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Analyzing Large Data Sets with Apache Spark

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101 Students Enrolled 42 Lectures 04:54:45 Hours

Curriculum For This Course

1

Getting Started with Spark

5Lectures

Time 00:27:14

2

Spark Basics and Simple Examples

11Lectures

Time 01:34:28

3

Advanced Examples of Spark Programs

10Lectures

Time 01:12:40

4

Running Spark on a Cluster

8Lectures

Time 00:49:01

5

SparkSQL, DataFrames, and DataSets

3Lectures

Time 00:20:16

6

Other Spark Technologies and Libraries

5Lectures

Time 00:31:06

Getting Started with Spark
	1. Introduction	02:16
	2. How to Use This Course	01:41
	3. [Activity]Getting Set Up: Installing Python, a JDK, Spark, and its Dependencies.	14:50
	4. [Activity] Installing the MovieLens Movie Rating Dataset	03:35
	5. [Activity] Run your first Spark program! Ratings histogram example.	04:52

Spark Basics and Simple Examples
	1. Introduction to Spark	10:11
	2. The Resilient Distributed Dataset (RDD)	12:17
	3. Ratings Histogram Walkthrough	13:33
	4. Key/Value RDD's, and the Average Friends by Age Example	16:13
	5. [Activity] Running the Average Friends by Age Example	05:39
	6. Filtering RDD's, and the Minimum Temperature by Location Example	08:10
	7. [Activity]Running the Minimum Temperature Example, and Modifying it for Maximums	05:08
	8. [Activity] Running the Maximum Temperature by Location Example	03:21
	9. [Activity] Counting Word Occurrences using flatmap()	07:28
	10. [Activity] Improving the Word Count Script with Regular Expressions	04:44
	11. [Activity] Sorting the Word Count Results	07:44

Advanced Examples of Spark Programs
	1. [Activity] Find the Most Popular Movie	05:52
	2. [Activity] Use Broadcast Variables to Display Movie Names Instead of ID Numbers	08:23
	3. Find the Most Popular Superhero in a Social Graph	04:29
	4. [Activity] Run the Script - Discover Who the Most Popular Superhero is!	06:00
	5. Superhero Degrees of Separation: Introducing Breadth-First Search	07:54
	6. Superhero Degrees of Separation: Accumulators, and Implementing BFS in Spark	06:44
	7. [Activity] Superhero Degrees of Separation: Review the Code and Run it	09:14
	8. Item-Based Collaborative Filtering in Spark, cache(), and persist()	10:12
	9. [Activity] Running the Similar Movies Script using Spark's Cluster Manager	10:54
	10. [Exercise] Improve the Quality of Similar Movies	02:58

Running Spark on a Cluster
	1. Introducing Elastic MapReduce	05:08
	2. [Activity] Setting up your AWS / Elastic MapReduce Account and Setting Up PuTTY	09:55
	3. Partitioning	04:21
	4. Create Similar Movies from One Million Ratings - Part 1	05:12
	5. [Activity] Create Similar Movies from One Million Ratings - Part 2	11:27
	6. Create Similar Movies from One Million Ratings - Part 3	03:28
	7. Troubleshooting Spark on a Cluster	03:43
	8. More Troubleshooting, and Managing Dependencies	05:47

SparkSQL, DataFrames, and DataSets
	1. Introducing SparkSQL	06:08
	2. Executing SQL commands and SQL-style functions on a DataFrame	08:16
	3. Using DataFrames instead of RDD's	05:52

Other Spark Technologies and Libraries
	1. Introducing MLLib	08:10
	2. [Activity] Using MLLib to Produce Movie Recommendations	02:56
	3. Analyzing the ALS Recommendations Results	04:53
	4. Using DataFrames with MLLib	07:31
	5. Spark Streaming and GraphX	07:36

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