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Learn Hadoop, MapReduce and BigData from Scratch

Learn Hadoop, MapReduce and BigData from Scratch

Online Self Paced Class

4,494
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Sample Chapters

Demo 1

About the Course


The growth of data both structured and unstructured is a big technological challenge and thus provides a great opportunity for IT and technology professionals world wide. There is just too much data and very few professionals to manage and analyze it. We bring together a comprehensive course which will help you master the concepts, technologies and processes involved in BigData.

In this course we will primarily cover MapReduce and its most popular implementation the Apache Hadoop. We will also cover Hadoop ecosystems and practical concepts involved in handling very large data.

The MapReduce Algorithm is used in Big Data to scale computations. Running in parallel the map reduce algorithms load a manageable chunk of data into RAM, perform some intermediate calculations, load the next chunk and keep going until all of the data has been processed. In its simplest representation it can be broken down into a Map step that often takes data set we can think of as ‘unstructured’ the a Reduce step that outputs a ‘structured’ data set often smaller.

In its simplest sense Hadoop is an implementation of the MapReduce Algorithm.

Topics Covered

SECTION 1: Introduction to Big Data

SECTION 2: Hadoop Architecture

SECTION 3: Distributed file systems

SECTION 4: Mapreduce Version 1

SECTION 5: Mapreduce with Hive ( Data warehousing )

SECTION 6: Mapreduce with Pig (Parallel processing)

SECTION 7: The Hadoop Ecosystem

SECTION 8: Mapreduce Version 2

SECTION 9: Putting it all together

Who should attend

Big Data professionals who want to Master MapReduce and Hadoop.
IT professionals and managers who want to understand and learn this hot new technology

Pre-requisites

A familiarity of programming in Java.
A familiarity of Linux
Have Oracle Virtualbox or VMware installed and functioning

Key Takeaways

Over 42 lectures and 16 hours of content!
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
Curriculum

Chapter 1 : Introduction to Big Data

  • Why Hadoop%2C Big Data and Map Reduce 33.68PREVIEW

  • Architecture of Clusters 19.17

  • Virtual Machine (VM), Provisioning a VM with vagrant and puppet 15.92

  • Why Hadoop, Big Data and Map Reduce -

  • Architecture of Clusters -

  • Virtual Machine (VM), Provisioning a VM with vagrant and puppet -

Chapter 2 : Hadoop Architecture

  • Set up a single Node Hadoop pseudo cluster 35.85

  • Clusters and Nodes, Hadoop Cluster 27.67

  • NameNode, Secondary Name Node, Data Nodes 21.33

  • Running Multi node clusters on Amazons EMR 63.83

  • Set up a single Node Hadoop pseudo cluster -

  • Clusters and Nodes, Hadoop Cluster -

  • NameNode, Secondary Name Node, Data Nodes -

  • Running Multi node clusters on Amazons EMR -

Chapter 3 : Distributed file systems

  • HDFS vs GFS a comparison 19.5

  • Run hadoop on Cloudera, Web Administration 17.55

  • Run hadoop on Hortonworks Sandbox 19.25

  • File system operations with the HDFS shell 33.68

  • Advanced hadoop development with Apache Bigtop 20.38

  • HDFS vs GFS a comparison -

  • File system operations with the HDFS shell -

  • Advanced hadoop development with Apache Bigtop -

Chapter 4 : Mapreduce Version 1

  • MapReduce Concepts in detail 24.13

  • Jobs definition, Job configuration, submission, execution and monitoring 37.2

  • Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers 39.08

  • The ETL class, Definition, Extract, Transform, and Load 39.48

  • The UDF class, Definition, User Defined Functions 25.35

  • MapReduce Concepts in detail -

  • Jobs definition, Job configuration, submission, execution and monitoring -

  • Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers -

  • The ETL class, Definition, Extract, Transform, and Load -

  • The UDF class, Definition, User Defined Functions -

Chapter 5 : Mapreduce with Hive ( Data warehousing )

  • Schema design for a Data warehouse 25.48

  • Hive Configuration 17.9

  • Hive Query Patterns 43.48

  • Example Hive ETL class 25.23

  • Schema design for a Data warehouse -

  • Hive Configuration -

  • Hive Query Patterns -

  • Example Hive ETL class -

Chapter 6 : Mapreduce with Pig (Parallel processing)

  • Introduction to Apache Pig 45.35

  • Pig LoadFunc and EvalFunc classes 13.48

  • Example Pig ETL class 26.87

  • Introduction to Apache Pig -

  • Pig LoadFunc and EvalFunc classes -

  • Example Pig ETL class -

Chapter 7 : The Hadoop Ecosystem

  • Introduction to Crunch 28.23

  • Introduction to Arvo 15.32

  • Introduction to Mahout 39.5

  • Introduction to Crunch -

  • Introduction to Arvo -

  • Introduction to Mahout -

Chapter 8 : Mapreduce Version 2

  • Apache Hadoop 2 and YARN 21.15

  • Yarn Examples 14.87

  • Apache Hadoop 2 and YARN -

  • Yarn Examples -

Chapter 9 : Putting it all together

  • Amazon EMR example 42.58

  • Apache Bigtop example 80.03

  • Amazon EMR example -

  • Apache Bigtop example -

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Content

9 Chapters

31 Video Lectures

29 Documents

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Course Id: 20399