Abstract
This book is about designing and implementing software systems that ingest, analyze, and visualize big data sets. Throughout the book, we’ll use the acronym BDA or BDAs (big data analytics system) to describe this kind of software. Big data itself deserves a word of explanation. As computer programmers and architects, we know that what we now call “big data” has been with us for a very long time—decades, in fact, because “big data” has always been a relative, multi-dimensional term, a space which is not defined by the mere size of the data alone. Complexity, speed, veracity—and of course, size and volume of data—are all dimensions of any modern “big data set”.
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Notes
- 1.
One of the best introductions to the “semantic web” approach is Dean Allemang and Jim Hendler’s “Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL”, 2008, Morgan-Kaufmann/Elsevier Publishing, Burlington, MA. ISBN 978-0-12-373556-0.
- 2.
The go-to book on Enterprise Integration Patterns (EIPs) is Gregor Hohpe and Bobby Woolf’s Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions, 2004, Pearson Education Inc. Boston, MA. ISBN 0-321-20068-3.
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© 2017 Kerry Koitzsch
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Koitzsch, K. (2017). Overview: Building Data Analytic Systems with Hadoop. In: Pro Hadoop Data Analytics . Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-1910-2_1
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DOI: https://doi.org/10.1007/978-1-4842-1910-2_1
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Publisher Name: Apress, Berkeley, CA
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