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Overview Guides Reference Resources
Google Cloud Documentation
  • Documentation
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    • Overview
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    • Resources
  • Console
  • Discover
  • What is borderless Lakehouse?
  • How borderless Lakehouse works
  • Key concepts
  • Get started
  • Set up cross-cloud data access
  • Query Iceberg tables with Spark and BigQuery
  • Configure the Lakehouse runtime catalog
  • About the Lakehouse runtime catalog
  • Set up catalog endpoints
    • Apache Iceberg REST catalog endpoint
      • About the Iceberg REST catalog endpoint
      • Set up the Iceberg REST catalog endpoint
      • Use open-source engines and tools
        • Apache Spark
        • Apache Flink
        • Trino
        • Terraform
    • Custom Apache Iceberg catalog for BigQuery endpoint
      • Overview
      • Configure for Apache Spark
        • Use Apache Iceberg 1.10 and higher
        • Use Apache Iceberg 1.9 and lower
      • Create and manage resources
      • Use with BigQuery tables
      • Use with stored procedures
      • Customize features
    • Apache Hive catalog endpoint
      • Overview
      • Set up Apache Spark and Hive
      • Supported storage formats and types
      • Limitations and considerations
  • Manage catalog endpoints
    • Overview
    • Create catalog
    • Update catalog
    • Create namespace
    • Enable credential vending
    • Get catalog details
    • Delete catalog
    • Delete namespace
    • View audit logs
  • Manage tables
  • Compare table types
  • Manage Apache Iceberg tables
    • Overview
    • Create table
    • Register table
    • List tables
    • Get table details
    • Insert data
    • Modify data with DML
    • Alter table
    • Configure table options
    • Upgrade Iceberg V1 tables to V2
    • Use Binary Deletion Vectors in Iceberg V3 tables
    • Delete table
  • Import tables
  • Lakehouse Iceberg REST catalog tables
    • Import Iceberg tables using Dataflow
    • Import Parquet files using Dataflow
    • Import Delta Lake tables using Dataflow
  • Query tables
    • Query tables using SQL
    • Query tables with conversational analytics
    • Generate data insights for Iceberg tables
  • Access cross-cloud data
  • About cross-cloud data access
  • Set up cross-cloud connections
    • AWS Glue
    • Databricks Unity
    • Snowflake Horizon
    • Workday Data Lake
    • SAP Business Data Cloud
      • About SAP BDC integration
      • Set up cross-cloud connection for SAP BDC
      • Query SAP BDC data
      • Publish Data Products to SAP BDC
      • VPC SC config for SAP BDC
  • Query remote data
  • Supported regions and capabilities
  • Back up and restore
  • Cross-region replication and disaster recovery
    • Overview
    • Use cross-region replication and disaster recovery
  • Load and ingest
  • Migrate external metadata to Apache Iceberg tables
  • Migrate metadata from Dataproc Metastore
  • Stream data with the Storage Write API
  • Change data capture ingestion
  • Secure and control access
  • Authentication
  • Access control with IAM
    • Overview
    • Roles and permissions
    • Manage catalog, namespace, and table ACLs
  • Credential vending
  • Troubleshoot
  • Common issues
  • Table management and security rules
  • Cross-cloud data access
    • Troubleshoot common issues
    • Databricks Unity
    • SAP Business Data Cloud
  • Get Started
  • Get Started with Google Cloud
  • Product List
  • Cloud Customer Care
  • Featured Products
  • Agent Platform
  • Apigee API Management
  • BigQuery
  • Compute Engine
  • Cloud CDN
  • Cloud Run
  • Cloud Storage
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  • Gemini Enterprise
  • Google Kubernetes Engine
  • Looker
  • Cross-product Tools
  • Access and resources management
  • Costs and usage management
  • Infrastructure as code
  • SDK, languages, frameworks, and tools
  • Technology Areas
  • AI and ML
  • Application development
  • Application hosting
  • Compute
  • Data analytics and pipelines
  • Databases
  • Distributed, hybrid, and multicloud
  • Industry solutions
  • Migration
  • Networking
  • Observability and monitoring
  • Security
  • Storage
As of April 20th, 2026, BigLake is now called Lakehouse. BigLake metastore is now called the Lakehouse runtime catalog. Lakehouse APIs, client libraries, CLI commands, and IAM names remain unchanged and still reference BigLake.