Learn Hadoop, MapReduce and BigData from Scratch
3.6 (210 ratings)
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Learn Hadoop, MapReduce and BigData from Scratch

A Complete Guide to Learn and Master the Popular Big Data Technologies
3.6 (210 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.
9,436 students enrolled
Last updated 8/2015
English
Current price: $10 Original price: $40 Discount: 75% off
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Includes:
  • 17 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
Become literate in Big Data terminology and Hadoop.
Understand the Distributed File Systems architecture and any implementation such as Hadoop Distributed File System or Google File System
Use the HDFS shell
Use the Cloudera, Hortonworks and Apache Bigtop virtual machines for Hadoop code development and testing
Configure, execute and monitor a Hadoop Job
View Curriculum
Requirements
  • A familiarity of programming in Java.
  • A familiarity of Linux
  • Have Oracle Virtualbox or VMware installed and functioning
Description

Modern companies estimate that only 12% of their accumulated data is analyzed, and IT professionals who are able to work with the remaining data are becoming increasingly valuable to companies. Big data talent requests are also up 40% in the past year.

Simply put, there is too much data and not enough professionals to manage and analyze it. This course aims to close the gap by covering MapReduce and its most popular implementation: Apache Hadoop. We will also cover Hadoop ecosystems and the practical concepts involved in handling very large data sets.

Learn and Master the Most Popular Big Data Technologies in this Comprehensive Course.

  • Apache Hadoop and MapReduce on Amazon EMR
  • Hadoop Distributed File System vs. Google File System
  • Data Types, Readers, Writers and Splitters
  • Data Mining and Filtering
  • Shell Comments and HDFS
  • Cloudera, Hortonworks and Apache Bigtop Virtual Machines

Mastering Big Data for IT Professionals World Wide
Broken down, Hadoop is an implementation of the MapReduce Algorithm and the MapReduce Algorithm is used in Big Data to scale computations. The MapReduce algorithms load a block of data into RAM, perform some calculations, load the next block, and then keep going until all of the data has been processed from unstructured data into structured data.

IT managers and Big Data professionals who know how to program in Java, are familiar with Linux, have access to an Amazon EMR account, and have Oracle Virtualbox or VMware working will be able to access the key lessons and concepts in this course and learn to write Hadoop jobs and MapReduce programs.

This course is perfect for any data-focused IT job that seeks to learn new ways to work with large amounts of data.

Contents and Overview
In over 16 hours of content including 74 lectures, this course covers necessary Big Data terminology and the use of Hadoop and MapReduce.

This course covers the importance of Big Data, how to setup Node Hadoop pseudo clusters, work with the architecture of clusters, run multi-node clusters on Amazons EMR, work with distributed file systems and operations including running Hadoop on HortonWorks Sandbox and Cloudera.

Students will also learn advanced Hadoop development, MapReduce concepts, using MapReduce with Hive and Pig, and know the Hadoop ecosystem among other important lessons.

Upon completion students will be literate in Big Data terminology, understand how Hadoop can be used to overcome challenging Big Data scenarios, be able to analyze and implement MapReduce workflow, and be able to use virtual machines for code and development testing and configuring jobs.

Who is the target audience?
  • Big Data professionals who want to Master MapReduce and Hadoop.
  • IT professionals and managers who want to understand and learn this hot new technology
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Curriculum For This Course
Expand All 76 Lectures Collapse All 76 Lectures 16:56:24
+
Introduction to Big Data
6 Lectures 01:19:34

Introduction to the Course

Preview 04:55

Introduction to Big Data, Hadoop and Map Reduce

Preview 11:55


Why Hadoop, Big Data and Map Reduce Part - C
12:26

Lecture to help you understand the server cluster architecture

Architecture of Clusters
20:43

Learn all about virtual machine provisioning

Virtual Machine (VM), Provisioning a VM with vagrant and puppet
17:39
+
Hadoop Architecture
12 Lectures 02:51:41

Learn to setup the single node cluster

Preview 13:07

Set up a single Node Hadoop pseudo cluster Part - B
13:40

Set up a single Node Hadoop pseudo cluster Part - c
14:31

Learn to set up a Hadoop Cluster

Clusters and Nodes, Hadoop Cluster Part - A
16:30

Clusters and Nodes, Hadoop Cluster Part - B
15:54

Lecture about Node Hiearchy

NameNode, Secondary Name Node, Data Nodes Part - A
11:55

NameNode, Secondary Name Node, Data Nodes Part - B
11:15

Learn to use Amazon web services for running multi node cluster

Running Multi node clusters on Amazons EMR Part - A
18:12

Running Multi node clusters on Amazons EMR Part - B
15:00

Running Multi node clusters on Amazons EMR Part - C
14:26

Running Multi node clusters on Amazons EMR Part - D
13:50

Running Multi node clusters on Amazons EMR Part - E
13:21
+
Distributed file systems
7 Lectures 01:57:03

A comparison between HDFS and GFS file systems

Preview 20:00

Learn to Run Hadoop on Cloudera

Run hadoop on Cloudera, Web Administration
18:04

Learn to run Hadoop on Hortonworks

Run hadoop on Hortonworks Sandbox
19:17

Learn to perform file system operations using HDFS Shell

File system operations with the HDFS shell Part - A
19:57

File system operations with the HDFS shell Part - B
17:08

Learn all about Hadoop development using Apache Bigtop

Advanced hadoop development with Apache Bigtop Part - A
11:13

Advanced hadoop development with Apache Bigtop Part - B
11:24
+
Mapreduce Version 1
12 Lectures 02:45:08

Learn the underlying concepts of the Map Reduce algorithm

Preview 13:12

MapReduce Concepts in detail Part - B
10:55

Learn to create Hadoop Jobs

Jobs definition, Job configuration, submission, execution and monitoring Part -A
09:39

Jobs definition, Job configuration, submission, execution and monitoring Part -B
10:44

Jobs definition, Job configuration, submission, execution and monitoring Part -C
16:48

Learn the basic syntax of Hadoop

Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part A
09:32

Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part B
10:39

Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part C
18:52

Learn all about the ETL class definition, transformation and load

The ETL class, Definition, Extract, Transform, and Load Part - A
15:14

The ETL class, Definition, Extract, Transform, and Load Part - B
24:14

Learn the basics of User defined class and functions

The UDF class, Definition, User Defined Functions Part - A
12:18

The UDF class, Definition, User Defined Functions Part - B
13:01
+
Mapreduce with Hive ( Data warehousing )
11 Lectures 02:29:13

Learn the schema design for data warehousing

Preview 15:41

Schema design for a Data warehouse Part - B
16:20

Introduction to Hive and its use for Data Warehousing

Hive Configuration Part A
10:29

Hive Configuration Part B
13:41

Learn all about Hive Query Patterns

Hive Query Patterns Part - A
16:50

Hive Query Patterns Part - B
17:15

Hive Query Patterns Part - C
12:06

Hive Query Patterns Part D
12:18

A live example to implement Hive ETL class

Example Hive ETL class Part - A
12:15

Example Hive ETL class Part - B
13:28

Example Hive ETL class Part C
08:50
+
Mapreduce with Pig (Parallel processing)
7 Lectures 01:25:37

Introduction to Parallel Processing using Apache Pig

Introduction to Apache Pig Part - A
12:17

Introduction to Apache Pig Part - B
13:45

Introduction to Apache Pig Part - C
09:07

Introduction to Apache Pig Part - D
10:09

Advance Pig features and usage of LoadFunc and EvalFunc Class

Pig LoadFunc and EvalFunc classes
13:28

A working example of PIG ETL class

Example Pig ETL class Part - A
12:40

Example Pig ETL class Part - B
14:11
+
The Hadoop Ecosystem
6 Lectures 01:22:58

A brief intro to Hadoop ecosystem and detail discussion on Crunch

Introduction to Crunch Part - A
15:20

Introduction to Crunch Part - B
12:52

Learn all about the Arvo hadoop component

Introduction to Avro
15:18

Lecture discussing the use and implementation of Mahout

Introduction to Mahout Part - A
12:51

Introduction to Mahout Part - B
13:05

Introduction to Mahout Part - C
13:32
+
Mapreduce Version 2
3 Lectures 35:58

Introduction to Yarn and its usage in hadoop 2

Apache Hadoop 2 and YARN Part - A
12:44

Apache Hadoop 2 and YARN Part - B
08:23

Yarn Implementation examples for beginners.

Yarn Examples
14:51
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Putting it all together
12 Lectures 02:07:12

Implementing the concepts on Amazon web services.

Amazon EMR example Part - A
12:03

Amazon EMR example Part - B
11:46

Amazon EMR example Part - C
08:26

Amazon EMR example Part - D
10:18

A live example implementation of Apache Bigtop

Apache Bigtop example Part - A
12:46

Apache Bigtop example Part - B
13:01

Apache Bigtop example Part - C
13:27

Apache Bigtop example Part - D
13:54

Apache Bigtop example Part - E
13:06

Apache Bigtop example Part - F
13:45

Course Summary

Preview 04:40

Reference links for various topics

References
2 pages
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