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