Big Data and Hadoop

This book introduces you to the Big Data processing techniques addressing but not limited to various BI (business intelligence) requirements, such as reporting, batch analytics, online analytical processing (OLAP), data mining and ...

Author: VK Jain

Publisher: KHANNA PUBLISHING

ISBN: 9789382609131

Category: Education

Page: 600

View: 308

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This book introduces you to the Big Data processing techniques addressing but not limited to various BI (business intelligence) requirements, such as reporting, batch analytics, online analytical processing (OLAP), data mining and Warehousing, and predictive analytics. The book has been written on IBMs Platform of Hadoop framework. IBM Infosphere BigInsight has the highest amount of tutorial matter available free of cost on Internet which makes it easy to acquire proficiency in this technique. This therefore becomes highly vunerable coaching materials in easy to learn steps. The book optimally provides the courseware as per MCA and M. Tech Level Syllabi of most of the Universities. All components of big Data Platform like Jaql, Hive Pig, Sqoop, Flume , Hadoop Streaming, Oozie: HBase, HDFS, FlumeNG, Whirr, Cloudera, Fuse , Zookeeper and Mahout: Machine learning for Hadoop has been discussed in sufficient Detail with hands on Exercises on each.
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Big Data Analytics with Hadoop 3

Apache Hadoop is the most popular platform for big data processing to build powerful analytics solutions. This book shows you how to do just that, with the help of practical examples.

Author: Sridhar Alla

Publisher: Packt Publishing Ltd

ISBN: 9781788624954

Category: Computers

Page: 482

View: 376

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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.
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Big Data with Hadoop MapReduce

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.

Author: Rathinaraja Jeyaraj

Publisher: CRC Press

ISBN: 9781000439083

Category: Computers

Page: 406

View: 121

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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
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Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data

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.

Author: Paul Zikopoulos

Publisher: McGraw Hill Professional

ISBN: 9780071790543

Category: Computers

Page: 176

View: 460

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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
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Big Data Made Easy

Anyone can download it and—with the help of this book—start to use it within a day. With the skills this book will teach you under your belt, you will add value to your company or client immediately, not to mention your career.

Author: Michael Frampton

Publisher: Apress

ISBN: 9781484200940

Category: Computers

Page: 392

View: 965

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Many corporations are finding that the size of their data sets are outgrowing the capability of their systems to store and process them. The data is becoming too big to manage and use with traditional tools. The solution: implementing a big data system. As Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset shows, Apache Hadoop offers a scalable, fault-tolerant system for storing and processing data in parallel. It has a very rich toolset that allows for storage (Hadoop), configuration (YARN and ZooKeeper), collection (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), moving (Sqoop and Avro), monitoring (Chukwa, Ambari, and Hue), testing (Big Top), and analysis (Hive). The problem is that the Internet offers IT pros wading into big data many versions of the truth and some outright falsehoods born of ignorance. What is needed is a book just like this one: a wide-ranging but easily understood set of instructions to explain where to get Hadoop tools, what they can do, how to install them, how to configure them, how to integrate them, and how to use them successfully. And you need an expert who has worked in this area for a decade—someone just like author and big data expert Mike Frampton. Big Data Made Easy approaches the problem of managing massive data sets from a systems perspective, and it explains the roles for each project (like architect and tester, for example) and shows how the Hadoop toolset can be used at each system stage. It explains, in an easily understood manner and through numerous examples, how to use each tool. The book also explains the sliding scale of tools available depending upon data size and when and how to use them. Big Data Made Easy shows developers and architects, as well as testers and project managers, how to: Store big data Configure big data Process big data Schedule processes Move data among SQL and NoSQL systems Monitor data Perform big data analytics Report on big data processes and projects Test big data systems Big Data Made Easy also explains the best part, which is that this toolset is free. Anyone can download it and—with the help of this book—start to use it within a day. With the skills this book will teach you under your belt, you will add value to your company or client immediately, not to mention your career.
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Data Analytics with Hadoop

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.

Author: Benjamin Bengfort

Publisher: "O'Reilly Media, Inc."

ISBN: 9781491913765

Category: Computers

Page: 288

View: 140

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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
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Processing Big Data with Azure HDInsight

After reading this book you will be familiar with Azure HDInsight and how it can be utilized to build big data solutions, including batch processing, stream analytics, interactive processing, and storing and retrieving data in an efficient ...

Author: Vinit Yadav

Publisher: Apress

ISBN: 9781484228692

Category: Computers

Page: 207

View: 184

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Get a jump start on using Azure HDInsight and Hadoop Ecosystem components. As most Hadoop and Big Data projects are written in either Java, Scala, or Python, this book minimizes the effort to learn another language and is written from the perspective of a .NET developer. Hadoop components are covered, including Hive, Pig, HBase, Storm, and Spark on Azure HDInsight, and code samples are written in .NET only. Processing Big Data with Azure HDInsight covers the fundamentals of big data, how businesses are using it to their advantage, and how Azure HDInsight fits into the big data world. This book introduces Hadoop and big data concepts and then dives into creating different solutions with HDInsight and the Hadoop Ecosystem. It covers concepts with real-world scenarios and code examples, making sure you get hands-on experience. The best way to utilize this book is to practice while reading. After reading this book you will be familiar with Azure HDInsight and how it can be utilized to build big data solutions, including batch processing, stream analytics, interactive processing, and storing and retrieving data in an efficient manner. What You'll Learn Understand the fundamentals of HDInsight and Hadoop Work with HDInsight cluster Query with Apache Hive and Apache Pig Store and retrieve data with Apache HBase Stream data processing using Apache Storm Work with Apache Spark Who This Book Is For Software developers, technical architects, data scientists/analyts, and Hadoop administrators who want to develop on Microsoft’s managed Hadoop offering, HDInsight
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BIG DATA AND HADOOP

Hadoop do not require any expensive hardware for storing data. It uses general hardware that is commonly used for storage, in storage purpose. It is designed to run clusters for that there is chance of node failure of cluster is high.

Author: Mayank Bhusan

Publisher: BPB Publications

ISBN: 9789387284838

Category: Computers

Page: 333

View: 102

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The book contains the latest trend in IT industry 'BigData and Hadoop'. It explains how big is 'Big Data' and why everybody is trying to implement this into their IT project.It includes research work on various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends.Big Data and Hadoop have taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience and would be a reference to help readers.KEY FEATURES Overview Of Big Data, Basics of Hadoop, Hadoop Distributed File System, HBase, MapReduce, HIVE: The Dataware House Of Hadoop, PIG: The Higher Level Programming Environment, SQOOP: Importing Data From Heterogeneous Sources, Flume, Ozzie, Zookeeper & Big Data Stream Mining, Chapter-wise Questions & Previous Years Questions
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1000 Big Data Hadoop Interview Questions and Answers

Hadoop major drawback was cross-switch network traffic due to the huge volume of data. To overcome this drawback, Data locality came into the picture. It refers to the ability to move the computation close to where the actual data ...

Author: Vamsee Puligadda

Publisher: Vamsee Puligadda

ISBN:

Category: Computers

Page:

View: 199

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Knowledge for Free... Get that job, you aspire for! Want to switch to that high paying job? Or are you already been preparing hard to give interview the next weekend? Do you know how many people get rejected in interviews by preparing only concepts but not focusing on actually which questions will be asked in the interview? Don't be that person this time. This is the most comprehensive Big Data, Hadoop interview questions book that you can ever find out. It contains: 1000 most frequently asked and important Big Data, Hadoop interview questions and answers Wide range of questions which cover not only basics in Big Data, Hadoop but also most advanced and complex questions which will help freshers, experienced professionals, senior developers, testers to crack their interviews.
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Hadoop BIG DATA Interview Questions You ll Most Likely Be Asked

This book puts the interviewee in the driver's seat and helps them steer their way to impress the interviewer.

Author: Vibrant Publishers

Publisher: Vibrant Publishers

ISBN: 9781946383495

Category: Computers

Page: 160

View: 119

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· 200 Hadoop BIG DATA Interview Questions · 76 HR Interview Questions · Real life scenario based questions · Strategies to respond to interview questions · 2 Aptitude Tests Hadoop BIG DATA Interview Questions You'll Most Likely Be Asked is a perfect companion to stand ahead above the rest in today’s competitive job market. Rather than going through comprehensive, textbook-sized reference guides, this book includes only the information required immediately for job search to build an IT career. This book puts the interviewee in the driver's seat and helps them steer their way to impress the interviewer. The following is included in this book: a) 200 Hadoop BIG DATA Interview Questions, Answers and Proven Strategies for getting hired as an IT professional b) Dozens of examples to respond to interview questions c) 76 HR Questions with Answers and Proven strategies to give specific, impressive, answers that help nail the interviews d) 2 Aptitude Tests download available on www.vibrantpublishers.com
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Security Issues of Big Data Hadoop

Scientific Essay from the year 2018 in the subject Computer Science - IT-Security, grade: 2, , language: English, abstract: In this we discuss security issues for big data Hadoop environment.

Author: Rohit Sharma

Publisher: GRIN Verlag

ISBN: 9783668652248

Category: Computers

Page: 4

View: 973

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Scientific Essay from the year 2018 in the subject Computer Science - IT-Security, grade: 2, , language: English, abstract: In this we discuss security issues for big data Hadoop environment. Big data applications are a great benefit to organization, business and in many small and large scale industries. Security and privacy issues are magnified by velocity, variety and volume of big data. Hadoop projects security as top agenda which in turn represents classified as critical term. With the increasing acceptance of Hadoop, there is increasing trend to create a vast security feature. Therefore a traditional security mechanism, which are tailored to securing a small scale static data are in adequate. The important issues relating to Hadoop are authentication, authorization, editing and encryption within a cluster. In this paper we have highlighted different security aspects of big data Hadoop.
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Big Data For Dummies

Implement — discover how to implement your big data solution with an eye to operationalizing and protecting your data What it means — see the importance of big data to your organization and how it's used to solve problems Open the book ...

Author: Judith S. Hurwitz

Publisher: John Wiley & Sons

ISBN: 9781118644171

Category: Computers

Page: 336

View: 685

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Find the right big data solution for your business ororganization Big data management is one of the major challenges facingbusiness, industry, and not-for-profit organizations. Data setssuch as customer transactions for a mega-retailer, weather patternsmonitored by meteorologists, or social network activity can quicklyoutpace the capacity of traditional data management tools. If youneed to develop or manage big data solutions, you'll appreciate howthese four experts define, explain, and guide you through this newand often confusing concept. You'll learn what it is, why itmatters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importanceto businesses, not-for-profit organizations, government, and ITprofessionals Authors are experts in information management, big data, and avariety of solutions Explains big data in detail and discusses how to select andimplement a solution, security concerns to consider, data storageand presentation issues, analytics, and much more Provides essential information in a no-nonsense,easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helpsyou take charge of big data solutions for your organization.
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Big Data and Hadoop

The book contains the latest trend in IT industry 'BigData and Hadoop'.

Author: Mayank Bhusan

Publisher: BPB Publications

ISBN: 9789386551993

Category: Computers

Page: 333

View: 490

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The book contains the latest trend in IT industry 'BigData and Hadoop'. It explains how big is 'Big Data' and why everybody is trying to implement this into their IT project.It includes research work on various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends.Big Data and Hadoop have taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience and would be a reference to help readers.KEY FEATURES Overview Of Big Data, Basics of Hadoop, Hadoop Distributed File System, HBase, MapReduce, HIVE: The Dataware House Of Hadoop, PIG: The Higher Level Programming Environment, SQOOP: Importing Data From Heterogeneous Sources, Flume, Ozzie, Zookeeper & Big Data Stream Mining, Chapter-wise Questions & Previous Years Questions
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Apache Hadoop YARN

Now, its data processing has been completely overhauled: Apache Hadoop YARN provides resource management at data center scale and easier ways to create distributed applications that process petabytes of data.

Author: Arun C. Murthy

Publisher: Pearson Education

ISBN: 9780321934505

Category: Computers

Page: 304

View: 492

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"Apache Hadoop is helping drive the Big Data revolution. Now, its data processing has been completely overhauled: Apache Hadoop YARN provides resource management at data center scale and easier ways to create distributed applications that process petabytes of data. And now in Apache HadoopTM YARN, two Hadoop technical leaders show you how to develop new applications and adapt existing code to fully leverage these revolutionary advances." -- From the Amazon
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Big Data Using Hadoop and Hive

This book is the basic guide for developers, architects, engineers, and anyone who wants to start leveraging the open-source software Hadoop and Hive to build distributed, scalable concurrent big data applications.

Author: Nitin Kumar

Publisher: Mercury Learning and Information

ISBN: 1683926455

Category:

Page: 250

View: 216

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This book is the basic guide for developers, architects, engineers, and anyone who wants to start leveraging the open-source software Hadoop and Hive to build distributed, scalable concurrent big data applications. Hive will be used for reading, writing, and managing the large, data set files. The book is a concise guide on getting started with an overall understanding on Apache Hadoop and Hive and how they work together to speed up development with minimal effort. It will refer to simple concepts and examples, as they are likely to be the best teaching aids. It will explain the logic, code, and configurations needed to build a successful, distributed, concurrent application, as well as the reason behind those decisions. FEATURES: Shows how to leverage the open-source software Hadoop and Hive to build distributed, scalable, concurrent big data applications Includes material on Hive architecture with various storage types and the Hive query language Features a chapter on big data and how Hadoop can be used to solve the changes around it Explains the basic Hadoop setup, configuration, and optimization
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Learning Big Data with Amazon Elastic MapReduce

This book is aimed at developers and system administrators who want to learn about Big Data analysis using Amazon Elastic MapReduce.

Author: Amarkant Singh

Publisher: Packt Publishing Ltd

ISBN: 9781782173441

Category: Computers

Page: 242

View: 159

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This book is aimed at developers and system administrators who want to learn about Big Data analysis using Amazon Elastic MapReduce. Basic Java programming knowledge is required. You should be comfortable with using command-line tools. Prior knowledge of AWS, API, and CLI tools is not assumed. Also, no exposure to Hadoop and MapReduce is expected.
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Apache Hadoop 3 Quick Start Guide

This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics such as MapReduce, YARN and HDFS.

Author: Hrishikesh Vijay Karambelkar

Publisher: Packt Publishing Ltd

ISBN: 9781788994347

Category: Computers

Page: 220

View: 603

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A fast paced guide that will help you learn about Apache Hadoop 3 and its ecosystem Key Features Set up, configure and get started with Hadoop to get useful insights from large data sets Work with the different components of Hadoop such as MapReduce, HDFS and YARN Learn about the new features introduced in Hadoop 3 Book Description Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster. What you will learn Store and analyze data at scale using HDFS, MapReduce and YARN Install and configure Hadoop 3 in different modes Use Yarn effectively to run different applications on Hadoop based platform Understand and monitor how Hadoop cluster is managed Consume streaming data using Storm, and then analyze it using Spark Explore Apache Hadoop ecosystem components, such as Flume, Sqoop, HBase, Hive, and Kafka Who this book is for Aspiring Big Data professionals who want to learn the essentials of Hadoop 3 will find this book to be useful. Existing Hadoop users who want to get up to speed with the new features introduced in Hadoop 3 will also benefit from this book. Having knowledge of Java programming will be an added advantage.
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BIG DATA ANALYTICS

This book dwells on all the aspects of Big Data Analytics and covers the subject in its entirety.

Author: Raj Kamal

Publisher: McGraw-Hill Education

ISBN: 9789353164973

Category: Computers

Page: 534

View: 343

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Big Data Analytics(BDA) is a rapidly evolving field that finds applications in many areas such as healthcare, medicine, advertising, marketing, and sales. This book dwells on all the aspects of Big Data Analytics and covers the subject in its entirety. It comprises several illustrations, sample codes, case studies and real-life analytics of datasets such as toys, chocolates, cars, and student’s GPAs. The book will serve the interests of undergraduate and post graduate students of computer science and engineering, information technology, and related disciplines. It will also be useful to software developers. Salient Features: - Comprehensive coverage on Big Data NoSQL Column-family, Object and Graph databases, programming with open-source Big Data - Hadoop and Spark ecosystem tools, such as MapReduce, Hive, Pig, Spark, Python, Mahout, Streaming, GraphX - Inclusion of latest topics machine learning, K-NN, predictive-analytics, similar and frequent item sets, clustering, decision-tree, classifiers recommenders, real-time streaming data analytics, graph networks, text, web structure, web-links, social network analytics. - Web supplement includes instructional PPT’s, solution of exercises, analysis using open source datasets of a car company, and topics for advanced learning.
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Big Data Analytics Beyond Hadoop

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.

Author: Vijay Srinivas Agneeswaran

Publisher: FT Press

ISBN: 9780133838251

Category: Computers

Page: 200

View: 130

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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.
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