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MERCURY LEARNING and INFORMATION provides print and digital content in the areas of science and medicine, technology and computing, engineering, and mathematics (STEM disciplines) designed for the professional/reference, trade, library, higher education, career school, and online training markets. We embrace both traditional and innovative publishing models designed to accommodate the needs of the intended audience by using the appropriate delivery methods.
Big Data Using Hadoop and Hive
ISBN: 9781683926450
Pub Date: March 2021
Specs: 7 x 9 Paperback
Pages: 250
Price: $54.95
This book is the basic guide for developers, architects, engineers, and anyone who wants to start leveraging the open-source software Hadoop and Hive to build distributed, scalable concurrent big data applications. Hive will be used for reading, writing, and managing the large, data set files. The book is a concise guide on getting started with an overall understanding on Apache Hadoop and Hive and how they work together to speed up development with minimal effort. It will refer to simple concepts and examples, as they are likely to be the best teaching aids. It will explain the logic, code, and configurations needed to build a successful, distributed, concurrent application, as well as the reason behind those decisions.
FEATURES
- Shows how to leverage the open-source software Hadoop and Hive to build distributed, scalable, concurrent big data applications
- Includes material on Hive architecture with various storage types and the Hive query language
- Features a chapter on big data and how Hadoop can be used to solve the changes around it
- Explains the basic Hadoop setup, configuration, and optimization
BRIEF TOC
1: Big Data. 2: What Is Apache Hadoop? 3: The Hadoop Distribution File System.4: Getting Started with Hadoop. 5: Interfaces to Access HDFS Files. 6: Yet Another Resource Negotiator. 7: MapReduce. 8: Hive. 9: Getting Started with Hive. 10: File Format. 11: Data Compression. Index.
ABOUT THE AUTHOR
Nitin Kumar has 18+ years of overall IT experience with technical specialty in architecture, systems analysis, design, performance tuning, and execution on a Distributed Parallel Processing system. He has published books and papers with special focus on Agile, Big Data, streaming, Java, and re-factoring.