Taming Big Data with MapReduce and Hadoop – Hands On!
“Big data” analysis is a hot and highly valuable skill – and this course will teach you two technologies fundamental to big data quickly: MapReduce and 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.
Learn the concepts of MapReduce
Run MapReduce jobs quickly using Python and 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.”
What are the requirements?
You’ll need a Windows system, and we’ll walk you through downloading and installing a Python development environment and the tools you need as part of the course. If you’re on Linux and already have a Python development environment in place that you’re familiar with, that’s OK too. Again, be sure you have at least some programming or scripting experience under your belt. You won’t need to be a Python expert to succeed in this course, but you’ll need the fundamental concepts of programming in order to pick up what we’re doing.
What am I going to get from this course?
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
What is the target audience?
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.
|Section 1: Introduction, and Getting Started|
Getting Started – Run your First MapReduce Program!
|Section 2: Understanding MapReduce|
MapReduce Basic Concepts
A quick note on file names.
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. Activity: Sort Results by Amount Spent
Compare your Code to Mine for Sorted Results.
|Section 3: 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
|Section 4: Using Hadoop and Elastic MapReduce|
Fundamental Concepts of Hadoop
The Hadoop Distributed File System (HDFS)
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
|Section 5: Advanced Hadoop and EMR|
Distributed Computing Fundamentals
Activity: Running Movie Similarities on Four Machines
Analyzing the Results of the 4-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
|Section 6: Other Hadoop Technologies|
Introducing Apache Hive
Introducing Apache Pig
Apache Spark: Concepts
Spark Example: Part 1
Spark Example: Part 2
|Section 7: Where to Go from Here|
Bonus Lecture: Discounts on my other courses!
Frank Kane, Data Miner and Software Engineer
Frank Kane spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
- Lectures 0
- Quizzes 0
- Duration 50 hours
- Skill level All level
- Language English
- Students 6241
- Assessments Self