Section 1 : Getting Started

lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 2 About Certification Pdf
lecture 3 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS Pdf
lecture 4 Introduction, and Getting Set Up 14:22
lecture 5 [Activity] Create a Histogram of Real Movie Ratin 14:1

Section 2 : Scala Crash Course [Optional]

lecture 6 [Activity] Scala Basics, Part 1 12:52
lecture 7 [Exercise] Scala Basics, Part 2 9:41
lecture 8 [Exercise] Flow Control in Scala 7:18
lecture 9 [Exercise] Functions in Scala 8:47
lecture 10 [Exercise] Data Structures in Scala 16:38

Section 3 : Spark Basics and Simple Examples

lecture 11 Introduction to Spark 8:41
lecture 12 The Resilient Distributed Dataset 11:4
lecture 13 Ratings Histogram Walkthrough 7:33
lecture 14 Spark Internals 4:42
lecture 15 Key - Value RDD's, and the Average Friends by Age 12:21
lecture 16 [Activity] Running the Average Friends by Age Exam 7:58
lecture 17 Filtering RDD's, and the Minimum Temperature by Lo 6:43
lecture 18 [Activity] Running the Minimum Temperature Example 10:11
lecture 19 [Activity] Counting Word Occurrences using Flatma 8:59
lecture 20 [Activity] Improving the Word Count Script with Re 6:42
lecture 21 [Activity] Sorting the Word Count Results 8:11
lecture 22 [Exercise] Find the Total Amount Spent by Custome 3:38
lecture 23 [Exercise] Check your Results, and Sort Them by To 4:26
lecture 24 Check Your Results and Implementation Against Mine 3:26

Section 4 : Advanced Examples of SparkPrograms'

lecture 25 [Activity] Find the Most Popular Movie 4:30
lecture 26 [Activity] Use Broadcast Variables to Display Movi 8:53
lecture 27 [Activity] Find the Most Popular Superhero in a S 14:10
lecture 28 Superhero Degrees of Separation- Introducing Bread 6:53
lecture 29 Superhero Degrees of Separation- Accumulators, and 5:54
lecture 30 Superhero Degrees of Separation- Review the code, 10:41
lecture 31 Item-Based Collaborative Filtering in Spark, cache 8:17
lecture 32 [Activity] Running the Similar Movies Script usin 14:13
lecture 33 [Exercise] Improve the Quality of Similar Movies 2:41

Section 5 : Running Spark on a Cluster

lecture 34 [Activity] Using spark-submit to run Spark driver 6:59
lecture 35 About Certification Pdf
lecture 36 Introducing Amazon Elastic MapReduce 14:7
lecture 37 Creating Similar Movies from One Million Ratings o 12:47
lecture 38 Partitioning 5:7
lecture 39 Best Practices for Running on a Cluster 5:31
lecture 40 Troubleshooting, and Managing Dependencies 9:8

Section 6 : SparkSQLDataFrames, andDataSets

lecture 41 Introduction to SparkSQL 7:8
lecture 42 [Activity] Using SparkSQ 7:1
lecture 43 [Activity] Using DataFrames and DataSets 6:39
lecture 44 [Activity] Using DataSets instead of RDD's 7:24

Section 7 : Machine Learning with MLLib

lecture 45 Introducing MLLib 7:38
lecture 46 [Activity] Using MLLib to Produce Movie Recommenda 7:23
lecture 47 [Activity] Linear Regression with MLLib 11:37
lecture 48 [Activity] Using DataFrames with MLLib 10:5

Section 8 : Intro to Spark Streaming

lecture 49 Spark Streaming Overview 9:54
lecture 50 [Activity] Set up a Twitter Developer Account, and 12:13
lecture 51 Structured Streaming 4:1

Section 9 : Intro to GraphX

lecture 52 GraphX, Pregel, and Breadth-First-Search with Preg 4:1