Hadoop Developer Course with MapReduce and Java
3.2 (10 ratings)
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Hadoop Developer Course with MapReduce and Java

Learn Basics of Hadoop and MapReduce with Java
3.2 (10 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
68 students enrolled
Created by Inflame Tech
Last updated 3/2017
English
Curiosity Sale
Current price: $10 Original price: $20 Discount: 50% off
30-Day Money-Back Guarantee
Includes:
  • 8 hours on-demand video
  • 1 Article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Introduction to Hadoop
  • Basics of MapReduce
  • MapReduce with YARN
View Curriculum
Requirements
  • Basics of JAVA
  • Knowledge of programming would be beneficial.
Description

This course will help you to comprehend MapReduce Programming, how to set up an environment for the same, how to submit and execute MapReduce applications. We will begin from the top and after that peel profound into the Advanced concepts of MapReduce. Towards the finish of the MapReduce course, you will hold skill on:

Processing unstructured data.

Analyse complex and large data sets in Hadoop framework.

YARN - NextGen MapReduce.

Designing and Implementing complex queries using MapReduce approach.

Will be able to break Big Data into meaningful information, process data in parallel on Hadoop cluster and make available for users.    

Learn how to extract patterns and business trends.

Who is the target audience?
  • Hadoop Beginners
  • Professionals who want to learn Hadoop
  • Graduates looking to build a career in Big Data Analytics
  • Aspiring Data Scientists
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Curriculum For This Course
77 Lectures
07:58:03
+
Module-1 Introduction to Course
4 Lectures 07:28

1.2 Prerequisites
00:23

1.3 what you will learn
02:06

1.4 Need of MapReduce
01:24
+
Module-2 A Look at Hadoop
11 Lectures 44:14

2.2 Hadoop History
03:41

2.3 Comparison of HDFS with RDBMS
08:02

2.4 Hadoop Cluster
04:24

2.5 Hadoop Features
03:10

2.6 Cluster Modes in Hadoop
02:25

2.7 Hadoop Core Components
01:44

2.8 What is HDFS
03:53

2.9 Block Replication in HDFS
01:00

2.10 HDFS and MapReduce
02:19

2.11 HDFS Daemons
06:36
+
Module-3 MapReduce Basics
8 Lectures 45:48
3.1 What is MapReduce
05:38

3.2 Why MapReduce
03:08

3.3 History of MapReduce
03:30

3.4 Use Cases to Illustrate Advantages of MapReduce
02:06

3.5 MapReduce Applications
06:43

3.6 Anatomy of MapReduce Program
01:25

3.7 Map and Reduce Function
04:19

3.8 Hands-On Session
18:59
+
Module-4 Understanding MapReduce
11 Lectures 50:56
4.1 Dataflow in MapReduce
02:03

4.2 Job Submission Flow of MapReduce
03:14

4.3 MapReduce Example
05:54

4.4 MapReduce Daemons
06:06

4.5 Job Tracker
03:57

4.6 Task Tracker
02:05

4.7 Task Assignment by JobTracker
01:45

4.8 Submission of MapReduce Job
02:02

4.9 Hands-On
11:50


4.11 Dataflow with a Single, Multiple and No Reduce Task
04:38
+
Module-5 MapReduce with YARN
16 Lectures 01:40:13
5.1 Hadoop 1.x Architecture
07:12

5.2 Hadoop 1.x Problems
06:10

5.3 NameNode-No Horizontal Scalability
01:45

5.4 No High Availability in NameNode
03:41

5.5 JobTracker-Overburdened
02:35

5.6 MRv1
03:17

5.7 Hadoop 2.x New Features
04:04

5.8 Hadoop 2.x Architecture
02:44

5.9 HDFS High Availability in Hadoop 2.x Architecture
04:02

5.10 YARN-Moving Beyond MapReduce
05:02

5.11 Different Processing Applications in YARN
03:25

5.12 MRv2 (YARN)
02:31

5.13 YARN MR Application Execution Flow
04:30

5.14 YARN Workflow
07:23

5.15 MapReduce 2.x Cluster Architecture
04:02

5.16 Hands-On
37:50
+
Module-6 Advanced MapReduce Concepts - I
12 Lectures 01:45:58
6.1 InputSplit and RecordReader
05:13

6.2 Mapper, Reducer and Driver Class
02:12

6.3 New vs Old API
08:06

6.4 Generic Option Parser, Tool and ToolRunner
02:44

6.5 GenericOptionsParser and ToolRunner Options
03:33

6.6 Writables in Hadoop
02:04

6.7 Serialization and Deserialization
19:11

6.9 Chaining of Jobs
18:02

6.10 Listing and Killing Jobs
01:15

6.11 Distributed Cache
14:17

6.12 Counters
17:00

6.13 Test cases in Hadoop
12:21
+
Module-7 Advance Mapreduce Concepts-II
12 Lectures 01:33:59
7.1 Schedulers
08:19

7.2 Implement Fair Scheduler in CDH
05:11

7.3 Data Compression in Hadoop
04:10

7.4 Different Compression Techniques in Hadoop
08:00

7.5 Hands-On
09:31

7.6 Multiple Inputs
13:30

7.7 Tuning
02:04

7.8 Profiling Map and Reduce Task
06:24

7.9 Filtering and Projection in Map Phase
03:12

7.10 Use Combiner Class
11:32

7.11 Analyze XML data using Map Reduce Framework
08:40

7.12 Custom Partitioner in Map Reduce
13:26
+
Module-8 Advance Mapreduce Concepts-III
2 Lectures 29:26
8.1 Joining in MapReduce
15:14

8.2 Different Input and Output Formats in MapReduce
14:12
+
Program & Projects for full Course
1 Lecture 00:02
Link for all Projects
00:02
About the Instructor
Inflame Tech
3.8 Average rating
104 Reviews
996 Students
7 Courses

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