BigQuery

In today's world, BigQuery has become a topic of great relevance and interest to a wide range of individuals. Whether due to its impact on society, its influence on popular culture or its importance in the scientific field, BigQuery has managed to capture the attention of people of all ages and backgrounds. In this article, we will explore the different aspects related to BigQuery, analyzing its evolution over time, its impact today, and the possible implications that its presence has in the future. Through a multidisciplinary approach, we will seek to offer a comprehensive view of BigQuery, providing the reader with a complete and enriching overview of this fascinating topic.

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

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.[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

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.

References

  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.