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Big Data Now: 2012 Edition

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

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Advanced Analytics with Spark: Patterns for Learning from Data at Scale

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields su

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Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies)

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Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale

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James Haight Big Data, Analytics, Hadoop Thought Leader – Hadooponomics
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Data Science from Scratch: First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data scien

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Hadoop Application Architectures – Big data
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Have you ever faced issues within your enterprise because the data is not updated or you are unable to manage it well? No matter whether you are a small business or an already established one, data management is the most important factor in increasing your enterprise’s work efficiency. There are numerous ways of managing data within any enterprise; however, some do not provide the expected results and others are extremely expensive. This is where open source technology of Hadoop comes in to the limelight.

Hadoop MapReduce is an open source software framework that is known to divide various types of large data clusters in to numerous small parts so that the management of data becomes easy. If used efficiently, this framework can help you manage the data well and can work as a boon for your enterprise. In a particular set-up where there is large amount of data along with huge network, it becomes important for an enterprise to distribute it in the best possible manner so that it can be used properly.

In such situation, Hadoop Distributed File System (HDFS) is definitely needed in order to manage the data within any enterprise well. The process usually termed as a huge-scale bath processing infrastructure that is considered to be extremely helpful for the overall set-up that has numerous computers connected to each other. While the process can easily be used on a particular machine, it definitely requires the experience of the power of a particular system. During this time, it is important for the user to use the system on any network where thousands of computers are easily available.

In any such huge network, where the distribution of overall data is required but it is a difficult task, Hadoop MapReduce technology can help because it is so well designed that it efficiently distributes the complete data across various networks. A lot of efforts are usually involved in making of this type of efficient software systems and also takes years in developing all these magical instruments that ease out the work process and efficiently bring better efficiency in various business outcomes.

Hadoop Distributed File System has the capability to store various types of vast quantities of information that can easily be processed regardless of the operating system that you are using. It also has a unique feature of Hadoop ecosystem that provides various addictive abilities to all your distributed systems completely. Get Hadoop tutorials to make the most out of Hadoop technology.

Getting Started Guide: Analyzing Big Data with AWS

Big data—data sets that are too large to store in a traditional relational database and that require distributed applications for processing—can be expensive and complicated to manage. Moving your data to the cloud can reduce storage and analysis costs and simplify administration. This guide explains how to use Amazon Simple Storage Service (S3) to store big data, Amazon Elastic Cloud Compute (EC2) instances (virtual servers) to process it, and Amazon Elastic MapReduce to manage the details

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Technology has reached a long way and programming is one part that has transformed the world of computers. Programming has literally allowed people to play high end games with the help of high quality sounds and graphics. The market today is flooded with skilled programmers who always come up with some or the other inventions. MapReduce is one of them. Became popular in the year 2004, it is an application platform that allows the skilled programmers to develop or create program by using a number of unsystematic clusters of content that is created to work in specific computers. Created by Google, this technology is a substitute for its former algorithms that were used for indexing purposes.

The major advantage that has persuaded programmers to opt for this technology is the way in which it enables the programmers to program in a more simplified way with respect to intra cluster. It is enabled in failure handling and monitoring and assures the user on efficient intra cluster communication.  Considered to be the best medium that can be used for duplication of the projects, it has the proficiency to outshine over common data bases that have been created. It is the easiest way of enabling programming in a simple manner so that they can function in a faster and smoother manner.

The word can be categorized into two parts: a part that is created to locate content and categorize it into different clusters is called Map. The map can be considered as the first line that is capable of categorizing the fundamental details that the user requires to carry out the indexing process. The second one is reduce, which is used to gather the assorted data that the former function “map” has composed and present it in easy to understand single values. The overall function of this technology is collecting data.

To increase the efficiency, Hadoop architecture is used. It serves a very important role in the process of MapReduce. Hadoop is a powerful suite of tools that is based on the idea that a large problem can be turned small and tackled by numerous pieces.  Hadoop architecture is designed to apply the concepts of functioning programming to the examination of huge volumes of data and that is the reasons why various websites including Facebook uses it.

Used by organizations in a variety of ways, MapReduce can bring efficiency into the ways in which the organization is processing data within your organization and save costs in data processing technologies.

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The programming framework called MapReduce was developed by Google to develop a large amount of data in the most effective way possible. In fact, it is often used while dealing with a large number of data that needs distribution across hundreds and thousands of machines to handle it efficiently.

Small companies and individuals can utilize this framework to work with data within an organization and discover some significant statistics or correlations in the data. No matter how much what is the total amount of data we need to go through, the functionality of this framework can help us facilitate quicker than ever before.

Whether a data set is complicated, broad or small, one can use this application to query the system to get accurate information. With the correct information to work with, an organization will be able to detect fraud, explore search and sharing behavior, work with graph analysis and monitor the transformations. These are some of the functions that were difficult to manage before, especially in the data sets and were continually adding to the complications in an organization.

Using MapReduce application will split the input data set into various smaller parts and make jobs more manageable, which will then be controlled by the map task in totally parallel way. The framework will then sort the output of the maps and place them into a reduce task. This is among the finest ways to use the resources of distributed and large systems.

Once the overall information has been reduced by splitting, users may depend upon this framework to handle other important functions. This process includes monitoring, scheduling and better re-execution of failed tasks. By systematizing such features, this kind of data mining becomes less complicated and easy to manage with time.

A lot of organizations also use Hadoop training, applications and API to communicate with the functionality of MapReduce. In order to keep the consistency of data, it is important to correctly input data transfers and job configurations into the system. By using Hadoop API, numerous organizations are developing innovative and extremely reliable ways to transfer and move data.

Whether you have a small organization or an already established one, if you feel that this functionality can help leverage your business, a reputed IT service provider can be searched using any reputed search engine online such as Google, Yahoo or Bing. However, the presence of spam sites cannot be denied, hence it is important to check credibility of IT service provider before going through with the process.