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People Click Techno Solutions Pvt., Ltd.,
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Hadoop Developer Programme Peopleclick Techno Solutions Pvt Ltd.
Course Description & Details This training programme is for developers who want to learn to use Apache Hadoop to build powerful data processing applications. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster, and Map-Reduce etc. will be covered in the course. Audience This course is intended and appropriate for developers who will be writing, maintaining, or optimizing Hadoop jobs. Prerequisites Participants should have programming experience, preferably hands-on Java. Understanding of common computer science concepts is a plus. Course Outline Module1: UNIX & Basic Sun Solaris OS concepts: Basic Primitives of UNIX: • Origin of Unix, Overview of System Administration • Unix basic commands • Working with editors – vi and sed • Linking files with symbolic link and hard link • Scheduling Jobs using Crontab and at • Locating a process and monitoring processes with ps and prstat • Killing Process in different ways Installation: • Planning system resources • Solaris installation on workstations • Managing Software Packages and Patches User Management and Security: • Creation of groups, users and modifying their accounts • Changing passwords, set password aging Assigning environmental variables to users, administration of Initialization of files • Change ownership of files and directories • Setting advanced file permissions like setuid, setgid, stickybit Disk and File System Management: • Managing disks formatting, labeling, viewing volume table of contents Bringing disks to O/S control dynamically • File Systems types, creating, mounting, unmounting checking and repairing File Systems. Module2: CORE JAVA Basic Java Programming Oops • Abstraction • Inheritance • Polymorphism • Interfaces
Java.lang package • String, StringBuffer & StringBuilder • Wrapper classes

Hadoop Developer Programme Peopleclick Techno Solutions Pvt Ltd
Generics • TypeSafty • Type Casting File I/O and Serialization Collection Framework Module3: HADOOP DEVELOPER COURSE DETAILS Understand Big Data & Hadoop: Basic Concepts • What is BigData • Characteristics of BigData • Challenges with Traditional Systems • Problems with BigData • Handling BigData HADOOP Core Concepts • Problems with Existing Distributed Systems to deal Big Data • Why Hadoop and An Overview and History of Hadoop • Requirements of NewApproach • The Hadoop Project and Hadoop Components Hadoop Distributed File System – HDFS • What is HDFS, Why it is required for running Map-Reduce. • How it differs from other distributed file systems. • Design of HDFS & Concepts • Command Line Interface, Hadoop File Systems, Java Interface • Data Flow (Anatomy of a File Read, Anatomy of a File Write) • Hadoop Archives. • Hands-on Exercise on HDFS Understanding - Map-Reduce Basics and Map-Reduce Types and Formats • Describe Map-Reduce framework works & Flow • Why Map-Reduce is tightly coupled with HDFS. • What are the different types of Input and Output formats and why they are required? • Hadoop Data Types, • Concept of Mappers & Reducers • Concept of Partitioners & Combiners • MapReduce Types • Input Formats (Input Splits and Records, Text Input, Binary Input, Multiple Inputs, Database Input and Output) • Output Formats (TextOutput, BinaryOutPut, Multiple Outputs, Databaseoutput). • Hands-on Exercise
Setting up Hadoop Cluster • Cluster introduction • How to setup Hadoop Cluster • Different configuration files which need to be edited for Cluster setup. • Cluster Specification, • Cluster Setup and Installation • SSH Configuration • Hadoop Configuration

Hadoop Developer Programme Peopleclick Techno Solutions Pvt Ltd.

• Important Hadoop Daemon Properties • Hadoop Daemon Addresses and Ports The Five Daemons working Other Hadoop Properties, User Account Creation. • Install Pseudo cluster • Install Multi node cluster • Hands-on Exercise Developing Map Reduce Programs • Setting up Eclipse Development Environment. • Eclipse integration with HADOOP for Rapid Application Development • Understanding HADOOP API • Creating Map Reduce Projects, • Writing MapReduce Drivers, Mappers and Reducers in Java • Driver Code • Mapper Code • Reducer Code • Map Reduce Code • Differences Between the Old and New MapReduce APIs • Hands-on Exercise Understanding ToolRunner • More about ToolRunner • Combiner • Reducer • configure and close methods • Hands-on Exercise Common MapReduce Algorithms • Sorting • Searching • Indexing • TF-IDF • Word_CoOccurance • Hands-on Exercise HADOOP EcoSystem • Flume • Sqoop • Importing data from RDBMS using sqoop • Hive • Introduction • Introduction to Hadoop and Hive • Getting Data into Hive • Manipulating Data with Hive • Partitioning and Bucketing Data
• Pig • Introduction to pig • Different modes of pig • when to use hive and when to use pig

• HBASE • Basics of HBASE • Hands-on Exercise Advanced MapReduce Programming • Developing custom Writable • Developing custom Writable Comparable • Understanding Input Output formats • Hands-on Exercise Introduction to Ooziee Hadoop Best Practices and Use Cases
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