Introduction to Hadoop for Developers Course

Course Summary

The Introduction to Hadoop for Developers training course teaches the fundamentals of setting up a Hadoop cluster, as well as the “soup” of related technologies like Hive, Pig and Oozie.

The course begins by teaching students how to access the Hadoop file system and write MapReduce jobs using Java, Pig, and Hive Oozie. Next, students will work with their own installation of a Hadoop 2, single node cluster in hands-on workshops. The course will then discuss examples of real world Map Reduce jobs and how Hadoop has solved real world data-intensive processing problems. The course concludes by exploring the different modes in which Hadoop can be run to support massive amounts of data, as well as students' MapReduce jobs during development.

Best of all, students will walk away with a fully configured virtual machine that can run under VirtualBox or VMWare with Hadoop and all related technologies installed, configured, and ready to run. The virtual machine will include the necessary development environment (using Eclipse), so students are immediately productive in growing their Hadoop knowledge by using a live environment, without the hassle of having to set one up from scratch.

Prerequisites: Basic Java knowledge
(experience with Eclipse is a plus); we recommend courses in our core Java catalog.

Purpose

Learn how to write MapReduce programs using Java.

Audience

System adminstrators, developers, and DevOps engineers creating Big Data solutions using Hadoop.

Role

Software Developer - System Administrator

Skill Level

Intermediate

Style

Hack-a-thon - Learning Spikes - Workshops

Duration

4 Days

Related Technologies

Hadoop

Java

Apache

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Productivity Objectives:

Discover the Hadoop Distributed File System (HDFS).
Interpret general Hadoop Cluster/HDFS administration.
Explain MapReduce.
Define how to write a MapReduce job with Java, Pig, and Hive.
Differentiate how the different Hadoop technologies inter-operate to provide a cohesive big data solution.
Demonstrate basic management of a Hadoop cluster.
Give examples of how to perform basic unit testing of MapReduce jobs.
Distinguish how Message Passing Interface (MPI) and High Performance Computing (HPC) intersect with Hadoop.

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What You'll Learn

In the Introduction to Hadoop for Developers training course, you'll learn:

Hadoop Overview
- Big Data Introduction
- History
- Comparison to Relational Databases
- Hadoop Ecosystem
HDFS
- Architecture/Concepts
- Access
- Namenodes
- Filesystem Shell
- Accessing HDFS with Java
- Reading/Writing/Browsing File System
- Basic HDFS Admin
HBASE
- Overview
- Architecture
- Data Model
- Installation and Shell
- Access via Java API
- Scan API
- Filters
- Storage Model
- Table Design
Map Reduce on YARN
- Introduction
- Processing Model
- Command line tools
- MapReduce Framework
- Submitting MapReduce Jobs
- Writing MapReduce Jobs in Java
- MapReduce Theory
- Distributive Cache
- Speculative Execution
- YARN Components
- Counters
- Details of MapReduce Job Execution
Hadoop Streaming
- Implementing a Streaming Job
- Counters in Streaming Jobs
- Contrast with Java Jobs
MapReduce Workflows
- Problem Decomposition into MapReduce Jobs
- Coding Workflows
- Using the JobControl Class
Oozie
- Installation
- Writing Oozie Workflows
- Deploying and Running Oozie Jobs
Pig
- Installation
- Pig Latin
- Writing Pig Scripts
- User Defined functions
- Data Set Joins
Hive
- Installation
- Table Creation and Deletion
- Partitioning
- Loading Data into Hive
- Joins
- Bucketing