Hadoop for Java Developers

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Course Description

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.

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.

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.

Course Curriculum

Chapter 1.Welcome
1. Welcome 00:09:22
Chapter 2.Introducing Hadoop
2. Introducing Hadoop 00:16:12
Chapter 3.The map-reduce programming model
3. The map-reduce programming model 00:20:45
The map-reduce programming model_Working files 00:00:00
Chapter 4.Operating modes & installation environment
4. Operating modes & installation environment 00:25:10
5 Installing Hadoop
5. Installing Hadoop 00:40:00
Installing Hadoop_Working files 00:00:00
Chapter 6.Writing our first map-reduce job
6. Writing our first map-reduce job 00:52:36
Writing our first map-reduce job_Working files 00:00:00
7 HDFS
7. HDFS 00:24:49
HDFS_Working files 00:00:00
Chapter 8.Running in Pseudo-Distributed Mode
8. Running in Pseudo-Distributed Mode 00:11:26
Chapter 9.Map-reduce process flow 1
9. Map-reduce process flow 1 00:40:36
Map-reduce process flow 1_Working files 00:00:00
Chapter 10.Map-reduce process flow 2
10. Map-reduce process flow 2 00:14:38
Map-reduce process flow 2_Working files 00:00:00
Chapter 11.Enhancing Map and Reduce
11. Enhancing Map and Reduce 00:23:41
Enhancing Map and Reduce_Working files 00:00:00
Chapter 12.Job Configuration
12. Job Configuration 00:25:11
Job Configuration_Working files 00:00:00
Chapter 13.Case Study 1 - Part 1
13. Case Study 1 – Part 1 00:53:08
Case Study 1 – Part 1_Working files 00:00:00
Chapter 14.Case Study 1 - Part 2
14. Case Study 1 – Part 2 00:09:16
Chapter 15.Case Study 1 - Part 3
15. Case Study 1 – Part 3 00:09:13
Case Study 1 – Part 3_Working files 00:00:00
Chapter 16.Chaining Multiple Map-Reduce Jobs
16. Chaining Multiple Map-Reduce Jobs 00:27:27
Chaining Multiple Map-Reduce Jobs_Working files 00:00:00
Chapter 17.Pre and Post Processing
17. Pre and Post Processing 00:47:39
Pre and Post Processing_Working files 00:00:00
Chapter 18.Optimising Map-Reduce jobs
18. Optimising Map-Reduce jobs 00:29:46
Optimising Map-Reduce jobs_Working files 00:00:00
Chapter 19.Log Files & Counters
19. Log Files & Counters 00:36:28
Log Files & Counters_Working files 00:00:00
20 Working with relational databases
20. Working with relational databases 00:56:11
Working with relational databases_Working files 00:00:00
Chapter 21.Unit testing
21. Unit testing 00:40:56
Unit testing_Working files 00:00:00
Chapter 22.Secondary Sorting
22. Secondary Sorting 00:36:11
Secondary Sorting_Working files 00:00:00
Chapter 23.Joining data
23. Joining data 00:51:56
Joining data_Working files 00:00:00
Chapter 24.Using Amazon Elastic Map Reduce
24. Using Amazon Elastic Map Reduce 00:40:38
Chapter 25.Case Study 2
25. Case Study 2 00:42:45
Case Study 2_Working files 00:00:00
26 Course Summary
26 Course Summary 14:47:00

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  • 1,500.00 750.00
  • 12 months
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