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by Nasir Raheem (Author)
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
Author Biography
Nasir Raheem is an accomplished, innovative, and results-driven project manager, architect and business analyst with over 20 years of wide-ranging experience encompassing I.T Infra-structure design, planning and implementation of highly integrated systems that included Big Data (HIVE) Database Administration, Business Re-engineering, Asset & Data management (ServiceNow), Data Integration, Data Modeling, Disaster Recovery and ERP Database /Application cloning projects. He is an experienced manager of IT projects related to multi-billion dollar corporate mergers, migration, server upgrades, database upgrades, data conversion, cloning and integration of supply chain management ERP and CRM application modules at Wells Fargo Bank, WebTV (now Microsoft) and Hitachi Global Storage Technologies (now Western Digital). He is also a published author and instructor of an online course approved by Harvard University Innovation Lab, 'March towards Big Data.'
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