268348993-Big-Data-Black-Book.pdf

BIG DATACovers Hadoop 2, MapReduce, Hive, YARN, Pig, R and Data Visualization THIS BOOK AIMS TO:  Acquaint the readers with the entire data analytics lifecycle  Familiarize the readers with the role and use of Big Data in various relevant industries through case studies  Provide complete technical know-how of basic and advanced Big Data analytics and data visualization techniques used to analyze data, and provide business insights  Give hands-on experience of working with Big Data analytics tools on datasets, including R and Hadoop  Enable readers to develop MapReduce and Pig programs, manipulate distributed files, and understand APIs supporting MapReduce programs ISBN: 9789351197577 | Author: DT Editorial Services ABOUT THE AUTHOR ABOUT THE BOOK DT Editorial Services has seized the market of computer books, Big Data is one of the most bringing excellent content in software development to the fore. popular buzzwords in The team is committed to excellence—excellence in the quality of technology industry today. content, excellence in the dedication of its authors and Organizations worldwide have editors, excellence in the attention to detail, and excellence realized the value of the immense in understanding the needs of its readers. volume of data available, and are trying their best to manage, analyse, and unleash the power of data to build THE BOOK COVERS: strategies and develop a competitive  Overview of Big Data edge. At the same time, the advent of  Big Data in Business Context the technology has led to the evolution of a variety of new and enhanced job roles.  Hadoop Ecosystem  MapReduce Fundamentals The objective of this book is to create a new  Big Data Technologies breed of versatile Big Data analysts and developers, who are thoroughly conversant with  Data Processing with MapReduce the basic and advanced analytic techniques for  YARN, Hive, and Pig manipulating and analysing data, the Big Data  Data manipulation using R platform, and the business and industry  Functions and Packages in R requirements to be able to participate productively in  Graphical Analyses in R Big Data projects.  Big Data Visualization Techniques ` 799/- 946 PAGES /dtechpress /dtechpress /dreamtechpress dreamtechpress.wordpress.com Exploring RGui  Skill Assessment for Big Data Jobs 10: Customizing MapReduce Execution  Exploring RStudioHandling Basic Expressions in R  Roles and Responsibilities in Big Data Jobs  Controlling MapReduce Execution with  Variables in R. Hadoop YARN. Email: [email protected] Mumbai: Tel: +91-22-2788 9263. Working with Vectors  Gaining a Foothold in the Big Data Market InputFormat  Storing and Calculating Values in R  Basic Educational Requirements for Big Data Jobs  Reading Data with Custom RecordReader  Creating and Using Objects  Basic Technological Requirements for Big Data  Organizing Output Data with OutputFormats  Interacting with Users Jobs. Daryaganj New Delhi-110 002.dreamtechpress. Distributed by: Published by: 19-A.wileyindia. INDIA New Delhi-110 002. Tools Used in Data Visualization. INDIA Tel: +91-11-2324 3463-73.com Email: csupport@wiley. YARN Commands  Using Arguments in Functions  Use of Big Data in Retail Industry  Log Management in Hadoop 1  Built‐in Functions in R. Saving Graphs to External Files  Cloud Computing and Big Data  Hive DDL. Ansari Road.com Website: www. Materialized Views  Introducing Social Media  Non‐Relational Database. ZooKeeper 25: Data Visualization‐I  Working with Functions in Pig  Flume. History of Analytical Tools  Challenges of Mobile Analytics  Recollecting the Concept of MapReduce  Introducing Popular Analytical Tools 29: Finding a Job in the Big Data Market Framework  Comparing Various Analytical Tools. Fax: +91-11-2327 5895 Email: feedback@dreamtechpress. LTD.com Website: www. Data Manipulation in Hive  In‐Memory Computing Technology for Big Data  Data Retrieval Queries.com . Oozie SLA  Introduction to Tableau Software  Exploring the Big Data Stack 16: NoSQL Data Management  Tableau Desktop Workspace  Virtualization and Big Data  Introduction to NoSQL. TABLE OF CONTENTS 1: Getting an Overview of Big Data 11: Testing and Debugging MapReduce Applications  Using the scan() Command  What is Big Data?  Performing Unit Testing for MapReduce  Reading Multiple Data Values from Large Files  History of Data Management – Evolution Applications  Reading Data from R Studio of Big Data  Performing Local Application Testing with Eclipse  Exporting Data from R  Structuring Big Data. Advantages of YARN  Managing Data in R Using Matrices  Use of Big Data in Preventing Fraudulent  YARN Architecture. Getting Started with Hive 23: Performing Graphical Analysis in R  Introducing Hadoop  Data Types in Hive. Careers in Big Data  Application Log Processing  Selecting the Most Appropriate Data Structure  Future of Big Data  Defensive Programming in MapReduce  Creating Data Subsets. Oozie Bundle  Tableau Products  Uses of MapReduce  Oozie Parameterization with EL  Role of HBase in Big Data Processing 26: Data Visualization with Tableau (Data  Oozie Job Execution Model Visualization‐II) 6: Understanding Big Data Technology Foundations  Accessing Oozie. Visualizing Big  The MapReduce Framework  Understanding the Oozie Workflow Data. Built‐In Functions in Hive  Using Plots.com Regional Offices: Bangalore: Tel: +91-80-2313 2383. Graph Databases 27: Social Media Analytics and Text Mining  RDBMS and Big Data  Schema‐Less Databases. 2788 9272. Fax: +91-80-2312 4319. Elements of Big Data  Logging for Hadoop Testing 21: Manipulating and Processing Data in R  Big Data Analytics. Using JOINS in Hive 24: Integrating R and Hadoop and Understanding Hive  RHadoop―An Integration of R and Hadoop 4: Understanding Hadoop Ecosystem 14: Analyzing Data with Pig  Text Mining in RHadoop  Hadoop Ecosystem  Introducing Pig. Daryaganj 4435-36/7.com /dtechpress /dtechpress /dreamtechpress dreamtechpress. Telefax: +91-22-2788 9263. Document Databases  Using Visual Controls in Tableau Public 7: Storing Data in Databases and Data Warehouses  Relationships. Sharding  Introducing Key Elements of Social Media  Integrating Big Data with Traditional Data  MapReduce Partitioning and Combining  Introducing Text Mining Warehouses  Composing MapReduce Calculations  Understanding Text Mining Process  Big Data Analysis and Data Warehouse 17: Understanding Analytics and Big Data  Sentiment Analysis  Changing Deployment Models in Big Data Era  Comparing Reporting and Analysis  Performing Social Media Analytics and Opinion 8: Storing Data in Hadoop  Types of Analytics Mining on Tweets  Introducing HDFS. Sqoop. Data Mining in Hive  MapReduce. Merging Datasets in R 2: Exploring the Use of Big Data in Business Context 12: Understanding Hadoop YARN Architecture  Sorting Data. Installing R  Importance and Scope of Big Data Jobs  Developing Simple MapReduce Application 19: Exploring R  Big Data Opportunities  Points to Consider while Designing MapReduce  Exploring Basic Features of R. Putting Your Data into Shape  Use of Big Data in Social Networking  Background of YARN. Introducing HBase  Points to Consider during Analysis 28: Mobile Analytics  Combining HBase and HDFS  Developing an Analytic Team  Introducing Mobile Analytics  Selecting the Suitable Hadoop Data  Understanding Text Analytics  Introducing Mobile Analytics Tools Organization for Applications 18: Analytical Approaches and Tools to Analyze Data  Performing Mobile Analytics 9: Processing Your Data with MapReduce  Analytical Approaches. Working of YARN  Managing Data in R Using Data Frames Activities  YARN Schedulers 22: Working with Functions and Packages in R  Use of Big Data in Detecting Fraudulent  Backward Compatibility with YARN  Using Functions Instead of Scripts Activities in Insurance Sector  YARN Configurations. Ansari Road. Oozie  Introducing Data Visualization 15: Using Oozie  Techniques Used for Visual Data Representation 5: Understanding MapReduce Fundamentals and  Introducing Oozie  Types of Data Visualization HBase  Installing and Configuring Oozie  Applications of Data Visualization. Polyglot Persistence  Distribution Models. Tools Supporting Big Data  Customizing Data with RecordWriter  Handling Data in R Workspace  Consultants and In‐House Specialists in Big Data  Optimizing MapReduce Execution with  Executing Scripts.wordpress.  Techniques to Optimize MapReduce Jobs  Oozie Coordinator. Hive  Working with Operators in Pig  Pig and Pig Latin. Running Pig  Data Analysis Using the MapReduce Technique in  Hadoop Distributed File System  Getting Started with Pig Latin Rhadoop. Email: mumsales@wiley. Creating Plots  Tactics for Searching Big Data Jobs Combiner  Accessing Help and Documentation in R  Preparing for Interviews  Controlling Reducer Execution with Partitioners  Using Built‐in Datasets in R  Obtaining Big Data Jobs through Social Media  Implementing a MapReduce Program for 20: Reading Datasets and Exporting Data from R Sorting Text Data  Using the c() Command Books are available on: DREAMTECH PRESS WILEY INDIA PVT. Introducing Packages 3: Introducing Technologies for Handling Big Data 13: Exploring Hive  Working with Packages  Distributed and Parallel Computing for Big Data  Introducing Hive. Hbase. Aggregate Data Models  Data Analytics in Tableau Public  Virtualization Approaches  Key Value Data Model. Fax: +91-11-2324 3078 Tel: +91-11-4363 0000.