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Use Cloud BigQuery to run super-fast, SQL-like queries against append-only tables. BigQuery makes it easy to:

  • Control who can view and query your data.

  • Use a variety of third-party tools to access data on BigQuery, such as tools that load or visualize your data.

  • Use datasets to organize and control access to tables, and construct jobs for BigQuery to execute (load, export, query, or copy data).

Find BigQuery in the left side menu of the Google Cloud Platform Console, under Big Data.

Get started
  1. Open your project in the console.

    If you're new to the console, you may need to sign up for a Google account, access the console, and create a project.

  2. Find BigQuery in the left side menu of the console, under Big Data.

    Note these two requirements:

    • BigQuery API: New projects automatically enable the BigQuery API. If an existing project doesn't have BigQuery enabled, open the left side menu, click APIs & services, and then find and enable the BigQuery API.

    • Billing: BigQuery offers a free tier for queries, but you must enable billing to use other operations.

  3. Try any of these quickstarts to learn how to query, load, and export data in BigQuery.

  4. See developer resources below for more information on loading, querying, and exporting data, as well as access control, API use, and other tools and solutions.

Developer resources

Load, query, and export data

  • Load data: Learn how to prepare data for BigQuery, bulk load data with a job, or stream records into BigQuery individually.

  • Query data: Learn to run synchronous and asynchronous queries from the BigQuery API.

  • Export data: Learn to export data from BigQuery into several formats. BigQuery can export up to 1 GB of data per file, and supports exporting to multiple files.

BigQuery API

  • BigQuery client libraries: Check out helper libraries, samples, and scripts that you can use to access the Google BigQuery API in different languages.

Tools and solutions


  • Support overview: Learn where to ask questions, get a support package, and file bugs.

  • Troubleshoot: Learn to troubleshoot HTTP error codes or job errors when working with BigQuery.

  • Launch checklist: Review recommended activities to complete before launching a commercial application that uses BigQuery.

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