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Hadoop

Hadoop Training

Hadoop is a Free Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Objectives

Facts related to Hadoop
  • A free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment
  • Based on Google File System (GFS)
  • Runs a number of applications on distributed systems with thousands of nodes involving petabytes of data
  • Has a distributed file system, called Hadoop Distributed File System or HDFS, which enables fast data transfer among the nodes
Hadoop originated from the Nutch open source project on search engines and works over distributed network nodes.

Intended Audience

IT Engineers, Programmers, Testers, System Analyst,Java experienced persons need to apply learning on Hadoop.

Prerequisites

Candidates should have knowledge of Basic Computers, Basic Linux, Basic Java for Hadoop training.

Course Content

  1. Course Overview
    • Objectives
    • Overview and Content of the Course
    • Goals of this Course
    • Your Instructor
    • Class Logistics
  2. Introduction to Big Data and Hadoop
    • Data Explosion,Types of Data
    • Need for Big Data
    • Big Data and Its Sources,characteristics
    • Appeal of Big Data Technology
    • Big Data Technology - Platform for Discovery and Exploration,Capabilities
    • Handling Limitations of Big Data
    • Introduction to Hadoop
    • History and Milestones of Hadoop
    • Organizations Using Hadoop
  3. Getting Started with Hadoop
    • Objectives
    • VMware Player – Introduction,Hardware Requirements
    • Steps to Install VMware Player
    • Steps to Create VM in VMware Player
    • Open a VM in VMware Player
    • Open VirtualBox to Open a VM
  4. Hadoop Architecture
    • Hadoop Cluster using Commodity Hardware
    • Hadoop Configuration,Core Services
    • Apache Hadoop Core Components
    • Hadoop Core Components – HDFS,MapReduce
    • Regular File System vs. HDFS
    • HDFS – Characteristics,Key Features,Architecture
    • Operational Principle, Detailed Description
    • File System Namespace
    • NameNode Operation
    • Data Block Split
    • Benefits of Data Block Approach
    • HDFS – Block Replication Architecture
    • Replication Method
    • Data Replication Topology & Representation
    • HDFS Access
  5. Hadoop Deployment
    • Ubuntu Server – Introduction
    • Installation of Ubuntu Server 12.04
    • Business Scenario
    • Hadoop Installation
    • Steps for Hadoop Multi-Node Installation
    • Single-Node Cluster vs. Multi-Node Cluster
  6. Introduction to MapReduce
    • MapReduce - Introduction,Analogy & Example
    • Map Execution,DistributedTwo Node Environment
    • MapReduce Essentials,Jobs,Engine,Associated Tasks
    • MapReduce Association with HDFS
    • Hadoop Job Work Interaction
    • Characteristics & Real-Time Uses of MapReduce
    • Steps to Install Hadoop
    • Set up Environment for MapReduce Development
    • Small Data and Big Data
    • Uploading Small Data and Big Data
    • Build MapReduce Program
    • Hadoop MapReduce Requirements,Features and Processes
    • Steps of Hadoop MapReduce
    • MapReduce – Responsibilities
    • MapReduce Java Programming in Eclipse
    • Creating a New Project
  7. Advanced HDFS and MapReduce
    • Objectives
    • Advanced HDFS - Introduction
    • HDFS Benchmarking
    • Settings Up HDFS Block Size
    • Advanced MapReduce
    • Interfaces
    • Data Types in Hadoop
    • InputFormats in MapReduce
    • OutputFormats in MapReduce
    • Distributed Cache
    • Joins in MapReduce
    • Reduce Side,Replicated,Composite Join
    • Cartesian Product
  8. Pig
    • Introduction to Pig
    • Components of Pig
    • How Pig Works
    • Data Model & Nested Data Model
    • Pig Execution Modes and Interactive Modes
    • Installing Pig Engine
    • Run a Sample Program to Test Pig
    • Getting Datasets for Pig Development
    • Prerequisites to Set the Environment for Pig Latin
    • Script Interpretation
    • Filtering and Transforming
    • Grouping and Sorting
    • Combining and Splitting
  9. Hive
    • Metastore
    • Data Model – Tables,External Tables,Partitions
    • Data Types In Hive
    • Data Model – Queries Used to Create Partition
    • Hive File Formats
    • Hive Query Language - Select,JOIN and INSERT
    • Hive Installation & Running Hive
    • Programming in Hive
    • Hive Query Language - Extensibility
  10. HBase
    • HBase Architecture
    • Storage Model of HBase
    • Row Distribution of Data between RegionServers
    • Data Storage in HBase
  11. Zookeeper, Sqoop, and Flume
    • Zookeeper Entities,Data Model and Services
    • Sqoop Processing
    • Sqoop Under the Hadoop
    • Importing Data Using Sqoop
    • Sqoop Import – Process
    • Flume Model
    • Flume – Goals
    • Scalability in Flume
  12. Ecosystem and its Components
    • Apache Hadoop Ecosystem
    • File System,Data Store,Serialization Components
    • Job Execution Components
    • Work Management, Operations, and Development Components
    • Security,Data Transfer Components
    • Components Related to Data Interactions,Analytics and Intelligence
    • Search Frameworks
    • Graph-Processing Framework
  13. Hadoop Administration, Troubleshooting, and Security
    • Typical Hadoop Core Cluster
    • Load Balancer
    • Command Used in Hadoop Programming
    • Different Configuration Files of Hadoop Cluster
    • Properties of hadoop-default.xml
    • Different Configuration for Hadoop Cluster
    • Port Number for Individual Hadoop Services
    • Performance Monitoring and Parameters of Performance Tuning
    • Troubleshooting and Log Observation
    • Apache Ambari

Certification

There is no certification for this course because it is an open source training program.