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Big Data—A New Medium?

Big Data—A New Medium?
Author: Natasha Lushetich
Publisher: Routledge
Total Pages: 228
Release: 2020-11-26
Genre: Social Science
ISBN: 1000214443
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Drawing on a range of methods from across science and technology studies, digital humanities and digital arts, this book presents a comprehensive view of the big data phenomenon. Big data architectures are increasingly transforming political questions into technical management by determining classificatory systems in the social, educational, and healthcare realms. Data, and their multiple arborisations, have become new epistemic landscapes. They have also become new existential terrains. The fundamental question is: can big data be seen as a new medium in the way photography or film were when they first appeared? No new medium is ever truly new. It’s always remediation of older media. What is new is the medium’s re-articulation of the difference between here and there, before and after, yours and mine, knowable and unknowable, possible and impossible. This transdisciplinary volume, incorporating cultural and media theory, art, philosophy, history, and political philosophy is a key resource for readers interested in digital humanities, cultural, and media studies.

Big Data--A New Medium?

Big Data--A New Medium?
Author: Natasha Lushetich
Publisher: Routledge
Total Pages: 228
Release: 2020-11-26
Genre: Social Science
ISBN: 9781000214529
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Drawing on a range of methods from across science and technology studies, digital humanities and digital arts, this book presents a comprehensive view of the big data phenomenon. Big data architectures are increasingly transforming political questions into technical management by determining classificatory systems in the social, educational, and healthcare realms. Data, and their multiple arborisations, have become new epistemic landscapes. They have also become new existential terrains. The fundamental question is: can big data be seen as a new medium in the way photography or film were when they first appeared? No new medium is ever truly new. It's always remediation of older media. What is new is the medium's re-articulation of the difference between here and there, before and after, yours and mine, knowable and unknowable, possible and impossible. This transdisciplinary volume, incorporating cultural and media theory, art, philosophy, history, and political philosophy is a key resource for readers interested in digital humanities, cultural, and media studies.

Big Data Now: 2012 Edition

Big Data Now: 2012 Edition
Author: O'Reilly Media, Inc.
Publisher: "O'Reilly Media, Inc."
Total Pages: 132
Release: 2012-10-23
Genre: Computers
ISBN: 1449356680
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The Big Data Now anthology is relevant to anyone who creates, collectsor relies upon data. It's not just a technical book or just a businessguide. Data is ubiquitous and it doesn't pay much attention toborders, so we've calibrated our coverage to follow it wherever itgoes. In the first edition of Big Data Now, the O'Reilly team tracked thebirth and early development of data tools and data science. Now, withthis second edition, we're seeing what happens when big data grows up:how it's being applied, where it's playing a role, and theconsequences -- good and bad alike -- of data's ascendance. We've organized the second edition of Big Data Now into five areas: Getting Up to Speed With Big Data -- Essential information on thestructures and definitions of big data. Big Data Tools, Techniques, and Strategies -- Expert guidance forturning big data theories into big data products. The Application of Big Data -- Examples of big data in action,including a look at the downside of data. What to Watch for in Big Data -- Thoughts on how big data will evolveand the role it will play across industries and domains. Big Data and Health Care -- A special section exploring thepossibilities that arise when data and health care come together.

New Horizons for a Data-Driven Economy

New Horizons for a Data-Driven Economy
Author: José María Cavanillas
Publisher: Springer
Total Pages: 303
Release: 2016-04-04
Genre: Computers
ISBN: 3319215698
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In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

Big Data in Small Business

Big Data in Small Business
Author: Lund Pedersen, Carsten
Publisher: Edward Elgar Publishing
Total Pages: 272
Release: 2021-09-21
Genre: Business & Economics
ISBN: 1839100168
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This important book considers the ways in which small and medium-sized enterprises (SMEs) can thrive in the age of big data. To address this central issue from multiple viewpoints, the editors introduce a collection of experiences, insights, and guidelines from a variety of expert researchers, each of whom provides a piece to solve this puzzle.

Data Engineering on Azure

Data Engineering on Azure
Author: Vlad Riscutia
Publisher: Simon and Schuster
Total Pages: 334
Release: 2021-08-17
Genre: Computers
ISBN: 1617298921
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Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data

Big Data in Education

Big Data in Education
Author: Ben Williamson
Publisher: SAGE
Total Pages: 257
Release: 2017-07-24
Genre: Education
ISBN: 1526416344
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This cutting-edge overview explores big data and the related topic of computer code, examining the implications for education and schooling for today and the near future.

Dear Data

Dear Data
Author: Giorgia Lupi
Publisher: Chronicle Books
Total Pages: 304
Release: 2016-09-13
Genre: Design
ISBN: 1616895462
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Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere.

Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data

Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data
Author: Paul Zikopoulos
Publisher: McGraw Hill Professional
Total Pages: 176
Release: 2011-10-22
Genre: Computers
ISBN: 0071790543
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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. The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. You'll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide. Learn how IBM hardens Hadoop for enterprise-class scalability and reliability Gain insight into IBM's unique in-motion and at-rest Big Data analytics platform Learn tips and tricks for Big Data use cases and solutions Get a quick Hadoop primer

Data Feminism

Data Feminism
Author: Catherine D'Ignazio
Publisher: MIT Press
Total Pages: 328
Release: 2023-10-03
Genre: Social Science
ISBN: 026254718X
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A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.