BigQuery

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.

   • BigQuery web UI quickstart: The BigQuery web UI is a visual interface for BigQuery tasks. 

   • bq command-line tool: The bq command-line tool is a python-based tool that accesses BigQuery from the command line.

   • BigQuery API quickstart: Learn to build command-line applications that that access the BigQuery API.

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.

• BigQuery API authorization: Learn to authorize access to the BigQuery API in various application scenarios. The BigQuery API requires all requests to be authorized by an authenticated user or a service account.

• BigQuery API basics: Learn how to manage jobs, datasets, and projects, manage tables, and make batch requests.

• API reference: Check out reference guides to API datasets, jobs, projects, tabledata, and tables.

Tools and solutions

• BigQuery web UI: The BigQuery web UI is a visual interface that helps you complete tasks like running queries, loading data, and exporting data. (See also the web UI quickstart.)

• bq command-line tool quickstart: The bq command-line tool is a python-based tool that accesses BigQuery from the command line. (See also the command-line tool quickstart.)

• BigQuery connector for Excel: Google BigQuery offers a connector for Excel that allows you to make queries to Google BigQuery from within Excel. This can be useful if you consistently use Excel to manage your data. 

• Third-party tools and services: Check out a sampling of industry-leading tools and services for loading, transforming, and visualizing data. They are provided by Google Cloud Platform Technology Partners.

Support

• 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.