Analyzing Large Data Sets with Apache Spark
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.
What’s included
- 42 : Lectures
- 4h 54m 45s : Duration
$14.99/24.99
Getting Started with Spark
| Lectures |
|---|
| 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 |
|---|
| 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 |
|---|
| 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 |
Running Spark on a Cluster
| Lectures |
|---|
| 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 |
|---|
| 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 |
|---|
| 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 |
