BigQuery

BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a serverless Software as a Service (SaaS) that supports querying using ANSI SQL. It also has built-in machine learning capabilities.

BigQuery
Type of site
Software as a service data warehouse
Available inEnglish
OwnerGoogle
URLcloud.google.com/products/bigquery/
RegistrationRequired
LaunchedMay 19, 2010 (2010-05-19)
Current statusActive

History

After a limited testing period in 2010, BigQuery was generally available in November 2011 at the Google Atmosphere conference.[1]

In April 2016, European users of the service suffered a 12-hour outage.[2] In May 2016, support was announced for Google Sheets.[3]

Design

BigQuery provides external access to the Dremel technology,[4][5] a scalable, interactive ad hoc query system for analysis of read-only nested data. BigQuery requires all requests to be authenticated, supporting a number of Google-proprietary mechanisms as well as OAuth.

Features
  • Managing data - create and delete tables based on a JSON-encoded schema, import data encoded as CSV or JSON from Google Storage.
  • Query - the queries are expressed in a standard 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 - is possible to share datasets with arbitrary individuals, groups, or the world.
  • Machine learning
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References
  1. Iain Thomson (November 14, 2011). "Google opens BigQuery for cloud analytics: Dangles free trial to lure doubters". Retrieved August 26, 2016.
  2. Simon Sharwood (April 7, 2016). "Google Euro-cloud glitch". Retrieved August 26, 2016.
  3. Jordan Novet (May 6, 2016). "Google BigQuery now lets you analyze data from Google Sheets". Retrieved August 26, 2016.
  4. 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).
  5. Kazunori Sato (2012). "An Inside Look at Google BigQuery" (PDF). Google. Retrieved August 26, 2016.
  6. "SQL Reference". Retrieved 26 June 2017.
  7. "Quota Policy". Retrieved 26 June 2017.
  8. "BigQuery Service | Apps Script | Google Developers". March 15, 2018. Retrieved April 23, 2018.
  9. "BigQuery Client Libraries". Retrieved 26 June 2017.
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