Databases Reference
In-Depth Information
data from all the different systems is loaded into the data warehouse, making it extremely complex to
navigate through. It does not matter whether you have a star schema or third normal form (3NF)—the
problem transcends all the underlying RDBMS. to make the data warehouse scalable and fit, there are
multiple techniques that can be utilized with the options available from the modern architectures.
Choices for reengineering the data warehouse
The most popular and proven options to reengineer or modernize the data warehouse are discussed in
the following sections. (
Note:
We will discuss cloud computing, data virtualization, data warehouse
appliance, and in-memory in Chapter 9, after we discuss workload dynamics in Chapter 8.)
Replatforming
A very popular option is to replatform the data warehouse to a new platform including all hardware
and infrastructure. There are several new technology options in this realm, and depending on the
requirement of the organization, any of these technologies can be deployed. The choices include data
warehouse appliances, commodity platforms, tiered storage, private cloud, and in-memory technolo-
gies. There are benefits and disadvantages to this exercise.
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Benefits:
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Replatforming provides an opportunity to move the data warehouse to a scalable and reliable
platform.
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The underlying infrastructure and the associated application software layers can be architected
to provide security, lower maintenance, and increase reliability.
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The replatform exercise will provide us an opportunity to optimize the application and
database code.
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The replatform exercise will provide some additional opportunities to use new functionality.
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Replatforming also makes it possible to rearchitect things in a different/better way, which is
almost impossible to do in an existing setup.
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Disadvantages:
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Replatforming takes a long cycle time to complete, leading to disruption of business activities,
especially in large enterprises and enterprises that have traditional business cycles based on
waterfall techniques. One can argue that this can be planned and addressed to not cause any
interruption to business, but this seldom happens in reality with all the possible planning.
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Replatforming often means reverse engineering complex business processes and rules that
may be undocumented or custom developed in the current platform. These risks are often not
considered during the decision-making phase to replatform.
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Replatforming may not be feasible for certain aspects of data processing or there may be
complex calculations that need to be rewritten if they cannot be directly supported by the
functionality of the new platform. This is especially true in cross-platform situations.
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Replatforming is not economical in environments that have large legacy platforms, as it
consumes too many business process cycles to reverse engineer logic and documenting the same.
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Replatforming is not economical when you cannot convert from daily batch processing to
microbatch cycles of processing.
