Agile Data Warehousing

Agile Data Warehousing

ü  DWBI projects are undoubtedly among the principal, most complex applications that many corporations own. In practice, agile "straight out of the box" cannot achieve their breadth and depth.

ü  The experiment of breadth arises from many warehouses' protracted data processing chains with components that vary widely in the type of modules being constructed.

ü  The experiment of depth happens when one or more of these modules become a conflicting data definitions, complex business rules, and high data volumes.

ü  Teams will need Scrum's adaptive nature to fashion a method for their project that can surmount these twin challenges.

For most dashboard work, programmers can operate within a prepared framework of data structures and semantics and need only build a single application to achieve their desired result. Data integration work, however, requires four or more modules to achieve anything usable. Typically it must traverse several distinct architectural layers, each dedicated to a particular task, including

ü  extracting necessary data from source systems

ü  purging it of errors and standardizing its formatting

ü  assimilating the cleansed records with data from other source systems

ü  transforming integrated records into dimensional structures for easy data exploration

 

 


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