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BigQuery
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Overview
Guides
Reference
Samples
Resources
Technology areas
More
Overview
Guides
Reference
Samples
Resources
Cross-product tools
More
Console
Discover
Product overview
Try BigQuery using the sandbox
Get started
Console walkthroughs and videos
Use the console
Explore the console
Load and query data
Use Notebook gallery
Create reservations
Try DataFrames
Use agents
Analyze data with the Colab Data Science Agent
Use the bq CLI tool
Use the client libraries
Plan
Introduction to the resource hierarchy
API dependencies
Datasets
Introduction
Create datasets
List datasets
Cross-region replication
Managed disaster recovery
Migrate to managed disaster recovery
Dataset data retention
Tables
BigQuery tables
Introduction
Create and use tables
Specify table schemas
Specify a schema
Specify nested and repeated columns
Specify default column values
Specify ObjectRef values
Segment with partitioned tables
Introduction
Create partitioned tables
Manage partitioned tables
Query partitioned tables
Optimize with clustered tables
Introduction
Create clustered tables
Manage clustered tables
Query clustered tables
Use metadata indexing
External data sources
Introduction
BigLake tables
Introduction
Create Amazon S3 BigLake tables
Create Azure Blob storage BigLake tables
Create Cloud Storage BigLake tables
Object tables
Introduction
Create Cloud Storage object tables
External tables
Introduction
Create Apache Iceberg external tables
Create Bigtable external tables
Create Cloud Storage external tables
Create Google Drive external tables
Use metadata caching
Manage Hive partitioned data
Create a table definition file for external data
Federated datasets
Create and manage AWS Glue federated datasets
Create Spanner external datasets
Apache Iceberg managed tables
Interact with Lakehouse data
Views
Overview
Logical views
Introduction
Create logical views
Materialized views
Introduction
Create materialized views
Load, transform, and export
Introduction
Build a data pipeline with Data Engineering Agent
Migrate data
Introduction
Migration assessment
Migrate schema and data
Migrate data pipelines
Use custom organization policies
Migrate SQL
Translate SQL queries interactively
Translate SQL queries using the API
Translate SQL queries in batch
Generate metadata for translation and assessment
Transform SQL translations with YAML
Map SQL object names for batch translation
Load data
Introduction
Create data integration workflows using the BigQuery web UI
Storage overview
BigQuery Data Transfer Service
Introduction
Supported data sources
Data location and transfers
Authorize transfers
Enable transfers
Set up network connections
Cloud SQL instance access
AWS VPN and network attachment
Azure VPN and network attachment
Manage transfers
Transfer run notifications
Troubleshoot transfer configurations
Use service accounts
Use third-party transfers
Use custom organization policies
Data source change log
Event-driven transfers
Transfer data into Managed Iceberg tables
Batch load data
Introduction
Auto-detect schemas
Load Avro data
Load Parquet data
Load ORC data
Load CSV data
Load JSON data
Load externally partitioned data
Load data from a Datastore export
Load data from a Firestore export
Load data using the Storage Write API
Load data into partitioned tables
Write and read data with the Storage API
Read data with the Storage Read API
Write data with the Storage Write API
Introduction
Use the Storage Write API (gRPC)
Overview
Stream data
Batch load data
Stream updates with change data capture ingestion
Best practices
Supported protocol buffer and Arrow data types
Use the Storage Write API (REST)
Load data from other Google services
Discover and catalog Cloud Storage data
Load data using third-party apps
Load data using BigQuery Omni operations
Optimize load jobs
Transform data
Introduction
Prepare data
Introduction
Prepare data with Gemini
Transform with DML
Transform data in partitioned tables
Work with change history
Transform data with pipelines
Introduction
Create pipelines
Export data
Introduction
Export query results
Export to Cloud Storage
Export to Bigtable
Export to Spanner
Export to AlloyDB
Export to Pub/Sub
Export as Protobuf columns
ELT tutorials
Build ELT for marketing analytics data
Analyze
Introduction
Explore your data
Search for resources
Profile your data
Data insights
About data insights
Generate table insights
Generate dataset insights
Analyze with a data canvas
Analyze data with Gemini
Analyze data with the Gemini CLI
Analyze your data
Run a query
Write queries with Gemini
Write query results
Query data with SQL
Introduction
Arrays
JSON data
Multi-statement queries
Parameterized queries
Pipe syntax
Analyze data using pipe syntax
Recursive CTEs
Sketches
Table sampling
Time series
Transactions
Wildcard tables
Use notebooks
Introduction
Use Colab notebooks
Introduction
Create notebooks
Explore query results
Visualize query results
Create and share Data Apps
Use Spark
Use Colab Data Science Agent
Use DataFrames
Introduction
Install DataFrames
Manipulate data
Customize Python functions
Use ML and AI
Use the data type system
Manage sessions and I/O
Visualize graphs
Use DataFrames in dbt
Optimize performance
Migrate to DataFrames version 2.0
Use the BigQuery JupyterLab plugin
Use geospatial analytics
Introduction
Work with geospatial analytics
Work with raster data
Best practices for spatial analysis
Visualize geospatial data
Grid systems for spatial analysis
Geospatial analytics syntax reference
Geospatial analytics tutorials
Get started with geospatial analytics
Use geospatial analytics to plot a hurricane's path
Visualize geospatial analytics data in a Colab notebook
Use raster data to analyze temperature
Routines
Introduction
Manage routines
User-defined functions
User-defined functions in Python
User-defined aggregate functions
Table functions
Remote functions
SQL stored procedures
Stored procedures for Apache Spark
Analyze object tables by using remote functions
Remote functions and Translation API tutorial
Analyze multimodal data
Introduction
Work with ObjectRef values
Analyze multimodal data with SQL and BigQuery DataFrames
Search indexes
Introduction
Manage search indexes
Search indexed data
Work with text analyzers
Run global queries
Access historical data
Use open source Python libraries
Use BigQuery Graph
Introduction
Create and query a graph
Schema overview
Query overview
Query best practices
Work with measures
Use the visual graph modeler
Visualize graphs
Chat with a graph
Work with visualization tools and integrations
Use BigQuery Graph and Spanner Graph
Search a graph
Manage queries
Save queries
Introduction
Create saved queries
Continuous queries
Introduction
Create continuous queries
Use stream-to-stream joins in continuous queries
Understand window aggregation in continuous queries
Use cached results
Use sessions
Introduction
Work with sessions
Write queries in sessions
Troubleshoot queries
Optimize queries
Introduction
Use the query plan explanation
Get query performance insights
Optimize query computation
Use history-based optimizations
Optimize storage for query performance
Use materialized views
Use BI Engine
Use nested and repeated data
Optimize functions
Use the advanced runtime
Use primary and foreign keys
Paginate with the BigQuery API
Query external data sources
Establish connections
Introduction
Create connections
Create a Cloud resource connection
Create a default Cloud resource connection
AlloyDB connections
Amazon S3 connections
Apache Spark connections
Azure Blob Storage connections
Cloud SQL connections
SAP Datasphere connections
Spanner connections
Create cross-cloud connections
Manage connections
Configure connections with network attachments
Run queries on external data
Query BigLake tables
Amazon S3 BigLake tables
Azure Blob storage BigLake tables
Cloud Storage BigLake tables
