Taming Big Data with MapReduce and Hadoop - Hands On!

Understand the basic concepts of MapReduce - what a mapper does, what a reducer does, and what happens in between.

We'll analyze the source of your ratings histogram job, and understand how it works.

Understand why MapReduce is a powerful tool for scaling big data analysis problems across compute clusters.

In our next example, we'll look at some fake social data and compute the average number of friends by age.

Actually run the friends by age example on your machine, and analyze the results.

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.

Now, we'll modify that same example to find the maximum temperature for the year, and run it too.

Another hands-on example: we'll find how often each word is used in a real book's text.

We build on the previous example to do a better job of identifying words, using regular expressions in Python.

We'll build further on the same example, this time using MapReduce to sort the results the way we want them, using a multi-stage MapReduce job.

Your first homework assignment! Propose what the mapper and reducer should do for a job that computes the total amount spent by customer in a fake e-commerce data set.

We'll review your approach for the e-commerce problem, and set you loose with the tools you need to go write your first MapReduce job on your own.

Compare your code to mine for analyzing our e-commerce data. Now, build upon your code to sort the final results to find the biggest spender.

We'll review your homework to sort the results of the e-commerce analysis, and compare your code to mine.

Learn how combiners can help reduce network throughput in MapReduce jobs, and run a simple example of using a combiner function.

Using the MovieLens data set, we'll write and run a MapReduce job to find the most-rated movie.

Extend the previous example to send movie ID - to - movie name lookup data along with our MapReduce tasks, so we can display results in human readable format.

We'll introduce the Marvel social graph data set, and cover how we'll find the most "popular" superhero!

Actually implement and run the code to identify the most popular superhero. I bet it's not who you think it is!

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.

First we walk through transforming the Marvel data set into a format usable for the BFS algorithm.

Now we'll cover the code needed to iteratively run breadth-first search using MapReduce, and use Hadoop counters to flag our results.

Actually run the code that allows us to find the degrees of separation between any two superheroes, and analyze the results.

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.

We'll walk through how a creative multi-step MapReduce job can compute similar movies with a surprisingly small amount of code.

We'll run our code on the 100K MovieLens data set, and analyze the results.

Your homework is to modify our program to produce better results, and I'll give you a few ideas on things you might try.

We'll cover what Hadoop is, and how it enables running MapReduce jobs across a cluster of computers.

Learn how HDFS distributes large data sets across a cluster in a reliable manner.

Learn how YARN manages resources on a Hadoop cluster running MapReduce V2

Learn how Hadoop can run mappers and reducers written in any programming language, through Hadoop streaming.

Set up an Amazon Elastic MapReduce account, so you can run larger examples in this course across real compute clusters at very low cost.

Tie your Amazon Elastic MapReduce account to your Python and MRJob development environment.

Run the movie recommendation program in the cloud!

Analyze the results - they should be what you expect, but why did it take so long?

Some basic concepts on distributed computing, and the overhead associated with it.

Learn how to run your movie similarity program on multiple machines in EMR, and actually run it.

Analyze the results of running movie similarities across four machines. It's faster - but there is a downside!

Learn how to troubleshoot EMR / MRJob programs that don't complete successfully.

Hands-on example of troubleshooting a failed job after the fact.

Finally, some truly big data: compute similar movies using one million movie ratings across a cluster of 20 computers.

We'll analyze the results of our one-million-rating analysis, and use a new script to extract the data we want.

A very brief overview of Apache Hive, QL, and a simple example.

A very brief overview of Apache Pig, and a simple example.

An overview of Spark, how it works, and why it might be a better choice than MapReduce for some tasks.

We'll walk through running a real Spark program to analyze gigabytes' worth of airline flight data to identify the worst airports in America.

We'll analyze the results of our Spark program, and find out which airport has the most flight delays.

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