16 Best Books on Big Data

“Learning Spark: Lightning-Fast Big Data Analysis” Book Review: This book is a valuable resource for those looking to learn how to perform data analytics and utilize machine learning algorithms. It covers various high-level structured APIs, including Python, SQL, Scala, and Java. The reader will also gain knowledge in analyzing Spark operations, tuning and debugging with Spark configurations, and connecting to data sources such as JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka. Additionally, the book teaches how to build data pipelines using open source Delta Lake and Spark and perform analytics on batch and streaming data through Structured Streaming.

“Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data” Book Review: This book offers readers a comprehensive guide to correctly analyzing big data using the appropriate methods and tools. It emphasizes the use of an organized lifecycle approach to solving data analytics problems and covers essential concepts, principles, and practical applications of data science and big data. Additionally, the book includes an analyst’s perspective on data repositories and reviews basic data analytic methods using R 63. It also delves into advanced analytical theory and methods, with detailed discussions of topics such as clustering (Chapter 117), association rules (Chapter 137), regression (Chapter 161), and classification (Chapter 191).

“Big Data: Does Size Matter?” Book Review: This book is about big data and how it’s used today. The author wants readers to think about what big data means for us. They tell stories, share interviews, use interesting words, and give examples to keep readers interested. The book talks about everything from the basics to the hard parts of big data. The author also talks about how big data changes really fast, and can become old quickly. The book has three parts: the beginning, how big data affects different things like business and science, and what might happen with big data in the future.

“Statistics for Big Data for Dummies” Book Review: This book offers a comprehensive overview of big data, with a particular emphasis on the statistical methods employed. It examines numerous crucial applications of big data, while also providing insights into how data can be organized and verified for potential inaccuracies or missing information. The book further delves into the techniques used to deal with outliers in datasets and presents an in-depth exploration of various univariate and multivariate statistical methods. In addition, readers can expect to gain a clear understanding of essential modelling techniques such as regression analysis. To enhance the learning experience, each chapter is supplemented with practical tips, reminders, and useful links to websites for future reference.

“Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses” Book Review: This book is mainly designed for business executives and managers seeking a comprehensive understanding of Big Data. Using relatable stories, metaphors, and analogies, the author simplifies complex concepts to make them accessible to readers. The book commences with a clear definition of Big Data and its significance in the current digital era, followed by a compilation of industry examples that underscore its relevance. The text further elucidates the enabling technology and explores the necessary organizational roles for the successful implementation of Big Data within a company. Additionally, the book delves into the ethical and privacy issues that arise from the use of Big Data.

“Big Data, Data Mining and Machine Learning (WILEY Big Data Series)” Book Review: In this book, the introduction explores the timeline of big data and provides an overview of the computing environment, including hardware, distributed systems, and analytical tools. The following sections cover predictive modeling and various techniques commonly used in this field, such as Segmentation, Incremental Response Making, and Time Series Data Mining. The author also delves into several case studies that offer valuable insights into the practical applications of these concepts. Towards the end of the book, the focus shifts to emerging technologies like the Internet of Things and software development, providing a glimpse into the future of the field.

“Big Data Analytics” Book Review: This book is ideal for those interested in exploring the latest trends in big data and its connection to data mining, particularly the mining of unstructured data. It offers a thorough understanding of how big data and text analytics intersect through the use of real-world problems and research in the field. The book covers a range of data mining models and techniques and includes informative case studies. It also addresses the use of various mining tools to extract valuable insights from large datasets, as well as delving into the concepts of big data and multi-level text categorization. In addition, the book provides an in-depth analysis of subspace clustering methodologies that are particularly well-suited for handling big data.

“Computational Intelligence for Big Data Analysis: Frontier Advances and Applications” Book Review: This book is designed to be useful for both experienced professionals and undergraduate students studying computer science engineering. Its focus is on the most recent advancements in cloud computing and big data analysis, as well as their practical applications. The text tackles real-world challenges and provides solutions for overcoming them. Utilizing computational intelligence techniques demonstrates the intricacies of dealing with big data problems. Additionally, the book incorporates visual aids such as figures, graphs, and tables to reinforce the concepts covered.

“Big Data Analytics with R and Hadoop” Book Review: This comprehensive guide to big data analytics with Hadoop empowers readers to create a powerful data analytics engine with significant potential. Through a focus on the integration techniques of R and Hadoop, using tools such as RHIPE and RHadoop, readers will learn how to build and run a MapReduce application that operates seamlessly with both R and Hadoop. Additionally, they will gain a clear understanding of how to manage HDFS data from within R, using RHIPE and RHadoop. The book covers all of the high-powered big data tasks that can be accomplished by combining R and Hadoop, making it an excellent resource for R developers seeking to perform big data analytics with Hadoop. It is also an essential tool for those interested in developing intelligent applications using R packages for big data.

“Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (WILEY Big Data Series)” Book Review: This is a valuable guide to leveraging big data and analytics to gain a competitive edge and identify new business opportunities. The book provides practical insights on how businesses can better understand and manage customer behavior complexities. It focuses on the analytics techniques that offer the most value in a business environment, drawing on the author’s expertise in big data and analytics applications. The book is an accessible resource that offers a clear roadmap for organizations seeking to effectively use data analytics. This book is an ideal resource for businesses looking to harness the power of data analytics to their advantage.

“Real-Time Big Data Analytics” Book Review: This book delves into big data technologies and frameworks and is tailored for architects, developers, and programmers. It covers transformations and database-level interactions, ensuring message process reliability with Storm. Additionally, it addresses loading datasets, constructing queries, and generating recommendations using Spark SQL. The book examines the challenges and use cases of real-time analytics versus batch analytics, providing mechanisms to handle and process real-time transactional data. It also includes strategies for processing and streaming data with Amazon Kinesis and Elastic MapReduce. The book includes executable code snippets and illustrative examples.

“Machine Learning for Big Data: Hands-On for Developers and Technical Professionals” Book Review: This book provides a comprehensive overview of diverse techniques employed to derive meaningful insights from data. It offers practical insights into the coding process and demonstrates how to apply suitable machine learning methods to tackle specific problems. Additionally, the book delves into the potent nature of data and how it can be utilized against us. It furnishes coded solutions for real-world instances while laying a strong emphasis on the fundamental principles of machine learning, i.e., data preparation and cleaning. Furthermore, every chapter elucidates the workings of the code and provides running examples.

“Big Data Analytics Strategies for the Smart Grid” Book Review: The book covers a wide range of topics, including electrical utility grids that encompass operational technology, IT, and storage processing. It also touches upon SAS for asset management, the Auto Grid approach, and Space Time Insight’s work at the California ISO. Additionally, the book provides concrete examples and illustrations of data analysis and its practical applications. It is designed for utility executives at the mid to upper levels and is also a valuable resource for data analytics professionals.

“Sublinear Algorithms for Big Data Applications (SpringerBriefs in Computer Science)” Book Review: This book offers a solution to the significant challenges associated with big data through the application of sublinear algorithms. It provides a comprehensive introduction to sublinear algorithms, highlighting their relevance in large-scale data systems. The book covers three major big data applications, including wireless sensor networks, big data processing in MapReduce, and smart grids. It is an invaluable resource for computer science researchers, graduate students, as well as professionals in communications and signal processing, including engineers.

“Big Data: Related Technologies, Challenges and Future Prospects (SpringerBriefs in Computer Science)” Book Review: This comprehensive book explores the world of big data, exploring its origins and recent advancements. The author covers the various stages of big data, including data generation, acquisition, storage, and analysis, and provides insight into the technical challenges associated with each stage. The latest advances in big data technologies such as cloud computing, IoT, and Hadoop are discussed in detail, along with their practical applications in enterprise management, online social networks, healthcare, collective intelligence, and smart grids. This book is an ideal resource for professionals and researchers in the field of big data, as well as advanced-level students studying computer science and electrical engineering.