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BigQuery

From Wikipedia, the free encyclopedia
BigQuery
Type of site
Platform as a service data warehouse
Available inEnglish
OwnerGoogle
URLcloud.google.com/bigquery
RegistrationRequired
LaunchedMay 19, 2010; 15 years ago (2010-05-19)
Current statusActive

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]

History

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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]

Design

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

Features

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  • 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.

References

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  1. ^ Saqib Ali (February 25, 2026). "Support for GQL is coming to Google BigQuery!". GQL. Retrieved March 4, 2026.
  2. ^ Iain Thomson (November 14, 2011). "Google opens BigQuery for cloud analytics: Dangles free trial to lure doubters". The Register. Retrieved August 26, 2016.
  3. ^ Sergey Melnik; Andrey Gubarev; Jing Jing Long; Geoffrey Romer; Shiva Shivakumar; Matt Tolton; Theo Vassilakis (2010). "Dremel: Interactive Analysis of Web-Scale Datasets". Proc. of the 36th International Conference on Very Large Data Bases (VLDB).
  4. ^ Kazunori Sato (2012). "An Inside Look at Google BigQuery" (PDF). Retrieved August 26, 2016.
  5. ^ a b c d Kwek, Ju-Kay. "BigQuery: the unlikely birth of a cloud juggernaut". Archived from the original on October 22, 2024. Retrieved October 20, 2024.
  6. ^ "Google I/O 2010 - BigQuery and Prediction APIs". YouTube. 26 May 2010.
  7. ^ "SQL Reference". Retrieved 26 June 2017.
  8. ^ "Quota Policy". Archived from the original on 15 July 2017. Retrieved 26 June 2017.
  9. ^ "BigQuery Service | Apps Script | Google Developers". March 15, 2018. Retrieved April 23, 2018.
  10. ^ "BigQuery Client Libraries". Retrieved 26 June 2017.
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