Section 1 : Getting Started with Spark
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Lecture 1 | Introduction | 00:01:39 Duration |
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Lecture 2 | How to Use This Course | 00:01:41 Duration |
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Lecture 3 | INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM | |
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Lecture 4 | [Activity]Getting Set Up Installing Python, a J | 00:14:42 Duration |
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Lecture 5 | [Activity] Installing the MovieLens Movie Ratin | 00:03:35 Duration |
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Lecture 6 | [Activity] Run your first Spark program! Rating | 00:06:12 Duration |
Section 2 : Spark Basics and the RDD Interface
Section 3 : SparkSQL, DataFrames, and DataSets
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Lecture 1 | Introducing SparkSQL | 00:09:29 Duration |
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Lecture 2 | [Activity] Executing SQL commands and SQL-styl | 00:07:52 Duration |
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Lecture 3 | Using DataFrames instead of RDD's | 00:07:40 Duration |
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Lecture 4 | [Exercise] Friends by Age, with DataFrames | 00:01:45 Duration |
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Lecture 5 | Exercise Solution Friends by Age, with DataFra | 00:07:55 Duration |
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Lecture 6 | [Activity] Word Count, with DataFrames | |
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Lecture 7 | [Activity] Minimum Temperature, with DataFrame | 00:10:27 Duration |
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Lecture 8 | [Exercise] Implement Total Spent by Customer | 00:02:08 Duration |
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Lecture 9 | Exercise Solution Total Spent by Customer, wit | 00:04:08 Duration |
Section 4 : Advanced Examples of Spark Programs
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Lecture 1 | [Activity] Find the Most Popular Movie | 00:04:16 Duration |
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Lecture 2 | [Activity] Use Broadcast Variables to Display | 00:10:34 Duration |
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Lecture 3 | Find the Most Popular Superhero in a Social Gr | |
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Lecture 4 | [Activity] Run the Script - Discover Who the M | 00:08:00 Duration |
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Lecture 5 | [Exercise] Find the Most Obscure Superheroes | 00:02:16 Duration |
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Lecture 6 | Exercise Solution Most Obscure Superheroes | 00:04:13 Duration |
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Lecture 7 | Superhero Degrees of Separation Introducing Br | |
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Lecture 8 | Superhero Degrees of Separation Accumulators, | 00:06:45 Duration |
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Lecture 9 | [Activity] Superhero Degrees of Separation Rev | 00:09:35 Duration |
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Lecture 10 | Item-Based Collaborative Filtering in Spark | 00:06:00 Duration |
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Lecture 11 | [Activity] Running the Similar Movies Script u | 00:13:43 Duration |
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Lecture 12 | [Exercise] Improve the Quality of Similar Movi | 00:03:05 Duration |
Section 5 : Running Spark on a Cluster
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Lecture 1 | Introducing Elastic MapReduce | 00:05:09 Duration |
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Lecture 2 | [Activity] Setting up your AWS Elastic MapRed | 00:09:58 Duration |
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Lecture 3 | Partitioning | 00:04:22 Duration |
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Lecture 4 | Create Similar Movies from One Million Ratings | 00:11:27 Duration |
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Lecture 5 | [Activity] Create Similar Movies from One Mill | 00:11:27 Duration |
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Lecture 6 | Create Similar Movies from One Million Ratings | 00:03:30 Duration |
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Lecture 7 | Troubleshooting Spark on a Cluster | 00:03:44 Duration |
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Lecture 8 | More Troubleshooting, and Managing Dependencie | 00:06:02 Duration |
Section 6 : Machine Learning with Spark ML
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Lecture 1 | Introducing MLLib | 00:06:04 Duration |
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Lecture 2 | [Activity] Using Spark ML to Produce Movie Rec | 00:09:55 Duration |
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Lecture 3 | Analyzing the ALS Recommendations Results | |
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Lecture 4 | [Activity] Linear Regression with Spark ML | 00:13:26 Duration |
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Lecture 5 | [Exercise] Using Decision Trees in Spark ML to | 00:05:34 Duration |
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Lecture 6 | Exercise Solution Decision Trees with Spark | 00:06:20 Duration |
Section 7 : Spark Streaming, Structured Streaming, and GraphX
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Lecture 1 | Spark Streaming | 00:08:04 Duration |
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Lecture 2 | [Activity] Structured Streaming in Python | 00:08:48 Duration |
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Lecture 3 | [Exercise] Use Windows with Structured Streami | 00:05:50 Duration |
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Lecture 4 | Exercise Solution Using Structured Streaming w | 00:06:38 Duration |
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Lecture 5 | GraphX | 00:02:11 Duration |