and logs to auto-tag assets—you establish a trusted natural-language glossary of your enterprise operations. These built-in semantic guardrails and pre-validated logic eliminate costly query errors and AI hallucinations, enabling both your analysts and autonomous agents make decisions based on trusted data.
Power search and unleash agents with secure retrieval
Power search and unleash agents with secure retrieval
Give your AI assistants the exact business context they need in under a second—safely and securely. Our advanced search instantly connects your AI to the right information for accurate decision-making, while strict security permissions guarantee they only see data they are authorized to access. This allows you to easily build and deploy trustworthy AI tools using our pre-built Data Cloud Agents or flexible Data Agent Kit.
Aggregate context across your data estate
Knowledge Catalog eliminates data fragmentation by automatically unifying metadata from Google Cloud, partner platforms, and third-party catalogs (such as Atlan, Collibra, and Datahub) into a single, governed source of truth. By automating the discovery and indexing of your entire data footprint, you dramatically reduce compliance risk, eliminate manual data-mapping overhead, and help both your teams and AI systems can find and trust the data they need instantly.
Enrich data and generate meaning through continuous learning
Accelerate AI-readiness and eliminate manual curation by turning raw, unstructured data into a self-updating map of your business. By leveraging Gemini to automate data enrichment—mining schemas, files, and logs to auto-tag assets—you establish a trusted natural-language glossary of your enterprise operations. These built-in semantic guardrails and pre-validated logic eliminate costly query errors and AI hallucinations, enabling both your analysts and autonomous agents make decisions based on trusted data.
Power search and unleash agents with secure retrieval
Give your AI assistants the exact business context they need in under a second—safely and securely. Our advanced search instantly connects your AI to the right information for accurate decision-making, while strict security permissions guarantee they only see data they are authorized to access. This allows you to easily build and deploy trustworthy AI tools using our pre-built Data Cloud Agents or flexible Data Agent Kit.
Combine analytical and operational data for real-time, agentic applications
The Google Cloud Borderless Lakehouse bridges the divide between real-time transactional systems and analytical intelligence. By connecting your active databases directly to your analytical engine, you can transition from simply analyzing historical reports to triggering instant, automated operational actions.
Introducing Spanner Omni
Spanner Omni brings the "unbreakable" DNA of Google Spanner—can achieve high availability with zonal and regional, strong consistency, and virtually unlimited scale—to your local hardware, your private clouds, and every edge in between.
Lakehouse federation with AlloyDB
Lakehouse Federation for AlloyDB enables real-time business insights by allowing your transactional systems to directly query data warehouses without costly data movement. This eliminates data silos and expensive infrastructure, empowering your team to make faster, data-driven decisions using both operational and historical data together.
Lakehouse federation with AlloyDB
Lakehouse federation with AlloyDB
Lakehouse Federation for AlloyDB enables real-time business insights by allowing your transactional systems to directly query data warehouses without costly data movement. This eliminates data silos and expensive infrastructure, empowering your team to make faster, data-driven decisions using both operational and historical data together.
Combine analytical and operational data for real-time, agentic applications
The Google Cloud Borderless Lakehouse bridges the divide between real-time transactional systems and analytical intelligence. By connecting your active databases directly to your analytical engine, you can transition from simply analyzing historical reports to triggering instant, automated operational actions.
Lakehouse federation with AlloyDB
Lakehouse federation with AlloyDB
Lakehouse Federation for AlloyDB enables real-time business insights by allowing your transactional systems to directly query data warehouses without costly data movement. This eliminates data silos and expensive infrastructure, empowering your team to make faster, data-driven decisions using both operational and historical data together.
Introducing Spanner Omni
Spanner Omni brings the "unbreakable" DNA of Google Spanner—can achieve high availability with zonal and regional, strong consistency, and virtually unlimited scale—to your local hardware, your private clouds, and every edge in between.
Lakehouse federation with AlloyDB
Lakehouse Federation for AlloyDB enables real-time business insights by allowing your transactional systems to directly query data warehouses without costly data movement. This eliminates data silos and expensive infrastructure, empowering your team to make faster, data-driven decisions using both operational and historical data together.
