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Google Cloud Documentation
  • Technology areas
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    • Overview
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  • Console
  • Discover
  • What is Lakehouse?
  • How Lakehouse works
  • Key concepts
  • Get started
  • Build a borderless Lakehouse
  • 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
      • 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
    • Import Iceberg tables using Dataflow
    • Import Parquet files using Dataflow
  • Query tables
    • Query tables using SQL
    • Query tables with conversational analytics
    • Generate data insights for Iceberg tables
  • Connect with borderless Lakehouse
  • About borderless Lakehouse
  • Set up borderless Lakehouse
    • AWS Glue
    • Databricks Unity
    • Snowflake Horizon
    • SAP Business Data Cloud
      • About SAP BDC integration
      • Set up borderless Lakehouse for SAP BDC
      • Query SAP BDC data
      • Publish Data Products to SAP BDC
      • VPC SC config for SAP BDC
  • Use borderless Lakehouse
  • 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
  • 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
  • Borderless Lakehouse
    • Troubleshoot common issues
    • SAP Business Data Cloud
  • 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
  • Access and resources management
  • Costs and usage management
  • Infrastructure as code
  • SDK, languages, frameworks, and tools
As of April 20th, 2026, BigLake is now called Lakehouse for Apache Iceberg. BigLake metastore is now called the Lakehouse runtime catalog. Lakehouse APIs, client libraries, CLI commands, and IAM names remain unchanged and still reference BigLake.
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