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- 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
- 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.
“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. 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 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."
- 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.
I'll walk you through installing Enthought Canopy, the mrjob Python package, and some sample movie ratings data from MovieLens - and then we'll run a simple MapReduce job on your desktop!
In another example, we'll use real weather data from the year 1800 and find the minimum temperature at each weather station for the year.
Using the MovieLens data set, we'll write and run a MapReduce job to find the most-rated movie.
In a more advanced example, we'll describe how to use MapReduce to find degrees of separation between superheroes in a social graph. We'll use a breadth-first-search algorithm in MapReduce to find the answers we want.
In another advanced example, we'll cover item-based collaborative filtering and how it can be used to identify movies similar to each other based on ratings data.
Finally, some truly big data: compute similar movies using one million movie ratings across a cluster of 20 computers.