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Taming Big Data with MapReduce and Hadoop – Hands On!

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Taming Big Data with MapReduce and Hadoop – Hands On!, available at $74.99, has an average rating of 4.45, with 64 lectures, based on 2952 reviews, and has 24343 subscribers.

You will learn about Understand how MapReduce can be used to analyze big data sets Write your own MapReduce jobs using Python and MRJob Run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce Chain MapReduce jobs together to analyze more complex problems Analyze social network data using MapReduce Analyze movie ratings data using MapReduce and produce movie recommendations with it. Understand other Hadoop-based technologies, including Hive, Pig, and Spark Understand what Hadoop is for, and how it works This course is ideal for individuals who are This course is best for students with some prior programming or scripting ability. We will treat you as a beginner when it comes to MapReduce and getting everything set up for writing MapReduce jobs with Python, MRJob, and Amazon's Elastic MapReduce service – but we won't spend a lot of time teaching you how to write code. The focus is on framing data analysis problems as MapReduce problems and running them either locally or on a Hadoop cluster. If you don't know Python, you'll need to be able to pick it up based on the examples we give. If you're new to programming, you'll want to learn a programming or scripting language before taking this course. It is particularly useful for This course is best for students with some prior programming or scripting ability. We will treat you as a beginner when it comes to MapReduce and getting everything set up for writing MapReduce jobs with Python, MRJob, and Amazon's Elastic MapReduce service – but we won't spend a lot of time teaching you how to write code. The focus is on framing data analysis problems as MapReduce problems and running them either locally or on a Hadoop cluster. If you don't know Python, you'll need to be able to pick it up based on the examples we give. If you're new to programming, you'll want to learn a programming or scripting language before taking this course.

Enroll now: Taming Big Data with MapReduce and Hadoop – Hands On!

Summary

Title: Taming Big Data with MapReduce and Hadoop – Hands On!

Price: $74.99

Average Rating: 4.45

Number of Lectures: 64

Number of Published Lectures: 55

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 55

Original Price: $79.99

Quality Status: approved

Status: Live

What You Will Learn

  • Understand how MapReduce can be used to analyze big data sets
  • Write your own MapReduce jobs using Python and MRJob
  • Run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce
  • Chain MapReduce jobs together to analyze more complex problems
  • Analyze social network data using MapReduce
  • Analyze movie ratings data using MapReduce and produce movie recommendations with it.
  • Understand other Hadoop-based technologies, including Hive, Pig, and Spark
  • Understand what Hadoop is for, and how it works

Who Should Attend

  • This course is best for students with some prior programming or scripting ability. We will treat you as a beginner when it comes to MapReduce and getting everything set up for writing MapReduce jobs with Python, MRJob, and Amazon's Elastic MapReduce service – but we won't spend a lot of time teaching you how to write code. The focus is on framing data analysis problems as MapReduce problems and running them either locally or on a Hadoop cluster. If you don't know Python, you'll need to be able to pick it up based on the examples we give. If you're new to programming, you'll want to learn a programming or scripting language before taking this course.

Target Audiences

  • This course is best for students with some prior programming or scripting ability. We will treat you as a beginner when it comes to MapReduce and getting everything set up for writing MapReduce jobs with Python, MRJob, and Amazon's Elastic MapReduce service – but we won't spend a lot of time teaching you how to write code. The focus is on framing data analysis problems as MapReduce problems and running them either locally or on a Hadoop cluster. If you don't know Python, you'll need to be able to pick it up based on the examples we give. If you're new to programming, you'll want to learn a programming or scripting language before taking this course.

“Big data” analysis is a hot and highly valuable skill – and this course will teach you two technologies fundamental to big data quickly: MapReduceand Hadoop. Ever wonder how Google manages to analyze the entire Internet on a continual basis? You’ll learn those same techniques, using your own Windows system right at home.

Learn and master the art of framing data analysis problems as MapReduce problems through over 10 hands-on examples, and then scale them up to run on cloud computing services in this course. You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb.

  • Learn the concepts of MapReduce
  • Run MapReduce jobs quickly using Pythonand MRJob
  • Translate complex analysis problems into multi-stage MapReduce jobs
  • Scale up to larger data sets using Amazon’s Elastic MapReduce service
  • Understand how Hadoop distributes MapReduce across computing clusters
  • Learn about other Hadoop technologies, like Hive, Pig, and Spark

By the end of this course, you’ll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes.

We’ll have some fun along the way. You’ll get warmed up with some simple examples of using MapReduce to analyze movie ratings data and text in a book. Once you’ve got the basics under your belt, we’ll move to some more complex and interesting tasks. We’ll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We’ll analyze a social graph of superheroes, and learn who the most “popular” superhero is – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You’ll find the answer.

This course is very hands-on; you’ll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. Over 5 hours of video content is included, with over 10 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Hadoop-based technologies, including Hive, Pig, and the very hot Spark framework – complete with a working example in Spark.

Don’t take my word for it – check out some of our unsolicited reviews from real students:

“I have gone through many courses on map reduce; this is undoubtedly the best, way at the top.”

“This is one of the best courses I have ever seen since 4 years passed I am using Udemy for courses.”

“The best hands on course on MapReduce and Python.I really like the run it yourself approach in this course. Everything is well organized, and the lecturer is top notch.”

Course Curriculum

Chapter 1: Introduction, and Getting Started

Lecture 1: Introduction

Lecture 2: Udemy 101: Getting the Most From This Course

Lecture 3: Note: Alternate download link for the MovieLens data set

Lecture 4: Getting Started – Run your First MapReduce Program!

Chapter 2: Understanding MapReduce

Lecture 1: MapReduce Basic Concepts

Lecture 2: A quick note on file names.

Lecture 3: Walkthrough of Rating Histogram Code

Lecture 4: Understanding How MapReduce Scales / Distributed Computing

Lecture 5: Average Friends by Age Example: Part 1

Lecture 6: Average Friends by Age Example: Part 2

Lecture 7: Minimum Temperature By Location Example

Lecture 8: Maximum Temperature By Location Example

Lecture 9: Word Frequency in a Book Example

Lecture 10: Making the Word Frequency Mapper Better with Regular Expressions

Lecture 11: Sorting the Word Frequency Results Using Multi-Stage MapReduce Jobs

Lecture 12: Activity: Design a Mapper and Reducer for Total Spent by Customer

Lecture 13: Activity: Write Code for Total Spent by Customer

Lecture 14: Compare Your Code to Mine. Activity: Sort Results by Amount Spent

Lecture 15: Compare your Code to Mine for Sorted Results.

Lecture 16: Combiners

Chapter 3: Advanced MapReduce Examples

Lecture 1: Example: Most Popular Movie

Lecture 2: Including Ancillary Lookup Data in the Example

Lecture 3: Example: Most Popular Superhero, Part 1

Lecture 4: Example: Most Popular Superhero, Part 2

Lecture 5: Example: Degrees of Separation: Concepts

Lecture 6: Degrees of Separation: Preprocessing the Data

Lecture 7: Degrees of Separation: Code Walkthrough

Lecture 8: Degrees of Separation: Running and Analyzing the Results

Lecture 9: Example: Similar Movies Based on Ratings: Concepts

Lecture 10: Similar Movies: Code Walkthrough

Lecture 11: Similar Movies: Running and Analyzing the Results

Lecture 12: Learning Activity: Improving our Movie Similarities MapReduce Job

Chapter 4: Using Hadoop and Elastic MapReduce

Lecture 1: Fundamental Concepts of Hadoop

Lecture 2: The Hadoop Distributed File System (HDFS)

Lecture 3: Apache YARN

Lecture 4: Hadoop Streaming: How Hadoop Runs your Python Code

Lecture 5: Setting Up Your Amazon Elastic MapReduce Account

Lecture 6: Linking Your EMR Account with MRJob

Lecture 7: Exercise: Run Movie Recommendations on Elastic MapReduce

Lecture 8: Analyze the Results of Your EMR Job

Chapter 5: Advanced Hadoop and EMR

Lecture 1: Distributed Computing Fundamentals

Lecture 2: Activity: Running Movie Similarities on Four Machines

Lecture 3: Analyzing the Results of the 4-Machine Job

Lecture 4: Troubleshooting Hadoop Jobs with EMR and MRJob, Part 1

Lecture 5: Troubleshooting Hadoop Jobs, Part 2

Lecture 6: ml-1m Dataset: Alternate Download Link

Lecture 7: Analyzing One Million Movie Ratings Across 16 Machines, Part 1

Lecture 8: Analyzing One Million Movie Ratings Across 16 Machines, Part 2

Chapter 6: Other Hadoop Technologies

Lecture 1: Introducing Apache Hive

Lecture 2: Introducing Apache Pig

Lecture 3: Apache Spark: Concepts

Lecture 4: Spark Example: Part 1

Lecture 5: Spark Example: Part 2

Lecture 6: Congratulations!

Chapter 7: Where to Go from Here

Lecture 1: Bonus Lecture: More courses to explore!

Instructors

Rating Distribution

  • 1 stars: 24 votes
  • 2 stars: 35 votes
  • 3 stars: 260 votes
  • 4 stars: 1024 votes
  • 5 stars: 1609 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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