Big Data

Big Data represents a new era in data exploration and utilization, and IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform.

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. This collaborative book coordinated by the European Journalism Centre and the Open Knowledge Foundation aims to answer questions like: Where can I find data? How can I request data? What tools can I use? How can I find stories in data? How can I make data journalism sustainable?

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 aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.

An Apache open source project, Hadoop stores huge amounts of data in safe, reliable storage and runs complex queries over data in an efficient way. It is at the core of a whole host of the most popular Big Data tools. Mastering Hadoop ensures you get the best out of all these tools and better insight from your data.

As technology advances, high volumes of valuable data are generated day by day in modern organizations. The management of such huge volumes of data has become a priority in these organizations, requiring new techniques for data management and data analysis in Big Data environments. These environments encompass many different fields including medicine, education data, and recommender systems.

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 edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

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.

Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others.

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 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 open access book 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.

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.