Название: Big Data and Hadoop: Fundamentals, tools, and techniques for data-driven success - 2nd Edition
Автор: Mayank Bhushan
Издательство: BPB Publications
Год: 2024
Страниц: 548
Язык: английский
Формат: epub (true)
Размер: 27.8 MB

In today's data-driven world, harnessing the power of big data is no longer a luxury, but a necessity. This comprehensive guide, "Big Data and Hadoop," dives deep into the world of big data and equips you with the knowledge and skills you need to conquer even the most complex data landscapes.

Start with the fundamentals of big data, exploring its growing significance and diverse applications. You'll look into the heart of the Apache Hadoop ecosystem, mastering its core components like HDFS and MapReduce. We'll demystify NoSQL databases, introducing you to HBase and Cassandra as powerful alternatives to traditional databases.

Clarify the details of MapReduce programming with practical examples, and discover the power of PigLatin and HiveQL for efficient data analysis. Explore advanced tools like Spark, unlocking its potential for real-time data processing and analytics. Rounding out your knowledge, the book delves into practical applications, exploring real-world scenarios and research-based insights. By the end of this book, you'll emerge as a confident big data explorer, equipped to tackle any data challenge with expertise and precision.

What you will learn:
- Gain a solid grasp of the fundamental concepts of big data.
- Acquire a comprehensive understanding of HDFS, MapReduce, YARN, Spark, and related components.
- Learn how to set up and configure Hadoop clusters to create scalable and reliable data processing environments.
- Develop the expertise to design, code, and execute MapReduce jobs to process and analyze vast datasets efficiently.
- Learn how to use Hadoop and related tools to perform advanced data analytics.

Chapter 1: Big Data Introduction and Demand – In this opening chapter, we embark on a journey to explore the foundations of Big Data. We will delve into the very concept of Big Data, its significance in today’s world, and the growing demand for solutions that can handle its challenges. We will also examine industry examples of how Big Data is being utilized and the myriad of possibilities it presents.
Chapter 2: NoSQL Data Management – This chapter takes us into the realm of NoSQL databases, offering an introduction to these non-relational data stores. We will compare SQL and NoSQL databases, explore the nuances of data consistency in NoSQL, and take a deep dive into the HBase database. Additionally, we will discuss the MapReduce paradigm and key concepts like partitioning and combining.
Chapter 3: MapReduce Technique – This chapter discusses a paradigm widely employed in the realm of distributed computing, that revolutionizes the processing of vast datasets with efficiency and scalability. Developed by Google, this technique serves as a cornerstone in the field of big data analytics. By harnessing the power of parallel processing and fault tolerance, MapReduce enables the seamless analysis of massive datasets across distributed clusters, making it a pivotal tool in addressing the challenges posed by the ever-expanding volume of data in diverse domains.
Chapter 4: Basics of Hadoop – To lay a solid foundation for your journey into Big Data technologies, this chapter introduces you to the basics of Hadoop. We will cover essential topics like data formats, analyzing data with Hadoop, scaling strategies, and the design of the Hadoop Distributed File System (HDFS). Concepts such as data flow, Hadoop I/O, compression, serialization, and Avro file-based data structures will be explored in detail.
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Chapter 10: Spark – As we conclude our journey through Big Data and related technologies, this chapter introduces Apache Spark, a powerful framework for distributed data processing. We will explore its capabilities and understand how it fits into the Big Data landscape, setting the stage for your next adventure in data processing.

Who this book is for:
Whether you are a beginner or have some experience with Big Data. This book is for aspiring Big Data professionals, including data analysts, software developers, IT professionals, and students in computer science and related fields.




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