Read Now Big Data Made Accessible: 2020 edition Kindle

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 ContentsChapter 1.Wholeness of Big DataChapter 2: Big Data ApplicationsChapter 3: Big Data ArchitecturesChapter 4: Distributed Systems with HadoopChapter 5: Parallel Programming with MapReduceChapter 6: Advanced NoSQL databasesChapter 7: Stream programming with SparkChapter 8:Data Ingest with KafkaChapter 9:Cloud Computing PrimerChapter 10: Web Log Analyzer development Chapter 11: Big Data Programming PrimerChapter 12: Data Modeling PrimerChapter 13: Data Analytics PrimerChapter 14: Artificial Intelligence PrimerChapter 15: Data Ownership and PrivacyChapter 16: Data Science CareersAppendix 1 on Installing Hadoop on LinuxAppendix 2 on Installing Hadoop on AWS cloudAppendix 3 on Installing and Running Spark