Taming Big Data with MapReduce and Hadoop - Hands On! [Video]

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Taming Big Data with MapReduce and Hadoop - Hands On! [Video]

Frank Kane

Master the art of processing Big Data using Hadoop and MapReduce with the help of real-world examples
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Video Details

ISBN 139781787125568
Course Length4 hours 58 minutes

Video Description

Big Data processing is creating a lot of buzz in the market lately, with organizations having to deal with large amounts of data on a daily basis. Processing such data and extracting actionable insights from it is a major challenge; that’s where Hadoop and MapReduce comes to the rescue. This course will teach you how to use MapReduce for Big Data processing – with lots of practical examples and use-cases. You will start with understanding the Hadoop ecosystem and the basics of MapReduce. You will proceed to see how MapReduce can be used to process different types of data – whether it is analyzing movie ratings or your social network data. You will also learn how to run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce. The course wraps up with an overview of other Hadoop-based technologies such as Hive, Pig, and the in-demand Apache Spark.

Table of Contents

Introduction, and Getting Started
Introduction
Getting Started – Run your First MapReduce Program!
Understanding MapReduce
MapReduce Basic Concepts
A Walkthrough of Rating Histogram Code
Understanding How MapReduce Scales / Distributed Computing
Average Friends by Age Example – Part 1
Average Friends by Age Example – Part 2
Minimum Temperature by Location Example
Maximum Temperature by Location Example
Word Frequency in a Book Example
Making the Word Frequency Mapper Better with Regular Expressions
Sorting the Word Frequency Results Using Multi-Stage MapReduce Jobs
Activity: Design a Mapper and Reducer for Total Spent by Customer
Activity: Write Code for Total Spent by Customer
Compare Your Code to Mine – Sort Results by Amount Spent
Compare Your Code to Mine for Sorted Results
Combiners
Advanced MapReduce Examples
Example – Most Popular Movie
Including Ancillary Lookup Data in the Example
Example – Most Popular Superhero Part 1
Example – Most Popular Superhero Part 2
Example: Degrees of Separation – Concepts
Degrees of Separation – Preprocessing the Data
Degrees of Separation – Code Walkthrough
Degrees of Separation – Running and Analyzing the Results
Example – Similar Movies Based on Ratings: Concepts
Similar Movies – Code Walkthrough
Similar Movies – Running and Analyzing the Results
Learning Activity – Improving Our Movie Similarities MapReduce Job
Using Hadoop and Elastic MapReduce
Fundamental Concepts of Hadoop
The Hadoop Distributed File System (HDFS)
Apache YARN
Hadoop Streaming – How Hadoop Runs Your Python Code
Setting Up Your Amazon Elastic MapReduce Account
Linking Your EMR Account with MRJob
Exercise – Run Movie Recommendations on Elastic MapReduce
Analyze the Results of Your EMR Job
Advanced Hadoop and EMR
Distributed Computing Fundamentals
Activity – Running Movie Similarities on Four Machines
Analyzing the Results of the Four-Machine Job
Troubleshooting Hadoop Jobs with EMR and MRJob – Part 1
Troubleshooting Hadoop Jobs – Part 2
Analyzing One Million Movie Ratings across 16 Machines – Part 1
Analyzing One Million Movie Ratings across 16 Machines – Part 2
Other Hadoop Technologies
Introducing Apache Hive
Introducing Apache Pig
Apache Spark – Concepts
Spark Example – Part 1
Spark Example – Part 2
Congratulations!

What You Will Learn

  • Understand what Hadoop is and what it is used for
  • Develop and run MapReduce jobs in Python
  • Use MapReduce to analyze different types like social network data, movie data, and more
  • Run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce
  • Get to grips with the other Hadoop-based techs like Hive, Spark and Pig

Authors

Table of Contents

Introduction, and Getting Started
Introduction
Getting Started – Run your First MapReduce Program!
Understanding MapReduce
MapReduce Basic Concepts
A Walkthrough of Rating Histogram Code
Understanding How MapReduce Scales / Distributed Computing
Average Friends by Age Example – Part 1
Average Friends by Age Example – Part 2
Minimum Temperature by Location Example
Maximum Temperature by Location Example
Word Frequency in a Book Example
Making the Word Frequency Mapper Better with Regular Expressions
Sorting the Word Frequency Results Using Multi-Stage MapReduce Jobs
Activity: Design a Mapper and Reducer for Total Spent by Customer
Activity: Write Code for Total Spent by Customer
Compare Your Code to Mine – Sort Results by Amount Spent
Compare Your Code to Mine for Sorted Results
Combiners
Advanced MapReduce Examples
Example – Most Popular Movie
Including Ancillary Lookup Data in the Example
Example – Most Popular Superhero Part 1
Example – Most Popular Superhero Part 2
Example: Degrees of Separation – Concepts
Degrees of Separation – Preprocessing the Data
Degrees of Separation – Code Walkthrough
Degrees of Separation – Running and Analyzing the Results
Example – Similar Movies Based on Ratings: Concepts
Similar Movies – Code Walkthrough
Similar Movies – Running and Analyzing the Results
Learning Activity – Improving Our Movie Similarities MapReduce Job
Using Hadoop and Elastic MapReduce
Fundamental Concepts of Hadoop
The Hadoop Distributed File System (HDFS)
Apache YARN
Hadoop Streaming – How Hadoop Runs Your Python Code
Setting Up Your Amazon Elastic MapReduce Account
Linking Your EMR Account with MRJob
Exercise – Run Movie Recommendations on Elastic MapReduce
Analyze the Results of Your EMR Job
Advanced Hadoop and EMR
Distributed Computing Fundamentals
Activity – Running Movie Similarities on Four Machines
Analyzing the Results of the Four-Machine Job
Troubleshooting Hadoop Jobs with EMR and MRJob – Part 1
Troubleshooting Hadoop Jobs – Part 2
Analyzing One Million Movie Ratings across 16 Machines – Part 1
Analyzing One Million Movie Ratings across 16 Machines – Part 2
Other Hadoop Technologies
Introducing Apache Hive
Introducing Apache Pig
Apache Spark – Concepts
Spark Example – Part 1
Spark Example – Part 2
Congratulations!

Video Details

ISBN 139781787125568
Course Length4 hours 58 minutes
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