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BigQuery is a managed, serverless data warehouse product by Google, offering scalable analysis over large quantities of data. It is a Platform as a Service (PaaS) that supports querying using a dialect of SQL and Graph Query Language.^[1] It also has built-in machine learning capabilities. BigQuery was announced in May 2010 and made generally available in November 2011.^[2]

BigQuery originated from Google's internal Dremel technology,^[3][4] which enabled quick queries across trillions of rows of data.^[5] The product was originally announced in May 2010 at Google I/O.^[6] Initially, it was only usable by a limited number of external early adopters due to limitations on the API.^[5] However, after the product proved its potential, it was released for limited availability in 2011 and general availability in 2012.^[5] After general availability, BigQuery found success among a broad range of customers, including airlines, insurance, and retail organizations.^[5]

BigQuery requires all requests to be authenticated, supporting a number of Google-proprietary mechanisms as well as OAuth.

• Managing data - Create and delete objects such as tables, views, and user defined functions. Import data from Google Storage in formats such as CSV, Parquet, Avro or JSON.
• Query - Queries are expressed in a SQL dialect^[7] and the results are returned in JSON with a maximum reply length of approximately 128 MB, or an unlimited size when large query results are enabled.^[8]
• Integration - BigQuery can be used from Google Apps Script^[9] (e.g. as a bound script in Google Docs), or any language that can work with its REST API or client libraries.^[10]
• Access control - Share datasets with arbitrary individuals, groups, or the world.
• Machine learning - Create and execute machine learning models using SQL queries.

• Official website