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DeutschBigQuery
 | 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]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]BigQuery requires all requests to be authenticated, supporting a number of Google-proprietary mechanisms as well as OAuth.
Features
[edit]- 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
[edit]- ^ Iain Thomson (November 14, 2011). "Google opens BigQuery for cloud analytics: Dangles free trial to lure doubters". The Register. Retrieved August 26, 2016.
- ^ 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).
- ^ Kazunori Sato (2012). "An Inside Look at Google BigQuery" (PDF). Retrieved August 26, 2016.
- ^ a b c d Kwek, Ju-Kay. "BigQuery: the unlikely birth of a cloud juggernaut". Retrieved October 20, 2024.
- ^ "Google I/O 2010 - BigQuery and Prediction APIs".
- ^ "SQL Reference". Retrieved 26 June 2017.
- ^ "Quota Policy". Retrieved 26 June 2017.
- ^ "BigQuery Service | Apps Script | Google Developers". March 15, 2018. Retrieved April 23, 2018.
- ^ "BigQuery Client Libraries". Retrieved 26 June 2017.
External links
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