BigQuery is a fully-managed, serverlessdata warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service (PaaS) that supports querying using ANSI SQL. It also has built-in machine learning capabilities. BigQuery was announced in May 2010 and made generally available in November 2011.[1]
BigQuery provides external access to Google's Dremel technology,[2][3] a scalable, interactive ad hoc query system for analysis of 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 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 standard SQL dialect[4] 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.[5]
Integration - BigQuery can be used from Google Apps Script[6] (e.g. as a bound script in Google Docs), or any language that can work with its REST API or client libraries.[7]
Access control - Share datasets with arbitrary individuals, groups, or the world.
Machine learning - Create and execute machine learning models using SQL queries.
^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).
