Big Data Made Accessible: 2020 edition

by Anil Maheshwari

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

332 pages, Kindle Edition

Published June 28, 2016

Reviews

[Keith Wood · Rating 4 out of 5]

Great book for new Data Analysts and Data Scientists

Was tasked with finding books to add to our onboarding process for external hires. While the software overviews are good, the useful parts involve the data analytics processes and some best practices. The AI and MLL section is somewhat dated, it does give a good base of knowledge for understanding the basic concepts.