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By:W.H. Inmon,Daniel Linstedt,Mary Levins
Published on 2019-04-30 by Academic Press
Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the |bigger picture| and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture
This Book was ranked at 12 by Google Books for keyword best architecture books 2019.
Book ID of Data Architecture: A Primer for the Data Scientist's Books is 2_aVDwAAQBAJ, Book which was written byW.H. Inmon,Daniel Linstedt,Mary Levinshave ETAG "pCn8xM9YykU"
Book which was published by Academic Press since 2019-04-30 have ISBNs, ISBN 13 Code is 9780128169179 and ISBN 10 Code is 0128169176
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