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Discover
Product overview
Get started
Use the BigQuery sandbox
Try the console
Query and visualize data
Load and query data
Try DataFrames
Try the command-line tool
Load and query data
Try the client libraries
Explore BigQuery tools
Explore the console
Explore the command-line tool
Migrate
Overview
Migrate a data warehouse
Introduction to BigQuery Migration Service
Migration assessment
Migrate schema and data
Migrate data pipelines
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
Migration guides
Amazon Redshift
Migration overview
Migrate Amazon Redshift schema and data
Migrate Amazon Redshift schema and data when using a VPC
SQL translation reference
Apache Hadoop
Extract metadata from Hadoop for migration
Migrate permissions from Hadoop
Schedule an HDFS data lake transfer
Apache Hive
Hive migration overview
Migrate Apache Hive schema and data
SQL translation reference
IBM Netezza
Migrate from IBM Netezza
SQL translation reference
Oracle
Migration guide
SQL translation reference
Snowflake
Introduction
Schedule a Snowflake transfer
Migration overview
SQL translation reference
Teradata
Introduction
Migration overview
Migrate Teradata schema and data
Migration tutorial
SQL translation reference
Design
Organize resources
API dependencies
Understand editions
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
BigLake Iceberg tables in BigQuery
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 tables
Introduction
Types of external tables
BigLake external tables
BigQuery Omni
Object tables
External tables
External table definition file
Externally partitioned data
Use metadata caching
Amazon S3 BigLake external tables
Apache Iceberg external tables
Azure Blob Storage BigLake tables
Bigtable external table
BigLake external tables for Cloud Storage
Cloud Storage object tables
Cloud Storage external tables
Delta Lake BigLake tables
Google Drive external tables
Views
Logical views
Introduction
Create logical views
Materialized views
Introduction
Create materialized views
Manage all view types
Get information about views
Manage views
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
Connections
Introduction
Amazon S3 connection
Apache Spark connection
Azure Blob Storage connection
Cloud resource connection
Spanner connection
Cloud SQL connection
AlloyDB connection
SAP Datasphere connection
Manage connections
Configure connections with network attachments
Default connections
Indexes
Search indexes
Introduction
Manage search indexes
Vector indexes
Introduction
Manage vector indexes
Load, transform, and export
Introduction
Load data
Introduction
Storage overview
BigQuery Data Transfer Service
Introduction
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 guides
Amazon S3
Introduction
Schedule transfers
Transfer runtime parameters
Azure Blob Storage
Introduction
Schedule transfers
Transfer runtime parameters
Campaign Manager
Schedule transfers
Report transformation
Cloud Storage
Introduction
Schedule transfers
Transfer runtime parameters
Comparison Shopping Service Center
Introduction
Schedule transfers
Transfer report schema
Display & Video 360
Schedule transfers
Report transformation
Facebook Ads
Schedule transfers
Report transformation
Google Ad Manager
Schedule transfers
Report transformation
Google Ads
Schedule transfers
Report transformation
Google Analytics 4
Schedule transfers
Report transformation
Google Merchant Center
Introduction
Schedule transfers
Query your data
Migration guides
Best sellers
Price competitiveness
Transfer report schema
Best Sellers table
Local Inventories table
Performance table
Price Benchmarks table
Price Competitiveness table
Price Insights table
Product Inventory table
Product Targeting table
Products table
Regional Inventories table
Top Brands table
Top Products table
Google Play
Schedule transfers
Transfer report transformation
MySQL
Schedule transfers
Oracle
Schedule transfers
PostgreSQL
Schedule transfers
Salesforce
Schedule transfers
Salesforce Marketing Cloud
Schedule transfers
Search Ads 360
Schedule transfers
Transfer report transformation
Migration guide
ServiceNow
Schedule transfers
YouTube channel
Schedule transfers
Transfer report transformation
YouTube content owner
Schedule transfers
Transfer report transformation
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
Stream data with the Storage Write API
Batch load data with the Storage Write API
Best practices
Supported protocol buffer and Arrow data types
Stream updates with change data capture
Use the legacy streaming API
Load data from other Google services
Discover and catalog Cloud Storage data
Load data using third-party apps
Load data using cross-cloud operations
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 Pub/Sub
Export as Protobuf columns
Analyze
Introduction
Search for resources
Explore your data
Create queries with table explorer
Profile your data
Generate data insights
Analyze with a data canvas
Analyze data with Gemini
Query BigQuery 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 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
Search data
Search indexed data
Work with text analyzers
Access historical data
Work with queries
Save queries
Introduction
Create saved queries
Continuous queries
Introduction
Create 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
Analyze multimodal data
Introduction
Analyze multimodal data with SQL and Python UDFs
Analyze multimodal data with BigQuery DataFrames
Query external data sources
Manage open source metadata with BigLake metastore
Introduction
Use with tables in BigQuery
Use with Dataproc
Use with Serverless for Apache Spark
Use with Spark stored procedures
Manage metastore resources
Create and query tables from Spark
Customize with additional features
Use with the Iceberg REST catalog
Optimal data and metadata formats for lakehouses
Use external tables and datasets
Amazon S3 data
Query Amazon S3 data
Export query results to Amazon S3
Query Apache Iceberg data
Query open table formats with manifests
Azure Blob Storage data
Query Azure Blob Storage data
Export query results to Azure Blob Storage
Query Cloud Bigtable data
Cloud Storage data
Query Cloud Storage data in BigLake tables
Query Cloud Storage data in external tables
Work with Salesforce Data Cloud data
Query Google Drive data
Create AWS Glue federated datasets
Create Spanner external datasets
Run federated queries
Federated queries
Query SAP Datasphere data
Query AlloyDB data
Query Spanner data
Query Cloud SQL data
Use notebooks
Introduction
Use Colab notebooks
Introduction
Create notebooks
Explore query results
Use Spark
Use Colab Data Science Agent
Use DataFrames
Introduction
Use DataFrames
Use the data type system
Manage sessions and I/O
Visualize graphs
Use DataFrames in dbt
Optimize performance
Use Jupyter notebooks
Use the BigQuery JupyterLab plugin
Use analysis and BI tools
Introduction
Use Connected Sheets
Use Tableau Desktop
Use Looker
Use Looker Studio
Use third-party tools
Google Cloud Ready - BigQuery
Overview
Partners
AI and machine learning
Introduction
Generative AI and pretrained models
End-to-end user journeys for generative AI models
Generative AI
Overview
Built-in models
The TimesFM time series forecasting model
Tutorials
Generate text
Generate text using public data and Gemini
Generate text using public data and Gemma
Generate text using your data
Handle quota errors by calling ML.GENERATE_TEXT iteratively
Analyze images with a Gemini model
Tune text generation models
Tune a model using your data
Use tuning and evaluation to improve model performance
Generate structured data
Generate structured data
Generate embeddings
Generate text embeddings using an LLM
Generate text embeddings using an open model
Generate image embeddings using an LLM
Generate video embeddings using an LLM
Handle quota errors by calling ML.GENERATE_EMBEDDING iteratively
Generate and search multimodal embeddings
Generate text embeddings using pretrained TensorFlow models
Vector search
Search embeddings with vector search
Perform semantic search and retrieval-augmented generation
Task-specific solutions
Overview
Tutorials
Natural language processing
Understand text
Translate text
Document processing
Process documents
Parse PDFs in a retrieval-augmented generation pipeline
Speech recognition
Transcribe audio files
Computer vision
Annotate images
Run inference on image data
Analyze images with an imported classification model
Analyze images with an imported feature vector model
Choose generative AI and task-specific functions
Choose a natural language processing function
Choose a document processing function
Choose a transcription function
Machine learning
End-to-end user journeys for ML models
End-to-end user journeys for imported models
ML models and MLOps
Model creation
Feature engineering and management
Feature preprocessing overview
Supported input feature types
Automatic preprocessing
Manual preprocessing
Feature serving
Perform feature engineering with the TRANSFORM clause
Hyperparameter tuning overview
Model evaluation overview
Model inference overview
Explainable AI overview
Model weights overview
ML pipelines overview
Model monitoring overview
Manage BigQueryML models in Vertex AI
Use cases
Classification
Regression
Dimensionality reduction
Clustering
Recommendation
Anomaly detection
Tutorials
Getting started
Get started with BigQuery ML using SQL
Get started with BigQuery ML using the Cloud console
Regression and classification
Create a linear regression model
Create a logistic regression classification model
Create a boosted trees classification model
Clustering
Cluster data with a k-means model
Recommendation
Create recommendations based on explicit feedback with a matrix factorization model
Create recommendations based on implicit feedback with a matrix factorization model
Anomaly detection
Anomaly detection with a multivariate time series
Imported and remote models
Make predictions with imported TensorFlow models
Make predictions with scikit-learn models in ONNX format
Make predictions with PyTorch models in ONNX format
Make predictions with remote models on Vertex AI
Hyperparameter tuning
Improve model performance with hyperparameter tuning
Export models
Export a BigQuery ML model for online prediction
Time series
Overview
End-to-end user journeys for forecasting models
Tutorials
Forecast a single time series with an ARIMA_PLUS univariate model
Forecast multiple time series with an ARIMA_PLUS univariate model
Forecast time series with a TimesFM univariate model
Scale an ARIMA_PLUS univariate model to millions of time series
Forecast a single time series with a multivariate model
Forecast multiple time series with a multivariate model
Use custom holidays with an ARIMA_PLUS univariate model
Limit forecasted values for an ARIMA_PLUS univariate model
Forecast hierarchical time series with an ARIMA_PLUS univariate model
Augmented analytics
Contribution analysis
Tutorials
Get data insights from contribution analysis using a summable metric
Get data insights from contribution analysis using a summable ratio metric
Work with models
List models
Manage models
Get model metadata
Update model metadata
Export models
Delete models
Reference patterns
Administer
Introduction
Manage resources
Organize resources
Understand reliability
Manage code assets
Manage data preparations
Manage notebooks
Manage saved queries
Manage pipelines
Manage tables
Manage tables
Manage table data
Modify table schemas
Restore deleted tables
Manage table clones
Introduction
Create table clones
Manage table snapshots
Introduction
Create table snapshots
Restore table snapshots
List table snapshots
View table snapshot metadata
Update table snapshot metadata
Delete table snapshots
Create periodic table snapshots
Manage configuration settings
Manage datasets
Manage datasets
Update dataset properties
Restore deleted datasets
Manage materialized views
Manage materialized view replicas
Schedule resources
Introduction
Schedule code assets