Section 1 : Getting Started with Spark

Lecture 1 Introduction 1:39
Lecture 2 How to Use This Course 1:41
Lecture 3 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 4 [Activity]Getting Set Up Installing Python, a J 14:42
Lecture 5 [Activity] Installing the MovieLens Movie Ratin 3:35
Lecture 6 [Activity] Run your first Spark program! Rating 6:12

Section 2 : Spark Basics and the RDD Interface

Lecture 7 What's new in Spark 3 6:49
Lecture 8 Introduction to Spark 10:11
Lecture 9 The Resilient Distributed Dataset (RDD) 12:35
Lecture 10 Ratings Histogram Walkthrough 13:28
Lecture 11 KeyValue RDD's, and the Average Friends by Age 16:8
Lecture 12 [Activity] Running the Average Friends by Age 5:40
Lecture 13 Filtering RDD's, and the Minimum Temperature b 8:12
Lecture 14 [Activity]Running the Minimum Temperature Exam 5:7
Lecture 15 [Activity] Running the Maximum Temperature by 3:19
Lecture 16 [Activity] Counting Word Occurrences using fla 7:25
Lecture 17 [Activity] Improving the Word Count Script wit 4:42
Lecture 18 [Activity] Sorting the Word Count Results 7:46
Lecture 19 [Exercise] Find the Total Amount Spent by Cust
Lecture 20 [Excercise] Check your Results, and Now Sort t 5:10
Lecture 21 Check Your Sorted Implementation and Results A 2:44

Section 3 : SparkSQL, DataFrames, and DataSets

Lecture 22 Introducing SparkSQL 9:29
Lecture 23 [Activity] Executing SQL commands and SQL-styl 7:52
Lecture 24 Using DataFrames instead of RDD's 7:40
Lecture 25 [Exercise] Friends by Age, with DataFrames 1:45
Lecture 26 Exercise Solution Friends by Age, with DataFra 7:55
Lecture 27 [Activity] Word Count, with DataFrames
Lecture 28 [Activity] Minimum Temperature, with DataFrame 10:27
Lecture 29 [Exercise] Implement Total Spent by Customer 2:8
Lecture 30 Exercise Solution Total Spent by Customer, wit 4:8

Section 4 : Advanced Examples of Spark Programs

Lecture 31 [Activity] Find the Most Popular Movie 4:16
Lecture 32 [Activity] Use Broadcast Variables to Display 10:34
Lecture 33 Find the Most Popular Superhero in a Social Gr
Lecture 34 [Activity] Run the Script - Discover Who the M 8:0
Lecture 35 [Exercise] Find the Most Obscure Superheroes 2:16
Lecture 36 Exercise Solution Most Obscure Superheroes 4:13
Lecture 37 Superhero Degrees of Separation Introducing Br
Lecture 38 Superhero Degrees of Separation Accumulators, 6:45
Lecture 39 [Activity] Superhero Degrees of Separation Rev 9:35
Lecture 40 Item-Based Collaborative Filtering in Spark 6:0
Lecture 41 [Activity] Running the Similar Movies Script u 13:43
Lecture 42 [Exercise] Improve the Quality of Similar Movi 3:5

Section 5 : Running Spark on a Cluster

Lecture 43 Introducing Elastic MapReduce 5:9
Lecture 44 [Activity] Setting up your AWS Elastic MapRed 9:58
Lecture 45 Partitioning 4:22
Lecture 46 Create Similar Movies from One Million Ratings 11:27
Lecture 47 [Activity] Create Similar Movies from One Mill 11:27
Lecture 48 Create Similar Movies from One Million Ratings 3:30
Lecture 49 Troubleshooting Spark on a Cluster 3:44
Lecture 50 More Troubleshooting, and Managing Dependencie 6:2

Section 6 : Machine Learning with Spark ML

Lecture 51 Introducing MLLib 6:4
Lecture 52 [Activity] Using Spark ML to Produce Movie Rec 9:55
Lecture 53 Analyzing the ALS Recommendations Results
Lecture 54 [Activity] Linear Regression with Spark ML 13:26
Lecture 55 [Exercise] Using Decision Trees in Spark ML to 5:34
Lecture 56 Exercise Solution Decision Trees with Spark 6:20

Section 7 : Spark Streaming, Structured Streaming, and GraphX

Lecture 57 Spark Streaming 8:4
Lecture 58 [Activity] Structured Streaming in Python 8:48
Lecture 59 [Exercise] Use Windows with Structured Streami 5:50
Lecture 60 Exercise Solution Using Structured Streaming w 6:38
Lecture 61 GraphX 2:11