Big Data Made Accessible: 2020 edition

This books fills the need for an easy and holistic book on essential Big Data technologies. Written in a lucid and simple language free from jargon and code, this book provides an intuition for Big Data from business as well as technological perspectives. This book is designed to provide the reader with the intuition behind this evolving area, along with a solid toolset of the major big data processing technologies such as Hadoop, MapReduce, Spark Streaming, and NoSql databases. A complete case study of developing a web log analyzer is included. The book also contains two primers on Cloud computing and Data Mining. It also contains two tutorials on installing Hadoop and Spark. The book contains case-lets from real-world stories.

The 2019 edition includes four new chapters. These are full primers Data Modeling, Data Analytics, Artificial Intelligence, and Data Science Careers.

Students across a variety of academic disciplines including business, computer science, statistics, engineering, and others attracted to the idea of harnessing Big Data for new insights and ideas from data, can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make the most of Big Data to monitor their infrastructure, discover new insights, and develop new data-based products. It is a flowing book that one can finish in one sitting, or one can return to it again and again for insights and techniques.

Table of Contents

Chapter 1.Wholeness of Big Data
Chapter 2: Big Data Applications
Chapter 3: Big Data Architectures
Chapter 4: Distributed Systems with Hadoop
Chapter 5: Parallel Programming with MapReduce
Chapter 6: Advanced NoSQL databases
Chapter 7: Stream programming with Spark
Chapter 8:Data Ingest with Kafka
Chapter 9:Cloud Computing Primer
Chapter 10: Web Log Analyzer development
Chapter 11: Big Data Programming Primer
Chapter 12: Data Modeling Primer
Chapter 13: Data Analytics Primer
Chapter 14: Artificial Intelligence Primer
Chapter 15: Data Ownership and Privacy
Chapter 16: Data Science Careers
Appendix 1 on Installing Hadoop on Linux
Appendix 2 on Installing Hadoop on AWS cloud
Appendix 3 on Installing and Running Spark