دانلود کتاب کلان داده و Hadoop، ویرایش دوم – مبانی، ابزارها و تکنیک های موفقیت مبتنی بر داده

Welcome to the world of Big Data! In today’s data-driven landscape, the ability to harness and process vast amounts of information has become not just an asset but a necessity for businesses, researchers, and individuals alike. This book, titled Big Data and Hadoop: Fundamentals, tools, and techniques for data-driven success is your gateway to understanding and mastering the fascinating realm of Big Data. 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. Chapter 5: Hadoop Installation – Getting hands-on with Hadoop is crucial, this chapter guides you through the step-by-step process of installing Hadoop on various platforms. Whether you’re using Ubuntu or setting up a fully distributed Hadoop system, this chapter provides detailed instructions to help you get started. Chapter 6: MapReduce Applications – This chapter is all about MapReduce, a fundamental programming model for processing Big Data. We will help you understand the principles behind MapReduce, walk you through the traditional way of using it, and explain the MapReduce workflow. Chapter 7: Hadoop Related Tools-I: HBase and Cassandra – This chapter introduces you to two important tools in the Big Data ecosystem: HBase and Cassandra. You will discover how to install HBase, explore its conceptual architecture, and gain practical insights into its implementation. We will also delve into HBase’s key differences from traditional relational databases. The chapter then shifts focus to Cassandra, explaining its data model, providing examples, and discussing its integration with Hadoop. Chapter 8: Hadoop Related Tool-II: PigLatin and HiveQL – It introduces two more essential tools: PigLatin and HiveQL. You will learn how to install PigLatin, understand its execution types, and explore the Pig data model. We will also guide you through the development and testing of PigLatin scripts. Next, we delve into Hive, exploring its data types, file formats, and comparing HiveQL with traditional database querying languages. Chapter 9: Practical and Research-based Topics – This chapter is dedicated to practical and research-based topics in the world of Big Data. You will explore real-world applications like data analysis with X, the use of Bloom Filters in MapReduce, leveraging Amazon Web Services, analyzing documents archived from The New York Times, mobile data mining, and Hadoop diagnostics. 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. This book is designed to provide you with a comprehensive understanding of Big Data technologies, enabling you to tackle real-world challenges and leverage the opportunities presented by the ever-expanding world of data. Whether you are a student, a professional, or a curious explorer, we hope this book equips you with the knowledge and skills to thrive in the era of Big Data. It is said “To err is human, to forgive divine”. In this light I wish that the shortcomings of the book will be forgiven. At the same I am open to any kind of constructive criticisms and suggestions for further improvement.