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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.

Pre-requisites

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.

1

Welcome


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

2

Introducing Hadoop


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

3

The map-reduce programming model


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

4

Operating modes & installation environment


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

5

Installing Hadoop


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

6

Writing our first map-reduce job


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

7

HDFS


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

8

Running in Pseudo-Distributed Mode


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

9

Map-reduce process flow 1


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

10

Map-reduce process flow 2


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

11

Enhancing Map and Reduce


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

12

Job Configuration


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

13

Case Study 1 - Part 1


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

14

Case Study 1 - Part 2


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

15

Case Study 1 - Part 3


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

16

Chaining Multiple Map-Reduce Jobs


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

17

Pre and Post Processing


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

18

Optimising Map-Reduce jobs


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

19

Log Files & Counters


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

20

Working with relational databases


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

21

Unit testing


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

22

Secondary Sorting


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

23

Joining data


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

24

Using Amazon Elastic Map Reduce


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

25

Case Study 2


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

26

Course Summary


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

Let the Course Come to You

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