Databases Reference
In-Depth Information
CHAPTER
7
Reengineering the Data
Warehouse
Simplicity is prerequisite for reliability.
—Edsger W. Dijkstra
INTRODUCTION
In Chapter 6 we discussed the different generations of data warehousing, the challenges of the data
warehouse, and some modern thoughts on evolving the data warehouse. Earlier chapters have pro-
vided us with an in-depth look at Big Data, and its challenges, problems, and solutions. The big
questions that stare at us at this juncture are: What will the data warehouse evolve to in the new
architecture landscape? Is there even room for a data warehouse? Is there a future for the current
investment? This chapter provides us an introduction to reengineering the data warehouse or modern-
izing the data warehouse. This is the foundational step in building the new architecture for the next-
generation data warehouse.
The data warehouse has been built on technologies that have been around for over 30 years and
infrastructure that is at least three generations old, compared to the advancements in the current-state
infrastructure and platform options. There are several opportunities that are being missed by organiza-
tions, including:
●
Gaining competitive advantage
●
Reducing operational and financial risks
●
Increasing revenue
●
Optimizing core business efficiencies
●
Analyzing and predicting trends and behaviors
●
Managing brand presence, channels, and reputation
●
Managing customer expectations proactively
One can argue that an enterprise data warehouse is not responsible for many of these types of
insights, true and false. The promise of the data warehouse is an enterprise repository for data and a
platform for the single version of truth. The issue, however, is due to complexities of users, processes,
and data itself. The data warehouse performance has eroded over time steadily and degraded to the
extent that it needs to be modernized by a combination of infrastructure and software upgrades to
ensure that its performance can be sustained and maintained.
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