Big data

By:

Anil Maheswari

Material type: Text

TextPublication details: New Delhi McGraw Hill 2017Description: xv, 236p. 24 cm ; PbkISBN:

978-9352605026

Subject(s):

Data mining Big Data

DDC classification:

005 ANI

Online resources:

Click here to access online

Contents:

Chapter 1. Wholeness of Big Data Chapter 2. Big Data Sources and Applications Chapter 3. Big Data Architectures Chapter 4. Distributed Computing using Hadoop Chapter 5. Parallel Processing with MapReduce Chapter 6. NoSQL Databases Chapter 7. Stream Processing with Spark Chapter 8. Ingesting Data Chapter 9. Cloud Computing Chapter 10. Web Log Analyzer Application Case Study Chapter 11. Data Mining Primer Chapter 12 Big Data Programming Primer Appendix 1 – Installing Hadoop Using Cloudera on Virtual Box Appendix 2 – Installing Hadoop on Amazon Web Services , Elastic Computing Cluster Appendix 3 – Spark Installation & Tutorial

Summary: This book is written to meet the needs for an introductoryBig Data course. It is meant for students, as well as executives, who wish totake advantage of emerging opportunities in Big Data. It provides an intuitionof the wholeness of the field in a simple language, free from jargon and code.All the essential Big Data technology tools and platforms such as Hadoop,MapReduce, Spark, and NoSql are discussed. Most of the relevant programming details have been moved to Appendicesto ensure readability. The short chapters make it easy to quickly understandthe key concepts. A complete case study of developing a Big Data application isincluded. 1. Provides fun and insightful case-lets from real-world stories at the beginning of every chapter. IMB Watson Case Study, Google Flu 2. Provides a running case study across the chapters as exercises e.g. Google Query Architecture, How Google Search Works. 3. Shows clear learning objectives, review questions, and objective type questions 4. Covers end-to-end data processing chain, from generation of data to the consumption of data 5. Covers important topics like Hive, NoSQL, Data Mining Primer. 6. Dedicated Chapters on Data Mining and Big Data Programming, Appendices on Installation of Hadoop, Spark and Amazon Web Services.

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	H.T. Parekh Library	GSB Collection	005 ANI (Browse shelf(Opens below))	Available		B2438

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Chapter 1. Wholeness of Big Data
Chapter 2. Big Data Sources and Applications
Chapter 3. Big Data Architectures
Chapter 4. Distributed Computing using Hadoop
Chapter 5. Parallel Processing with MapReduce
Chapter 6. NoSQL Databases
Chapter 7. Stream Processing with Spark
Chapter 8. Ingesting Data
Chapter 9. Cloud Computing
Chapter 10. Web Log Analyzer Application Case Study
Chapter 11. Data Mining Primer
Chapter 12 Big Data Programming Primer
Appendix 1 – Installing Hadoop Using Cloudera on Virtual Box
Appendix 2 – Installing Hadoop on Amazon Web Services , Elastic Computing Cluster
Appendix 3 – Spark Installation & Tutorial

This book is written to meet the needs for an introductoryBig Data course. It is meant for students, as well as executives, who wish totake advantage of emerging opportunities in Big Data. It provides an intuitionof the wholeness of the field in a simple language, free from jargon and code.All the essential Big Data technology tools and platforms such as Hadoop,MapReduce, Spark, and NoSql are discussed. Most of the relevant programming details have been moved to Appendicesto ensure readability. The short chapters make it easy to quickly understandthe key concepts. A complete case study of developing a Big Data application isincluded.
1. Provides fun and insightful case-lets from real-world stories at the beginning of every chapter. IMB Watson Case Study, Google Flu
2. Provides a running case study across the chapters as exercises e.g. Google Query Architecture, How Google Search Works.
3. Shows clear learning objectives, review questions, and objective type questions
4. Covers end-to-end data processing chain, from generation of data to the consumption of data
5. Covers important topics like Hive, NoSQL, Data Mining Primer.
6. Dedicated Chapters on Data Mining and Big Data Programming, Appendices on Installation of Hadoop, Spark and Amazon Web Services.

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