Hadoop Essential Training for Administrators and Developers

Customized Onsite Training

  • Customized Content
  • For Groups of 5+
  • Online or On-location
  • Expert Instructors

Big data is here! In this Hadoop Essential Training for Administrators and Developers class, students will learn the fundamentals of setting up a Hadoop cluster as well as the "soup" of related technologies like Hive, Pig, and Oozie. Come prepared to learn how to access the Hadoop file system, write MapReduce jobs using java, Pig, and Hive, as well as how to use Pig, Hive, and Oozie. Every participant will work with their own installation of a Hadoop 2, single node cluster.

  1. Gain an understanding of the Hadoop File System (HDFS).
  2. Learn what MapReduce is and why you should care.
  3. Learn how to write a MapReduce job with java, Pig, and Hive.
  4. Learn how the different Hadoop technologies interoperate to provide a cohesive big data solution.
  5. Learn basic management of a Hadoop cluster.
  6. Learn how to perform basic unit testing of your MapReduce jobs.
  7. Learn the different modes that Hadoop can be run in to support massive amounts of data as well as your MapReduce jobs during development.
  1. Hadoop Overview
    1. What is Big Data?
    2. How did we get to this point?
    3. How does Hadoop compare to a relational database system?
    4. Big Data Introduction
    5. History
    6. Comparison to Relational Databases
    7. Hadoop Ecosystem
  2. HDFS
    1. Architecture/Concepts
    2. Access
    3. Namenodes
    4. Filesystem Shell
    5. Accessing HDFS with Java
    6. Reading/Writing/Browsing file system
  3. HBASE
    1. Overview
    2. Architecture
    3. Data Model
    4. Installation and Shell
    5. Access via Java API
    6. Administration access via Java
    7. Scan API
    8. Filters
    9. Storage Model
    10. Table Design
  4. Map Reduce on YARN
    1. Introduction
    2. Processing Model
    3. Command line tools
    4. MapReduce framework
    5. Submitting MapReduce Jobs
    6. Writing MapReduce jobs in Java
    7. MapReduce Theory
    8. Distributive Cache
    9. Speculative Executin
    10. YARN Components
    11. Counters
    12. Details of MapReduce Job Execution
  5. Hadoop Streaming
    1. Implementing a streaming job
    2. Counters in streaming jobs
    3. Contrast with Java Jobs
  6. MapReduce Workflows
    1. Problem decomposition into MapReduce Jobs
    2. Coding workflows
    3. Using the JobControl Class
  7. Oozie
    1. Oozie Installation
    2. Writing Oozie workflows
    3. Deploying and running Oozie jobs
  8. Pig
    1. Installation
    2. Pig Latin
    3. Writing Pig Scripts
    4. User Defined functions
    5. Data set joins
  9. Hive
    1. Installation
    2. Table creation and deletion
    3. Partitioning
    4. Loading data into Hive
    5. Joins
    6. Bucketing
Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this Hadoop class:

  • Basic Java Knowledge .

Experience in the following would be useful for this Hadoop class:

  • Experience with Eclipse.
Preparing for Class

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