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.

Points to Remember

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.

CONTENT:
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