Section 1 : Introduction, and Getting Started
Lecture 1 Note Alternate download link for the MovieLens data set
Lecture 2 Getting Started - Run your First MapReduce Program! 00:10:02 Duration

Section 2 : Understanding MapReduce
Lecture 1 MapReduce Basic Concepts
Lecture 2 A quick note on file names
Lecture 3 Walkthrough of Rating Histogram Code 00:10:39 Duration
Lecture 4 Understanding How MapReduce Scales Distributed Computing
Lecture 5 Average Friends by Age Example Part 1 00:03:05 Duration
Lecture 6 Average Friends by Age Example Part 2 00:07:14 Duration
Lecture 7 Minimum Temperature By Location Example 00:09:40 Duration
Lecture 8 Maximum Temperature By Location Example
Lecture 9 Word Frequency in a Book Example 00:05:26 Duration
Lecture 10 Making the Word Frequency Mapper Better with Regular Expressions 00:03:16 Duration
Lecture 11 Sorting the Word Frequency Results Using Multi-Stage MapReduce Jobs 00:08:18 Duration
Lecture 12 Activity Design a Mapper and Reducer for Total Spent by Customer 00:02:54 Duration
Lecture 13 Activity Write Code for Total Spent by Customer 00:03:57 Duration
Lecture 14 Compare Your Code to Mine 00:05:39 Duration
Lecture 15 Compare your Code to Mine for Sorted Results 00:03:49 Duration
Lecture 16 Combiners 00:07:27 Duration

Section 3 : Advanced MapReduce Examples
Lecture 1 Example Most Popular Movie 00:07:27 Duration
Lecture 2 Including Ancillary Lookup Data in the Example 00:08:01 Duration
Lecture 3 Example Most Popular Superhero, Part 1
Lecture 4 Example Most Popular Superhero, Part 2 00:06:31 Duration
Lecture 5 Example Degrees of Separation Concepts 00:12:28 Duration
Lecture 6 Degrees of Separation Preprocessing the Data 00:05:15 Duration
Lecture 7 Degrees of Separation Code Walkthrough 00:06:34 Duration
Lecture 8 Degrees of Separation Running and Analyzing the Results 00:05:41 Duration
Lecture 9 Example Similar Movies Based on Ratings Concepts 00:07:25 Duration
Lecture 10 Similar Movies Code Walkthrough 00:07:17 Duration
Lecture 11 Similar Movies Running and Analyzing the Results 00:06:37 Duration
Lecture 12 Learning Activity Improving our Movie Similarities MapReduce Job 00:03:58 Duration

Section 4 : Using Hadoop and Elastic MapReduce
Lecture 1 Fundamental Concepts of Hadoop 00:06:00 Duration
Lecture 2 The Hadoop Distributed File System (HDFS) 00:03:10 Duration
Lecture 3 Apache YARN 00:04:20 Duration
Lecture 4 Hadoop Streaming How Hadoop Runs your Python Code 00:03:37 Duration
Lecture 5 Setting Up Your Amazon Elastic MapReduce Account 00:06:49 Duration
Lecture 6 Linking Your EMR Account with MRJob 00:03:40 Duration
Lecture 7 Exercise Run Movie Recommendations on Elastic MapReduce 00:04:35 Duration
Lecture 8 Analyze the Results of Your EMR Job

Section 5 : Advanced Hadoop and EMR
Lecture 1 Distributed Computing Fundamentals 00:04:33 Duration
Lecture 2 Activity Running Movie Similarities on Four Machines 00:04:28 Duration
Lecture 3 Analyzing the Results of the 4-Machine Job 00:05:44 Duration
Lecture 4 Troubleshooting Hadoop Jobs with EMR and MRJob, Part 1 00:04:01 Duration
Lecture 5 Troubleshooting Hadoop Jobs, Part 2 00:10:28 Duration
Lecture 6 ml-1m Dataset Alternate Download Link
Lecture 7 Analyzing One Million Movie Ratings Across 16 Machines, Part 1 00:06:09 Duration
Lecture 8 Analyzing One Million Movie Ratings Across 16 Machines, Part 2 00:08:02 Duration

Section 6 : Other Hadoop Technologies
Lecture 1 Introducing Apache Hive 00:06:17 Duration
Lecture 2 Introducing Apache Pig 00:03:26 Duration
Lecture 3 Apache Spark Concepts 00:09:37 Duration
Lecture 4 Spark Example Part 1 00:11:15 Duration
Lecture 5 Spark Example Part 2 00:03:22 Duration
Lecture 6 Congratulations! 00:00:41 Duration