Big data and analytics is an indispensable guide that navigates the complex data management and analysis. This comprehensive book covers the core principles, processes, and tools, ensuring readers grasp the essentials and progress to advanced applications. It will help you understand the different analysis types like descriptive, predictive, and prescriptive. Learn about NoSQL databases and their benefits over SQL. The book centers on Hadoop, explaining its features, versions, and main components like HDFS (storage) and MapReduce (processing). Explore MapReduce and YARN for efficient data processing. Gain insights into MongoDB and Hive, popular tools in the big data landscape.

• Cover
• Title Page
• Copyright Page
• Dedication Page
• About the Authors
• About the Reviewer
• Acknowledgements
• Preface
• Table of Contents
• 1. Introduction to Big Data
  • Introduction
  • Structure
  • Diverse facets of big data
  • Digital data and its types
  • Characteristics of big data
  • Types of big data
    • Structured data
    • Unstructured data
    • Semi-structured data
  • Evolution of big data
  • Applications and challenges of big data
    • Challenges of big data in retail
    • Manufacturing and supply chain
    • Transportation and logistics
    • Challenges for the transportation and logistics industry
    • Energy and utilities
    • Challenges of big data in utility and energy
    • Telecommunications
    • Challenges of big data in telecommunications
    • Government and public sector
    • Challenges of big data in the public sector and government
    • Marketing and advertising
    • Challenges of big data in marketing and advertising
    • Sports analytics
    • Challenges of big data in sports analytics
  • 3 Vs of big data
    • Example of 3Vs of big data
  • Non-definitional traits of big data
  • Big data workflow management
  • Business intelligence versus big data
    • Definition and scope
    • Data volume and variety
    • Data processing and analysis
    • Time sensitivity
    • Decision-making scope
    • The future of business intelligence is big data analytics
  • Data science process steps
  • Foundations for big data systems and programming
    • Distributed systems
    • Data storage and management
    • Data processing and analytics
    • Programming languages and tools
    • Data streaming and real-time processing
    • Cloud computing
      • Data visualization and reporting
  • Distributed file systems
    • Definition and purpose
    • Characteristics and key features
    • Architecture and data flow
    • Use cases and benefits
  • Data warehouse and Hadoop environment
    • Data warehouse
    • Hadoop
    • Difference between data warehouse and Hadoop environment
  • Coexistence
  • Conclusion
  • Questions
• 2. Big Data Analytics
  • Introduction
  • Structure
  • Classification of analytics
  • Data science
    • Difference between data science and big data
      • Data characteristics
      • Tools and technologies
  • Terminologies in big data
  • CAP theorem
    • Example of the CAP theorem in big data
  • BASE concept
  • Conclusion
  • Questions
• 3. Introduction of NoSQL
  • Introduction
  • Structure
  • Introduction to NoSQL
  • NoSQL databases creation history
  • NoSQL categories
    • Document databases
    • Key-value stores
    • Column-family stores
  • NoSQL advantages
    • Graph databases
    • Graph database in NoSQL
  • NewSQL
  • SQL versus NoSQL versus NewSQL
  • Conclusion
  • Questions
• 4. Introduction to Hadoop
  • Introduction
  • Structure
  • History of Hadoop
  • Features of Hadoop
    • Distributed storage
    • Scalability
    • Fault tolerance
    • Parallel processing
    • Flexibility
    • Ecosystem
    • Cost-effectiveness
  • Advantages of Hadoop
  • Versions of Hadoop
  • Hadoop ecosystems
    • Hadoop distributed file system
    • MapReduce
    • Apache Hive
    • Apache Pig
    • Apache Spark
    • Apache HBase
    • Apache Kafka
    • Apache Sqoop
    • Apache Zeppelin
    • Apache Oozie
  • Hadoop distributions
  • HQL versus SQL
  • Relational database management system versus Hadoop
    • Relational database management system
    • Hadoop
  • Hadoop architecture
  • Conclusion
  • Questions
• 5. Map Reduce
  • Introduction
  • Structure
  • MapReduce architecture
    • Steps in MapReduce
  • Working of MapReduce
    • Example of MapReduce
    • Limitations of MapReduce
  • Mapper
    • Working of Mapper
  • Reducer
  • Combiner
    • Working of MapReduce combiner
    • Advantages of combiners
    • Disadvantages of combiners
  • Partitioner
    • Need of MapReduce partitioner
    • Advantages and disadvantages of partitioner
  • Searching
  • Sorting
    • The sorting algorithm
  • Compression
  • Hadoop 2
    • Architecture of Hadoop 2
  • Hadoop YARN architecture
    • Interacting with Hadoop ecosystems
  • Conclusion
  • Questions
• 6. Introduction to MongoDB
  • Introduction
  • Structure
  • Not only SQL databases
  • Mongo DB
    • Advantages of MongoDB
    • Data modeling in MongoDB
    • History of MongoDB
  • MongoDB key features
    • Document model
    • Scalability
    • High performance
    • Replication and high availability
      • Replication
    • High availability
    • Flexible querying
    • Integration with programming languages
  • Data types
  • MongoDB query language
  • Create, read, update, and delete operations
  • Arrays
  • Functions
    • Count
    • Sort
    • Limit
    • Skip
    • Aggregate
  • MapReduce
  • Cursors in MongoDB
    • Indexes in MongoDB
    • mongoimport and mongoexport
      • mongoimport
      • mongoexport
  • Conclusion
  • Questions
• Index