MP4 | AVC 160kbps | English | 1024x768 | 15fps | 13h 23mins | AAC stereo 94kbps | 1.73 GB

Genre: Video Training

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.

Content:

1 - Welcome

2 - Introducing Hadoop

3 - The map-reduce programming model

4 - Operating modes & installation environment

5 - Installing Hadoop

6 - Writing our first map-reduce job

7 - HDFS

8 - Running in Pseudo-Distributed Mode

9 - Map-reduce process flow 1

10 - Map-reduce process flow 2

11 - Enhancing Map and Reduce

12 - Job Configuration

13 - Case Study 1 - Part 1

14 - Case Study 1 - Part 2

15 - Case Study 1 - Part 3

16 - Chaining Multiple Map-Reduce Jobs

17 - Pre and Post Processing

18 - Optimising Map-Reduce jobs

19 - Log Files & Counters

20 - Working with relational databases

21 - Unit testing

22 - Secondary Sorting

23 - Joining data

24 - Using Amazon Elastic Map Reduce

25 - Case Study 2

26 - Course Summary