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Data Science & Big Data Analytics
The course, "Data Science and Big Data Analytics," has become well accepted across academia and the industry. Led by EMC Education Services, this book is ...
Big-Data.pdf - Bernard Marr
'Being smart, Bernard Marr has created an enjoyable book that describes the world of Big Data and analytics and how this will completely change our business ...
Understanding Big Data - instructional media + magic
written more than 350 magazine articles and 14 books on database technolo- ... Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming ...
Big Data: Principles and Paradigms - Dhoto-Pens
The book is presented in a coordinated and integrated manner starting with Big Data analytics methods, going through the infrastructures and platforms ...
Big Data Analytics Made Easy
This book is like your personal trainer, explains the art of Big data Analytics/ Data Science with R Programming in 18 steps which covers from Statistics, ...
Big Data For Dummies.pdf - Jan Newmarch
Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD ...
DIGITAL NOTES ON BIG DATA ANALYTICS B.TECH IV YEAR
BIG DATA ANALYTICS. DEPT. OF INFORMATION TECHNOLOGY. Text Book: 1. Seema Acharya, SubhashiniChellappan, “Big Data and Analytics”, Wiley Publications, First.
Uncharted: Big Data as a Lens on Human Culture
uncharted in this book. But erez Aiden and Jean-Baptiste. Michel don't mean by “uncharted” that things are left out—in fact, a.
Big Data Summary - Kim Hartman
A summary of the book. Big Data. A revolution that will transform how we live, work and think. By Victor Mayer-Schnberger & Kenneth Cukier.
Big Data Networked Storage Solution for Hadoop
por Prem Jain
This IBM® RedpaperTM provides a reference architecture, based on Apache Hadoop, to help businesses gain control over their data, meet tight service level agreements (SLAs) around their data applications, and turn data-driven insight into effective action.Big Data Networked Storage Solution for Hadoop delivers the capabilities for ingesting, storing, and managing large data sets with high reliability. IBM InfoSphere® Big InsightsTM provides an innovative analytics platform that processes and see more
analyzes all types of data to turn large complex data into insight.
IBM InfoSphere BigInsights brings the power of Hadoop to the enterprise. With built-in analytics, extensive integration capabilities, and the reliability, security and support that you require, IBM can help put your big data to work for you.
This IBM Redpaper publication provides basic guidelines and best practices for how to size and configure Big Data Networked Storage Solution for Hadoop.
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Big Data: A Revolution That Will Transform How We Live, Work, and Think
por Viktor Mayer-Schönberger
A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large.
Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak?
The key to answering these questions, and many more, is big data. “Big data” refers to our see more burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.
In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com
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Disruptive Possibilities: How Big Data Changes Everything
por Jeffrey Needham
Big data has more disruptive potential than any information technology developed in the past 40 years. As author Jeffrey Needham points out in this revealing book, big data can provide unprecedented visibility into the operational efficiency of enterprises and agencies.Disruptive Possibilities provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ see more
from Hadoop analytics clouds. This relentlessly innovative form of computing will soon become standard practice for organizations of any size attempting to derive insight from the tsunami of data engulfing them.
Replacing legacy silos—whether they’re infrastructure, organizational, or vendor silos—with a platform-centric perspective is just one of the big stories of big data. To reap maximum value from the myriad forms of data, organizations and vendors will have to adopt highly collaborative habits and methodologies.
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Big Data Now: 2012 Edition: Edition 2
por O'Reilly Media, Inc.
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 see more 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.
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Planning for Big Data
por Edd Wilder-James
In an age where everything is measurable, understanding big data is an essential. From creating new data-driven products through to increasing operational efficiency, big data has the potential to make your organization both more competitive and more innovative.As this emerging field transitions from the bleeding edge to enterprise infrastructure, it's vital to understand not only the technologies involved, but the organizational and cultural demands of being data-driven.
Written by see more O'Reilly Radar's experts on big data, this anthology describes:
The broad industry changes heralded by the big data eraWhat big data is, what it means to your business, and how to start solving data problemsThe software that makes up the Hadoop big data stack, and the major enterprise vendors' Hadoop solutionsThe landscape of NoSQL databases and their relative meritsHow visualization plays an important part in data work
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Big Data Now: Current Perspectives from O'Reilly Radar
por O'Reilly Radar Team
This collection represents the full spectrum of data-related content we’ve published on O’Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas:
Data issues -- The opportunities and ambiguities of the data space are evident in see more discussions around privacy, the implications of data-centric industries, and the debate about the phrase “data science” itself.
The application of data: products and processes – A “data product” can emerge from virtually any domain, including everything from data startups to established enterprises to media/journalism to education and research.
Data science and data tools -- The tools and technologies that drive data science are of course essential to this space, but the varied techniques being applied are also key to understanding the big data arena.
The business of data – Take a closer look at the actions connected to data -- the finding, organizing, and analyzing that provide organizations of all sizes with the information they need to compete.
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Building Big Data and Analytics Solutions in the Cloud
por Wei-Dong Zhu
Big data is currently one of the most critical emerging technologies. Organizations around the world are looking to exploit the explosive growth of data to unlock previously hidden insights in the hope of creating new revenue streams, gaining operational efficiencies, and obtaining greater understanding of customer needs.It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. see more
Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models.
With today's deluge of data comes the problems of processing that data, obtaining the correct skills to manage and analyze that data, and establishing rules to govern the data's use and distribution. The big data technology stack is ever growing and sometimes confusing, even more so when we add the complexities of setting up big data environments with large up-front investments.
Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. A solution architecture that can allow these mutually exclusive principles to coexist is required to truly exploit the elasticity and ease-of-use of cloud computing for big data environments.
This IBM® RedpaperTM publication is aimed at chief architects, line-of-business executives, and CIOs to provide an understanding of the cloud-related challenges they face and give prescriptive guidance for how to realize the benefits of big data solutions quickly and cost-effectively.
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The Culture of Big Data
por Mike Barlow
Technology does not exist in a vacuum. In the same way that a plant needs water and nourishment to grow, technology needs people and process to thrive and succeed. Culture (i.e., people and process) is integral and critical to the success of any new technology deployment or implementation.
Big data is not just a technology phenomenon. It has a cultural dimension. It's vitally important to remember that most people have not considered the immense difference between a world seen see more through the lens of a traditional relational database system and a world seen through the lens of a Hadoop Distributed File System.This paper broadly describes the cultural challenges that accompany efforts to create and sustain big data initiatives in an evolving world whose data management processes are rooted firmly in traditional data warehouse architectures.
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Information Governance Principles and Practices for a Big Data Landscape
por Chuck Ballard
This IBM® Redbooks® publication describes how the IBM Big Data Platform provides the integrated capabilities that are required for the adoption of Information Governance in the big data landscape. As organizations embark on new use cases, such as Big Data Exploration, an enhanced 360 view of customers, or Data Warehouse modernization, and absorb ever growing volumes and variety of data with accelerating velocity, the principles and practices of Information Governance become ever more critical see more
to ensure trust in data and help organizations overcome the inherent risks and achieve the wanted value.
The introduction of big data changes the information landscape. Data arrives faster than humans can react to it, and issues can quickly escalate into significant events. The variety of data now poses new privacy and security risks. The high volume of information in all places makes it harder to find where these issues, risks, and even useful information to drive new value and revenue are.
Information Governance provides an organization with a framework that can align their wanted outcomes with their strategic management principles, the people who can implement those principles, and the architecture and platform that are needed to support the big data use cases. The IBM Big Data Platform, coupled with a framework for Information Governance, provides an approach to build, manage, and gain significant value from the big data landscape.
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Performance and Capacity Implications for Big Data
por Dave Jewell
Big data solutions enable us to change how we do business by exploiting previously unused sources of information in ways that were not possible just a few years ago. In IBM® Smarter Planet® terms, big data helps us to change the way that the world works.The purpose of this IBM RedpaperTM publication is to consider the performance and capacity implications of big data solutions, which must be taken into account for them to be viable. This paper describes the benefits that big data approaches see more
can provide. We then cover performance and capacity considerations for creating big data solutions. We conclude with what this means for big data solutions, both now and in the future.
Intended readers for this paper include decision-makers, consultants, and IT architects.
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