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Hadoop for Java Developers

the quickest and easiest way to learn Hadoop
  • This Hadoop Training course is the easiest and quickest way to learn to program using the Map-Reduce programming model.
  • If you are a Java developer looking to learn how to design and build big-data applications, this course will both get you up and running quickly, and provide you with the core skills to produce production-quality functioning applications.
  • The course contains approx. 13 hours of video tutorials, together with guidance notes and lots of sample code.
  • Two real world case studies and processing sizeable amounts of data as you progress through the training material.
  • All the software you will need is either included or we’ll show you where you can download it.
  • The tutorials cover how to install and configure Hadoop for a typical development environment – all you need to get started with the training is a working computer capable of running Java, the Eclipse IDE and watching videos.


The course is designed to be accessible to anyone with a reasonable knowledge of basic Java. You will need to be able to write classes and create objects. Our Java Fundamentals course covers all the Java knowledge you need for this course.

Important note for Windows users: Hadoop is difficult to install on Windows, so in the course we show you to how set up a virtual machine running Linux. No prior knowledge of Linux is needed.

Contents - contains over 13 hours of video - equivalent to 4 days of live training.


Having problems? check the errata for this course.



10 m 49 s
A brief overview chapter, with a preview of the work we're going to be doing.


Introducing Hadoop

16 m 12 s
An overview of what Hadoop is and introduction to the concept of map-reduce.


The map-reduce programming model

20 m 45 s
A deeper look at the map-reduce programming model.


Operating modes & installation environment

25 m 10 s
Understanding the operating modes of Hadoop, getting ready to install (including setting up a virtual machine if needed)


Installing Hadoop

40 m 0 s
Installing Hadoop and configuring for both standalone and pseudo-distributed modes.


Writing our first map-reduce job

52 m 36 s
Using a generic map-reduce template to create a real Hadoop job.



24 m 49 s
Understanding the Hadoop file system and how to put files into and out of it from the command line.


Running in Pseudo-Distributed Mode

11 m 26 s
Running larger jobs in pseudo-distributed mode. Viewing the Hadoop Web User Interface.


Map-reduce process flow 1

40 m 36 s
Look at the steps in a map-reduce job in more detail. Learn about the shuffle process and adding a combine class.


Map-reduce process flow 2

14 m 38 s
An exercise to practice with the full map-reduce workflow.


Enhancing Map and Reduce

23 m 41 s
An overview of the built in map and reduce functions, and learning to create custom key and value data types.


Job Configuration

25 m 11 s
Understanding Hadoop file formats, and using the tool runner template to set command line parameters.


Case Study 1 - Part 1

53 m 8 s
An explanation of the first major case study, using real-world data, together with a walk through of the first 2 tasks.


Case Study 1 - Part 2

9 m 16 s
Walk through of task 3 in our case study.


Case Study 1 - Part 3

9 m 13 s
Walk through of task 4 in our case study.


Chaining Multiple Map-Reduce Jobs

27 m 27 s
Learning to automate the chaining of jobs with the JobControl object. Using the sequence file format


Pre and Post Processing

47 m 39 s
Using the ChainMapper and ChainReducer objects to add additional Map steps.


Optimising Map-Reduce jobs

29 m 46 s
Looking at multiple ways to improve the efficiency of Map-Reduce jobs


Log Files & Counters

36 m 28 s
Learning to use log files and counters as a tool to debug map-reduce code.


Working with relational databases

56 m 11 s
Reading and writing from relational databases using JDBC


Unit testing

40 m 56 s
Using Junit to test map-reduce code with the MRUnit library.


Secondary Sorting

36 m 11 s
Understanding how to sort the values before the reduce phase.


Joining data

51 m 56 s
Joining 2 data sets together with a reduce-side join.


Using Amazon Elastic Map Reduce

40 m 38 s
Using the Amazon EMR cloud based Hadoop platform to run map-reduce jobs.


Case Study 2

42 m 45 s
Our second major case study based on a real world use of Hadoop.


Course Summary

14 m 47 s
Review of what we've learned, and ideas of where to go next.

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