Big data: a tutorial-based approach
Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the 'What', 'How', and 'Why' of Big Data. Featu…
Read More ↓Title Statements
- Full title :Big data : a tutorial-based approach / Nasir Raheem.
Summary
Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the 'What', 'How', and 'Why' of Big Data. Features Identifies the primary drivers of Big Data Walks readers through the theory, methods and technology of Big Data Explains how to handle the 4 V's of Big Data in order to extract value for better business decision making Shows how and why data connectors are critical and necessary for Agile text analytics Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases.
Read More ↓Edition Statements
- Publication Date:2020
- Edition:First issued in paperback
Publication Statements
- Publisher(s):CRC Press, Taylor & Francis Group
- Place of Publication:Boca Raton
- Language:eng
- Publication Date:2020
Notes
Literaturverzeichnis: Seite 163-165.
The Physical Object
- Description:xxv, 175 Seiten : Illustrationen, Diagramme
Edition Identifiers
- ISBN:9780367670245, 0367670240, 9780367183455, 0367183455
- oclc:on1244531231
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