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To be effective, data-intensive systems require extensive ongoing
customisation to reflect changing user requirements, organisational
policies, and the structure and interpretation of the data they
hold. Manual customisation is expensive, time-consuming, and
error-prone. In large complex systems, the value of the data can be
such that exhaustive testing is necessary before any new feature
can be added to the existing design. In most cases, the precise
details of requirements, policies and data will change during the
lifetime of the system, forcing a choice between expensive
modification and continued operation with an inefficient design.
Engineering Agile Big-Data Systems outlines an approach to dealing
with these problems in software and data engineering, describing a
methodology for aligning these processes throughout product
lifecycles. It discusses tools which can be used to achieve these
goals, and, in a number of case studies, shows how the tools and
methodology have been used to improve a variety of academic and
business systems.
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Nadine Gordimer
Paperback
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R398
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Discovery Miles 3 300
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