BigQuery
 | This article may rely excessively on sources too closely associated with the subject, potentially preventing the article from being verifiable and neutral. (May 2023) |
Type of site | Platform as a service data warehouse |
|---|---|
| Available in | English |
| Owner | |
| URL | cloud |
| Registration | Required |
| Launched | May 19, 2010 |
| Current status | Active |
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. It also has built-in machine learning capabilities. BigQuery was announced in May 2010 and made generally available in November 2011.[1]
History
[edit | edit source]Bigquery originated from Google's internal Dremel technology,[2][3] which enabled quick queries across trillions of rows of data.[4] The product was originally announced in May 2010 at Google I/O.[5] Initially, it was only usable by a limited number of external early adopters due to limitations on the API.[4] However, after the product proved its potential, it was released for limited availability in 2011 and general availability in 2012.[4] After general availability, BigQuery found success among a broad range of customers, including airlines, insurance, and retail organizations.[4]
Design
[edit | edit source]BigQuery requires all requests to be authenticated, supporting a number of Google-proprietary mechanisms as well as OAuth.
Features
[edit | edit source]- 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[6] 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.[7]
- Integration - BigQuery can be used from Google Apps Script[8] (e.g. as a bound script in Google Docs), or any language that can work with its REST API or client libraries.[9]
- Access control - Share datasets with arbitrary individuals, groups, or the world.
- Machine learning - Create and execute machine learning models using SQL queries.
References
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- ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
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- ^ Lua error in Module:Citation/CS1/Configuration at line 2172: attempt to index field '?' (a nil value).
