Datawarehouse Design
There are two
major approaches to data warehouse design.
1.
Bottom-up approach
• This approach
is recommended by Kimball.
• In the
bottom-up approach data marts are first created to provide reporting and
analytical capabilities for specific business processes.
• Data marts
contain, primarily, dimensions and facts. Facts can contain either atomic data
and, if necessary, summarized data. The single data mart often models a
specific business area such as Sales or Production.
• These data
marts can eventually be integrated to create a comprehensive data warehouse.
• The integration
of the data marts in the data warehouse is centered on the conformed
dimensions.
• The actual
integration of two or more data marts is then done by a process known as
"Drill across". A drill-across works by grouping (summarizing) the
data along the keys of the (shared) conformed dimensions of each fact
participating in the "drill across" followed by a join on the keys of
these grouped (summarized) facts.
• Some consider
it an advantage of the Kimball method, that the data warehouse ends up being
"segmented" into a number of logically self contained and consistent
data marts, rather than a big and often complex centralized model.
• Business value
can be returned as quickly as the first data mart is built.
2.
Top-down approach
• This approach
is recommended by Bill Inmon.
• Inmon is one of
the leading proponents of the top-down approach to data warehouse design, in
which the data warehouse is designed using a normalized enterprise data model.
• In the Inmon
vision the data warehouse is at the center of the "Corporate Information
Factory" (CIF), which provides a logical framework for delivering business
intelligence (BI) and business management capabilities.
• The top-down
design methodology generates highly consistent dimensional views of data across
data marts since all data marts are loaded from the centralized repository.
• Generating new
dimensional data marts against the data stored in the data warehouse is a relatively
simple task.
• The main
disadvantage to the top-down methodology is that it represents a very large
project with a very broad scope, cost and time.
• In addition,
the top-down methodology can be inflexible and unresponsive to changing
departmental needs during the implementation phases.
nice
ReplyDeleteA new learning I've learned today about the datawarehouse design. The approaches shared in here can be very useful which are so illuminating. Thanks for this post. This is great and interesting.
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