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BigQuery
BigQuery
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BigQuery
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BigQuery

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]

Key Information

History

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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

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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[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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  1. ^ Iain Thomson (November 14, 2011). "Google opens BigQuery for cloud analytics: Dangles free trial to lure doubters". The Register. Retrieved August 26, 2016.
  2. ^ 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).
  3. ^ Kazunori Sato (2012). "An Inside Look at Google BigQuery" (PDF). Retrieved August 26, 2016.
  4. ^ a b c d Kwek, Ju-Kay. "BigQuery: the unlikely birth of a cloud juggernaut". Retrieved October 20, 2024.
  5. ^ "Google I/O 2010 - BigQuery and Prediction APIs".
  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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