Analyzing Large Data Sets with Apache Spark

What’s included

$14.99 / $24.99

Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.

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Video Courses

Getting Started with Spark

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

1. Introduction

2m 16s

2. How to Use This Course

1m 41s

3. [Activity]Getting Set Up: Installing Python, a JDK, Spark, and its Dependencies.

14m 50s

4. [Activity] Installing the MovieLens Movie Rating Dataset

3m 35s

5. [Activity] Run your first Spark program! Ratings histogram example.

4m 52s

Spark Basics and Simple Examples

Lectures	Duration
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. 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

Advanced Examples of Spark Programs

Lectures	Duration
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. [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!

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

Running Spark on a Cluster

Lectures	Duration
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 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

SparkSQL, DataFrames, and DataSets

Lectures	Duration
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 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

Other Spark Technologies and Libraries

Lectures	Duration
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

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