Covers the breadth of activities, methods and tools that Data Scientists use. Focuses on concepts, principles and practical applications. The learning is supported and explained with illustrative examples using open-source software.

Big data is all about vast amounts of data, i.e., large datasets measured in terabytes or petabytes or even more. Big data helps numerous companies to generate valuable insights about the products or the services they offer.

This open access book is a comprehensive exploration into the pivotal role of data in shaping our contemporary society. The variety of topics and examples provided on the general topic are a highlight.

Explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. Provides a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas.

Presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. Also introduces some of the most popular Big Data, AI and Blockchain applications in the sector.

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society.

This book shows data engineers and data scientists why structure and unification in Apache Spark matters. Specifically, it explains how to perform simple and complex data analytics and employ machine learning algorithms.

This book offers an introduction to the Apache Spark ecosystem, you will learn a wide array of concepts about PySpark in Data Mining, Text Mining, Machine Learning and Deep Learning.

Explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us.

Provides data engineers and scientists insight into how Kubernetes provides a platform for building data platforms that increase an organization’s data agility. How Kubernetes has changed the way we process big data and why businesses must adapt.

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.

Provides an updated vision on the most advanced computing, storage, and interconnection technologies, that are at basis of convergence among the High-Performance Computing (HPC), Cloud, Big Data, and artificial intelligence (AI) domains.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.