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Taming Big Data with MapReduce & Hadoop
Introduction
Introduction (3:22)
Getting Started
New Setup Instructions!
Installing Enthought Canopy (7:44)
Understanding MapReduce
MapReduce Basic Concepts (13:25)
Walkthrough of Rating Histogram Code (10:38)
Understanding How MapReduce Scales / Distributed Computing (3:00)
Average Friends by Age Example: Part 1 (3:04)
Average Friends by Age Example: Part 2 (7:13)
Minimum Temperature By Location Example (9:39)
Maximum Temperature By Location Example (3:22)
Word Frequency in a Book Example (5:25)
Making the Word Frequency Mapper Better with Regular Expressions (3:15)
Sorting the Word Frequency Results Using Multi-Stage MapReduce Jobs (8:18)
Activity: Design a Mapper and Reducer for Total Spent by Customer (2:54)
Activity: Write Code for Total Spent by Customer (3:57)
Compare Your Code to Mine. Activity: Sort Results by Amount Spent (5:38)
Compare your Code to Mine for Sorted Results. (3:49)
Combiners (7:26)
Advanced MapReduce Examples
Example: Most Popular Movie (7:23)
Including Ancillary Lookup Data in the Example (8:00)
Example: Most Popular Superhero, Part 1 (4:22)
Example: Most Popular Superhero, Part 2 (6:31)
Example: Degrees of Separation: Concepts (12:27)
Degrees of Separation: Preprocessing the Data (5:14)
Degrees of Separation: Code Walkthrough (6:34)
Degrees of Separation: Running and Analyzing the Results (5:41)
Example: Similar Movies Based on Ratings: Concepts (7:24)
Similar Movies: Code Walkthrough (7:16)
Similar Movies: Running and Analyzing the Results (6:37)
Learning Activity: Improving our Movie Similarities MapReduce Job (3:58)
Using Hadoop and Elastic MapReduce
Fundamental Concepts of Hadoop (5:59)
The Hadoop Distributed File System (HDFS) (3:09)
Apache YARN (4:20)
Hadoop Streaming: How Hadoop Runs your Python Code (3:37)
Setting Up Your Amazon Elastic MapReduce Account (6:49)
Linking Your EMR Account with MRJob (3:40)
Exercise: Run Movie Recommendations on Elastic MapReduce (4:34)
Analyze the Results of Your EMR Job
Advanced Hadoop and EMR
Distributed Computing Fundamentals (4:33)
Activity: Running Movie Similarities on Four Machines (4:27)
Analyzing the Results of the 4-Machine Job (5:44)
Troubleshooting Hadoop Jobs with EMR and MRJob, Part 1 (4:01)
Troubleshooting Hadoop Jobs, Part 2 (10:28)
Analyzing One Million Movie Ratings Across 16 Machines, Part 1 (6:08)
Analyzing One Million Movie Ratings Across 16 Machines, Part 2 (8:02)
Other Hadoop Technologies
Introducing Apache Hive (6:16)
Introducing Apache Pig (3:26)
Apache Spark: Concepts (9:37)
Spark Example: Part 1 (11:15)
Spark Example: Part 2 (3:22)
Congratulations! (0:41)
Analyzing One Million Movie Ratings Across 16 Machines, Part 1
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MovieSimilaritiesLarge.py
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