Big Data

The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow.

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. Some of these ideas are very old, some have emerged in just the last few decades, and many of them have come together to create the particularly twenty-first century practice of Data Dournalism.

This book provides a rich and panoramic introduction to Data Dournalism, combining both critical reflection and practical insight. It offers a diverse collection of perspectives on how data journalism is done around the world and the broader consequences of datafication in the news, serving as both a textbook and a sourcebook for this emerging field.

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.

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples.

This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. It provides a systematic contemporary overview of the field of critical data studies that reflects on practices of digital data collection and analysis.

This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines.

Apache Spark is an open-source big-data processing framework built around speed, ease of use, and sophisticated analytics.

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 sheds new light on a selection of big data scenarios from an interdisciplinary perspective. It features legal, sociological and economic approaches to fundamental big data topics such as privacy, data quality and the ECJ's Safe Harbor decision on the one hand, and practical applications such as smart cars, wearables and web tracking on the other. Addressing the interests of researchers and practitioners alike, it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.

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 but want to put in correct order the whole dispersed knowledge one may have.

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.

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.

This open access book 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.