Access "Applying agile methods to data warehouse projects"
This article is part of the Issue 1 January 2012 issue of Big Data: Separating the hype from the reality
Rapidly gaining in popularity, the Agile approach to data warehousing solves many of the thorny problems typically associated with data warehouse development—most notably high costs, low user adoption, ever-changing business requirements and the inability to rapidly adapt as business conditions change. The Agile approach can be used to develop any analytical database, so let’s begin with two familiar definitions: A Data Warehouse (DW) is simply a database that contains integrated and homogenized information from one or more sources brought together to support analysis and reporting. These sources can be your internal online transactional processing (OLTP) systems such as finance, accounting, sales, marketing, payroll, supply chain, etc., or external sources such as supplier files, purchased marketing lists, Facebook, Twitter or census data, etc. In addition to the data warehouse, you may also be using additional types of databases for analysis and reporting. The most common types include data marts and operational data stores (ODSs). Business Intelligence... Access >>>
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The hype vs. the reality of big data
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The air is thin at the top of the hype curve, so breathe deeply as we explore the reality of big data—and the changes it entails for BI and data warehousing systems.
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Applying agile methods to data warehouse projects
by Jim Gallo
Agile development processes can take a lot of the pain out of building data warehouses and enable project teams to deliver functionality, and business value, on a rolling basis.
- Applying agile methods to data warehouse projects by Jim Gallo
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