Big Data With Hadoop Mapreduce

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Big Data With Hadoop Mapreduce

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Author : Rathinaraja Jeyaraj
language : en
Publisher: CRC Press
Release Date : 2020-05-01

Big Data With Hadoop Mapreduce written by Rathinaraja Jeyaraj and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-01 with Computers categories.

The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts. They have employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines. This book covers almost all the necessary information on Hadoop MapReduce for most online certification exams. Upon completing this book, readers will find it easy to understand other big data processing tools such as Spark, Storm, etc. Ultimately, readers will be able to: • understand what big data is and the factors that are involved • understand the inner workings of MapReduce, which is essential for certification exams • learn the features and weaknesses of MapReduce • set up Hadoop clusters with 100s of physical/virtual machines • create a virtual machine in AWS • write MapReduce with Eclipse in a simple way • understand other big data processing tools and their applications

Data Intensive Text Processing With Mapreduce

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Author : Jimmy Lin
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2010-10-10

Data Intensive Text Processing With Mapreduce written by Jimmy Lin and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-10 with Computers categories.

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 data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks

Mapreduce Design Patterns

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Author : Donald Miner
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012

Mapreduce Design Patterns written by Donald Miner and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

Big Data

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Author : Balamurugan Balusamy
language : en
Publisher: John Wiley & Sons
Release Date : 2021-03-15

Big Data written by Balamurugan Balusamy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-15 with Mathematics categories.

Learn Big Data from the ground up with this complete and up-to-date resource from leaders in the field Big Data: Concepts, Technology, and Architecture delivers a comprehensive treatment of Big Data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. Beginning with a fulsome overview of what we mean when we say, “Big Data,” the book moves on to discuss every stage of the lifecycle of Big Data. You’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like SQL, data processing, data analytics, machine learning, and data mining. You’ll also discover how specific technologies like Apache Hadoop, SQOOP, and Flume work. Big Data also covers the central topic of big data visualization with Tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. Accessibly organized, Big Data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. Some of those concepts include: The common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns Relational and non-relational databases, like RDBMS, NoSQL, and NewSQL databases Virtualizing Big Data through encapsulation, partitioning, and isolating, as well as big data server virtualization Apache software, including Hadoop, Cassandra, Avro, Pig, Mahout, Oozie, and Hive The Big Data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization Perfect for data scientists, data engineers, and database managers, Big Data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.

Frank Kane S Taming Big Data With Apache Spark And Python

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Author : Frank Kane
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-06-30

Frank Kane S Taming Big Data With Apache Spark And Python written by Frank Kane and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-30 with Computers categories.

Frank Kane's hands-on Spark training course, based on his bestselling Taming Big Data with Apache Spark and Python video, now available in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book Understand how Spark can be distributed across computing clusters Develop and run Spark jobs efficiently using Python A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who wants to learn Big Data processing using Apache Spark and Python, this book is for you. If you have some programming experience in Python, and want to learn how to process large amounts of data using Apache Spark, Frank Kane's Taming Big Data with Apache Spark and Python will also help you. What You Will Learn Find out how you can identify Big Data problems as Spark problems Install and run Apache Spark on your computer or on a cluster Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets Implement machine learning on Spark using the MLlib library Process continuous streams of data in real time using the Spark streaming module Perform complex network analysis using Spark's GraphX library Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster In Detail Frank Kane's Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you'll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. Apache Spark has emerged as the next big thing in the Big Data domain – quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease. Style and approach Frank Kane's Taming Big Data with Apache Spark and Python is a hands-on tutorial with over 15 real-world examples carefully explained by Frank in a step-by-step manner. The examples vary in complexity, and you can move through them at your own pace.

Big Data Analytics With Hadoop 3

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Author : Sridhar Alla
language : en
Publisher:
Release Date : 2018-05-29

Big Data Analytics With Hadoop 3 written by Sridhar Alla and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-29 with Computers categories.

Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3 Key Features Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud Integrate Hadoop with other big data tools such as R, Python, Apache Spark, and Apache Flink Exploit big data using Hadoop 3 with real-world examples Book Description 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. Once you have taken a tour of Hadoop 3's latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly. What you will learn Explore the new features of Hadoop 3 along with HDFS, YARN, and MapReduce Get well-versed with the analytical capabilities of Hadoop ecosystem using practical examples Integrate Hadoop with R and Python for more efficient big data processing Learn to use Hadoop with Apache Spark and Apache Flink for real-time data analytics Set up a Hadoop cluster on AWS cloud Perform big data analytics on AWS using Elastic Map Reduce Who this book is for Big Data Analytics with Hadoop 3 is for you if you are looking to build high-performance analytics solutions for your enterprise or business using Hadoop 3's powerful features, or you're new to big data analytics. A basic understanding of the Java programming language is required.

Optimizing Hadoop For Mapreduce

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Author : Khaled Tannir
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-02-21

Optimizing Hadoop For Mapreduce written by Khaled Tannir and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-21 with Computers categories.

This book is an example-based tutorial that deals with Optimizing Hadoop for MapReduce job performance. If you are a Hadoop administrator, developer, MapReduce user, or beginner, this book is the best choice available if you wish to optimize your clusters and applications. Having prior knowledge of creating MapReduce applications is not necessary, but will help you better understand the concepts and snippets of MapReduce class template code.

Data Analytics With Hadoop

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Author : Benjamin Bengfort
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-06

Data Analytics With Hadoop written by Benjamin Bengfort and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06 with Computers categories.

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib

Big Data Analytics Beyond Hadoop

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Author : Vijay Srinivas Agneeswaran
language : en
Publisher: FT Press
Release Date : 2014-05-15

Big Data Analytics Beyond Hadoop written by Vijay Srinivas Agneeswaran and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-15 with Business & Economics categories.

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.

Hadoop Mapreduce V2 Cookbook Second Edition

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Author : Thilina Gunarathne
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-02-25

Hadoop Mapreduce V2 Cookbook Second Edition written by Thilina Gunarathne and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-25 with Computers categories.

If you are a Big Data enthusiast and wish to use Hadoop v2 to solve your problems, then this book is for you. This book is for Java programmers with little to moderate knowledge of Hadoop MapReduce. This is also a one-stop reference for developers and system admins who want to quickly get up to speed with using Hadoop v2. It would be helpful to have a basic knowledge of software development using Java and a basic working knowledge of Linux.