BigQuery

BigQuery

BigQuery

Cloud-based data warehouse service


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]

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Design

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 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.
  • Cross-cloud analytics - Analyze data across Google Cloud, Amazon Web Services, and Microsoft Azure[8][9]
  • Data sharing - Exchange data and analytics assets across organizational boundaries.[10]
  • In-Memory analysis service - BI Engine built into BigQuery that enables users to analyze large and complex datasets interactively with sub-second query response time and high concurrency.[11][12]
  • Business intelligence - Visualize data from BigQuery by importing into Data Studio, a data visualization tool [13]

Pricing

The two main components of BigQuery pricing are the cost to process queries and the cost to store data. BigQuery offers two types of pricing - on demand pricing which charges for the number of petabytes processed for each query and flat-rate pricing which charges for slots or virtual CPUs.[14]

Partnerships & integrations

BigQuery partners and natively integrates with several tools:[15]

Adoption

Customers of BigQuery include 20th Century Fox, American Eagle Outfitters, HSBC, CNA Insurance, Asahi Group, ATB Financial, Athena, The Home Depot, Wayfair, Carrefour, Oscar Health, and several others.[16] Gartner named Google as a Leader in the 2021 Magic Quadrantâ„¢ for Cloud Database Management Systems.[17] BigQuery is also named a Leader in The 2021 Forrester Wave: Cloud Data Warehouse.[18] According to a study by Enterprise Strategy Group, BigQuery saves up to 27% in total cost of ownership over three years compared to other cloud data warehousing solutions.[19]


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. "SQL Reference". Retrieved 26 June 2017.
  5. "Quota Policy". Retrieved 26 June 2017.
  6. "BigQuery Service | Apps Script | Google Developers". March 15, 2018. Retrieved April 23, 2018.
  7. "BigQuery Client Libraries". Retrieved 26 June 2017.
  8. "BigQuery Costs". 13 July 2023.

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