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BigQuery Documentation Guide

Enable the BigQuery sandbox | Google Cloud

How does BigQuery work?​

Get started​

Quickstarts​

Try the Cloud console​

Try the command-line tool​

Explore BigQuery tools​

Migrate​

Migrate a data warehouse​

Migrate SQL​

Migration guides​

Amazon Redshift​

Apache Hive​

IBM Netezza​

Netezza is a data warehouse system that offers analytics, AI, and machine learning (ML) capabilities. It's a subsidiary of IBM, and is available on IBM Cloud, AWS, and Microsoft Azure.

Features​

  • Scalability: Scales up and down based on usage

  • Open formats: Supports open formats like Parquet and Iceberg for secure data sharing

  • In-database analytics: Allows users to run complex queries and build models directly in the database

  • Geospatial capabilities: Built-in geospatial capabilities for analyzing data

  • Solid-state disks: Data is stored on solid-state disks (SSDs) that are self-encrypting drives (SEDs)

  • Migrate from IBM Netezza

  • SQL translation reference

Oracle​

Snowflake​

Teradata​

Design​

Datasets​

Tables​

BigQuery tables​

External tables​

Views​

Logical views​

Materialized views​

Routines​

Connections​

Indexes​

Search indexes​

Vector indexes​

Load, transform, and export​

Load data​

BigQuery Data Transfer Service​

Batch load data​

Write and read data with the Storage API​

Transform data​

Prepare data​

Transform data with workflows​

Export data​

Analyze​

Explore your data​

Query BigQuery data​

Query data with SQL​

Use geospatial analytics​

Search data​

Work with queries​

Save queries​

Continuous queries​

Work with sessions​

Optimize queries​

Query external data sources​

Manage open source metadata​

Use external tables and datasets​

Run federated queries​

Use notebooks​

Use Colab notebooks​

Use DataFrames​

Use Jupyter notebooks​

Use analysis and BI tools​

Google Cloud Ready - BigQuery​

Share with Analytics Hub​

Entity resolution​

AI and machine learning​

Generative AI and pretrained models​

Choose generative AI and task-specific functions​

Generative AI​

Tutorials​

Task-specific solutions​

Tutorials​

Machine learning​

ML models and MLOps​

Use cases​

Tutorials​

Augmented analytics​

Tutorials​

Create and manage features​

Work with models​

Administer​

Manage resources​

Manage code assets​

Manage tables​

Manage table clones​

Manage table snapshots​

Orchestrate resources​

Orchestrate code assets​

Orchestrate jobs and queries​

Workload management​

Use reservations​

Manage jobs​

Legacy reservations​

Manage BI Engine​

Monitor workloads​

Optimize resources​

Control costs​

Optimize with recommendations​

Organize with labels​

Manage data quality​

Govern​

Control access to resources​

Control access with IAM​

Control access with authorization​

Restrict network access​

Control column and row access​

Control access to table columns​

Manage policy tags​

Control access to table rows​

Protect sensitive data​

Mask data in table columns​

Anonymize data with differential privacy​

Manage encryption​

Audit workloads​

Develop​

BigQuery API basics​

Authentication​