# Definição “A **data warehouse** is a storage architecture designed to hold data extracted from transaction systems, operational data stores and external sources. The warehouse then combines that data in an aggregate, summary form suitable for enterprise-wide data analysis and reporting for predefined business needs.” The five components of a data warehouse are: - production data sources - data extraction and conversion - the data warehouse database management system - data warehouse administration - business intelligence (BI) tools Gartner IT Glossary [Link](https://www.gartner.com/it-glossary/data-warehouse/) --- “O que é um **data warehouse**? Um data warehouse é um repositório central de informações que pode ser analisado para tomar decisões mais embasadas. Os dados fluem de sistemas transacionais, bancos de dados relacionais e de outras fontes para o data warehouse, normalmente com uma cadência regular. Analistas de negócios, cientistas de dados e tomadores de decisões acessam os dados por meio de ferramentas de inteligência de negócios (BI), clientes SQL e outros aplicativos de análise.” **Conceitos de Data Warehouse** Amazon Web Services, Inc [Link](https://aws.amazon.com/pt/data-warehouse/) --- # Referências Bibliográficas **The Kimball Group Reader** **Relentlessly Practical Tools for Data Warehousing and Business Intelligence –** **Remastered Collection** Ralph Kimball Margy Ross Bob Becker Joy Mundy Warren Thornthwaite Wiley 2016 **Data Warehouse Systems** **Design and Implementation** Alejandro Vaisman Esteban Zim´anyi Springer 2014 **Data Warehouse Design** **Modern Principles and Methodologies** Matteo Golfarelli Stefano Rizzi McGraw Hill 2009 --- # Referências Importantes **Kimball Group** [Link](https://www.kimballgroup.com/) **Data Warehouse and Business Intelligence Resources** **Ralph Kimball** [Link](http://www.kimballgroup.com/data-warehouse-business-intelligence-resources/) **Modern BI Platforms: The Role of the Data Warehouse and Semantic Models** Cindi Howson [Link](https://blogs.gartner.com/cindi-howson/2015/11/19/modernbi/) **Data Warehousing Makes a Comeback** **Data warehousing is not dead, but it is changing as new technologies, including Hadoop and Cloud Platforms, have an impact** David Stodder 2018 [Link](https://tdwi.org/Articles/2018/11/08/DWT-ALL-Data-Warehousing-Makes-a-Comeback.aspx) **Long Live the Traditional Data Warehouse** **A data warehouse is more than a storage repository. Don’t lose sight of the benefits a traditional warehouse provides** Mike Schiff 2018 [Link](https://tdwi.org/articles/2018/04/09/dwt-all-long-live-traditional-data-warehouse.aspx) **Data Warehousing and Data Mart** Microsoft Zoiner Tejada Hiroki Christopher Bennage Mike Wasson 2018 [Link](https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/data-warehousing) **Data Lakes Are Cool, But You Still Need A Data Warehouse** Henry Eckerson 2018 [Link](https://www.eckerson.com/articles/data-lakes-are-cool-but-you-still-need-a-data-warehouse) **Evolving the Data Warehouse** **The classic data warehouse architecture is in need of a retrofit. It must be updated to support a real-time, data-in-motion paradigm** Steve Swoyer 2017 [Link](https://tdwi.org/articles/2017/04/10/evolving-the-data-warehouse.aspx) **SAP SQL Data Warehousing with HANA** Klaus-Peter Sauer SAP 2017 [Link](https://blogs.sap.com/2017/12/19/sap-sql-data-warehousing-with-hana/) --- # Trilha [[Orientação por Dados]] [[Agile Data Warehouse]] [[Modelagem Dimensional]] [[Dimensional Fact Model]] [[Data Warehouse in the Cloud]]