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Analyzing Large Data Sets with Apache Spark

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Curriculum For This Course

  • 1. Introduction to Spark 10m 11s
  • 2. The Resilient Distributed Dataset (RDD) 12m 17s
  • 3. Ratings Histogram Walkthrough 13m 33s
  • 4. Key/Value RDD's, and the Average Friends by Age Example 16m 13s
  • 5. [Activity] Running the Average Friends by Age Example 5m 39s
  • 6. Filtering RDD's, and the Minimum Temperature by Location Example 8m 10s
  • 7. [Activity]Running the Minimum Temperature Example, and Modifying it for Maximums 5m 8s
  • 8. [Activity] Running the Maximum Temperature by Location Example 3m 21s
  • 9. [Activity] Counting Word Occurrences using flatmap() 7m 28s
  • 10. [Activity] Improving the Word Count Script with Regular Expressions 4m 44s
  • 11. [Activity] Sorting the Word Count Results 7m 44s
  • 1. [Activity] Find the Most Popular Movie 5m 52s
  • 2. [Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers 8m 23s
  • 3. Find the Most Popular Superhero in a Social Graph 4m 29s
  • 4. [Activity] Run the Script - Discover Who the Most Popular Superhero is! 6m
  • 5. Superhero Degrees of Separation: Introducing Breadth-First Search 7m 54s
  • 6. Superhero Degrees of Separation: Accumulators, and Implementing BFS in Spark 6m 44s
  • 7. [Activity] Superhero Degrees of Separation: Review the Code and Run it 9m 14s
  • 8. Item-Based Collaborative Filtering in Spark, cache(), and persist() 10m 12s
  • 9. [Activity] Running the Similar Movies Script using Spark's Cluster Manager 10m 54s
  • 10. [Exercise] Improve the Quality of Similar Movies 2m 58s
  • 1. Introducing Elastic MapReduce 5m 8s
  • 2. [Activity] Setting up your AWS / Elastic MapReduce Account and Setting Up PuTTY 9m 55s
  • 3. Partitioning 4m 21s
  • 4. Create Similar Movies from One Million Ratings - Part 1 5m 12s
  • 5. [Activity] Create Similar Movies from One Million Ratings - Part 2 11m 27s
  • 6. Create Similar Movies from One Million Ratings - Part 3 3m 28s
  • 7. Troubleshooting Spark on a Cluster 3m 43s
  • 8. More Troubleshooting, and Managing Dependencies 5m 47s
  • 1. Introducing SparkSQL 6m 8s
  • 2. Executing SQL commands and SQL-style functions on a DataFrame 8m 16s
  • 3. Using DataFrames instead of RDD's 5m 52s
  • 1. Introducing MLLib 8m 10s
  • 2. [Activity] Using MLLib to Produce Movie Recommendations 2m 56s
  • 3. Analyzing the ALS Recommendations Results 4m 53s
  • 4. Using DataFrames with MLLib 7m 31s
  • 5. Spark Streaming and GraphX 7m 36s

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