BigQuery explained

BigQuery
Type:	Platform as a service data warehouse
Language:	English
Current Status:	Active
Registration:	Required
Owner:	Google

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

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. However, after the product proved its potential, it was released for limited availability in 2011 and general availability in 2012. After general availability, BigQuery found success among a broad range of customers, including airlines, insurance, and retail organizations.

Design

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

Features

• 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.

Notes and References

1. Web site: Google opens BigQuery for cloud analytics: Dangles free trial to lure doubters . Iain Thomson . . November 14, 2011 . August 26, 2016 .
2. Web site: 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. Web site: Kazunori Sato . 2012 . An Inside Look at Google BigQuery . August 26, 2016.
4. Web site: Kwek . Ju-Kay . BigQuery: the unlikely birth of a cloud juggernaut . October 20, 2024.
5. Web site: Google I/O 2010 - BigQuery and Prediction APIs .
6. Web site: SQL Reference. 26 June 2017.
7. Web site: Quota Policy. 26 June 2017.
8. Web site: BigQuery Service | Apps Script | Google Developers . March 15, 2018 . April 23, 2018 .
9. Web site: BigQuery Client Libraries. 26 June 2017.