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Links to Free Computer, Mathematics, Technical Books all over the World
Big Data and Data Stream
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  • Mastering Spark with R: Large-Scale Analysis and Modeling

    With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.

  • Using Spark from R for Performance with Arbitrary Code

    This short publication attempts to provide practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R code organized in custom-built functions and packages.

  • Big Data for Qualitative Research (Kathy A. Mills)

    This book explores the potentials of qualitative methods and analysis for big data, covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges.

  • Engineering of Big Data Processing (Piotr FulmaƄski)

    This book is addressed to all the people who want to understand how Big Data differs from Data and why they should be treated different way. It may be good both for someone with no computer scientist background and for those who have some IT experience.

  • Algorithms for Big Data (Hannah Bast, et al)

    This open access book surveys the progress in addressing selected challenges related to the growth of big data in combination with increasingly complicated hardware. Tackles problems such as transportation systems, energy supply, medicine.

  • Engineering Agile Big-Data Systems (Kevin Feeney, et al)

    This book outlines an approach to dealing with problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals.

  • Knowledge Graphs and Big Data Processing (Valentina Janev, et al)

    Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.

  • Big Data in Context: Legal, Social and Technological Insights

    This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.

  • Modelling and Simulation for Big Data Applications

    Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations.

  • Kafka: The Definitive Guide: Real-Time Data and Stream Processing

    Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.

  • Designing Event-Driven Systems (Ben Stopford)

    Concepts and Patterns for Streaming Services with Apache Kafka: this book explains how service-based architectures and stream processing tools such as Apache Kafka can help you build business-critical systems.

  • Making Sense of Stream Processing: Behind Apache Kafka

    This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. It explains how these projects can help you reorient your database architecture around streams and materialized views.

  • Big Data Security (Shibakali Gupta, et al)

    After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology.

  • Big Data Processing with Apache Spark (Srini Penchikala)

    Learn about the Apache Spark framework and develop Spark programs for use cases in big-data analysis. It covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.

  • The Internals of Apache Spark (Jacek Laskowski)

    This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala.

  • The Data Engineer's Guide to Apache Spark (Databricks)

    This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines. It leverages Spark's amazing speed, scalability, simplicity, and versatility to build practical Big Data solutions.

  • Forecasting: Principles and Practice (Rob J. Hyndman, et al.)

    This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

  • BIG CPU, BIG DATA: Solving the World's Toughest Problems

    The goal of this book is to teach you how to write parallel programs that take full advantage of the vast processing power of modern multicore computers, compute clusters, and graphics processing unit (GPU) accelerators.

  • The Big Data Agenda: Data Ethics and Critical Data Studies

    This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Specific case studies explore how big data have been used in academic work.

  • Artificial Intelligence for Big Data (Anand Deshpande, et al)

    You will learn to use machine learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of machine and deep learning techniques to work on genetic and neuro-fuzzy algorithms.

  • Big Data Analytics with Hadoop 3 (Sridhar Alla)

    This book shows you how to combine Hadoop with a host of other big data tools to build powerful analytics solutions, by providing insights into the software as well as its benefits with the help of practical examples.

  • Hadoop with Python (Zachary Radtka, et al)

    This book takes you through the basic concepts behind Hadoop, MapReduce, Pig, and Spark. Then, through multiple examples and use cases, you'll learn how to work with these technologies by applying various Python tools.

  • Hadoop for Windows Succinctly (Dave Vickers)

    This book provides a thorough guide to using Hadoop directly on Windows operating systems. From a conceptual overview to practical examples, Hadoop for Windows Succinctly is a valuable resource for developers.

  • Hadoop Succinctly (Elton Stoneman)

    This booky explains how Hadoop works, what goes on in the cluster, demonstrates how to move data in and out of Hadoop, and how to query it efficiently. It also walks through a Java MapReduce example, illustrates it in Python and .NET, too.

  • Hadoop Illuminated (Mark Kerzner, et al)

    This book aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical. It book introduces you to Hadoop and to concepts such as 'MapReduce', etc., which will help you get acquainted with the technology.

  • Understanding Big Data: Analytics for Hadoop and Streaming Data

    In this book, the three defining characteristics of Big Data - volume, variety, and velocity, are discussed. Industry use cases are also included in this practical guide, to deliver a robust, secure, highly available, enterprise-class Big Data platform.

  • The Data Journalism Handbook: Towards A Critical Data Practice

    This book provides a rich and panoramic introduction to Data Dournalism, combining both critical reflection and practical insight. It serves as both a textbook and a sourcebook for this emerging field.

  • O'Reilly® The Data Journalism Handbook (Jonathan Gray, et al.)

    This book is intended to be a useful resource for anyone who thinks that they might be interested in becoming a data journalist, or dabbling in Data Dournalism, aims to answer questions like: Where can I find data? How can I request data? etc.

  • The Curious Journalist's Guide to Data (Jonathan Gray)

    This is a book about using data in journalism, but it's not a particularly practical book. Instead it's for the curious, for those who wonder about the deep ideas that hold everything together. Practice of Data Dournalism.

  • Big Data on Real-World Applications (Sebastian Ventura Soto)

    The aim of this book is to provide the reader with a variety of fields and systems where the analysis and management of Big Data are essential. It describes the importance of the Big Data era and how existing information systems are required to be adapted.

  • Concept of Scientific Inference When Working with Big Data

    Examine critical challenges and opportunities in performing scientific inference reliably when working with big data, focued on the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations.

  • Disruptive Possibilities: How Big Data Changes Everything

    This book takes you on a journey of discovery into the emerging world of big data, from its relatively simple technology to the ways it differs from cloud computing. It provides an historically-informed overview through a wide range of topics.

  • The Promise and Peril of Big Data (David Bollier)

    This book explores the positive aspects and the social perils that arise when the ever-rising floods of data being generated by mobile networking, cloud computing and other new technologies meets continued innovations in advanced correlation techniques.

  • Machine Learning for Data Streams: Practical Examples in MOA

    This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, it demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

  • O'Reilly® Big Data Now: Current Perspectives from O'Reilly Radar

    This book represents report recaps the trends, tools, applications, and forecasts. This collection of blog posts, authored by leading thinkers and experts in the field, reflects a unique set of themes we've identified as gaining significant attention and traction.

  • O'Reilly® Planning for Big Data: Changing Data Landscape

    This book provides an efficient, user-friendly 'brief' on the current status of Big Data analytics and how you can economically deploy this technology to increase your firm's profitability.

  • Data-Intensive Text Processing with MapReduce (Jimmy Lin)

    This free book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

  • Mining of Massive Datasets (A. Rajaraman, J. D. Ullman)

    This book teaches algorithms that have been used in practice to solve key problems in data mining and includes exercises suitable for students from the advanced undergraduate level and beyond.

  • Introducing Microsoft Azure HDInsight - Technical Overview

    This book covers what big data really means, how you can use it to your advantage in your company or organization, and one of the services you can use to do that quickly. Especifically, Microsoft's HDInsight service.

  • HDInsight Succinctly (James Beresford)

    Learn how to set up and manage HDInsight clusters on Azure, how to use Azure Blob Storage to store input and output data, connect with Microsoft BI, and much more. It will reveal a new avenue of data management.

  • O'Reilly® Agile Data: Building Data Analytics Applications

    How to create an environment for exploring data, using lightweight tools such as Ruby, Python, Apache Pig, and the D3.js (Data-Driven Documents) JavaScript library.

  • The Fourth Paradigm: Data-Intensive Scientific Discovery

    This book presents the first broad look at the rapidly emerging field of data-intensive science, with the goal of influencing the worldwide scientific and computing research communities and inspiring the next generation of scientists.

  • The Global Impact of Open Data: Key Findings from Case Studies

    Open data has spurred economic innovation, social transformation, etc. This book presents detailed case studies of open data projects throughout the world, along with in-depth analysis of what works and what doesn't.

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