Big Data Systems: A 360-degree Approach

Book Description Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples.
Key Features:

• Introduces concepts and evolution of Big Data technology.
• Illustrates examples for thorough understanding.
• Contains programming examples for hands on development.
• Explains a variety of topics including NoSQL Systems, NewSQL systems, Security, Privacy, Networking, Cloud, High Performance Computing, and Deep Learning.
• Exemplifies widely used big data technologies such as Hadoop and Spark.
• Includes discussion on case studies and open issues.
• Provides end of chapter questions for enhanced learning.

Content

Section I - Introduction
Chapter 1. Introduction to Big Data Systems
Chapter 2. Architecture and Organization of Big Data Systems
Chapter 3. Cloud Computing for Big Data

Section II - Storage and Processing for Big Data
Chapter 4. HADOOP: An Efficient Platform for Storing and Processing Big Data
Chapter 5. Enhancements in Hadoop
Chapter 6. Spark
Chapter 7. NoSQL Systems
Chapter 8. NewSQL Systems

Section III - Networking, Security, and Privacy for Big Data
Chapter 9. Networking for Big Data
Chapter 10. Security for Big Data
Chapter 11. Privacy for Big Data

Section IV - Computation for Big Data
Chapter 12. High Performance Computing for Big Data
Chapter 13. Deep Learning with Big Data

Section V - Case Studies and Future Trends
Chapter 14. Big dаta: Case Studies and Future Trends