Section 1 : Introduction

Lecture 1 Introduction to Apache Beam 5:31
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 3 Evolution of Big data Frameworks 6:4
Lecture 4 Architecture of Apache Beam 5:53
Lecture 5 Flow of Beam's Programming Model 3:5
Lecture 6 Basic Terminologies in Beam 5:54
Lecture 7 Installation 8:7

Section 2 : Transformations in Beam

Lecture 8 Structure of a Beam Pipeline 5:25
Lecture 9 Various Read Transforms in Beam 8:42
Lecture 10 Create Transform 3:18
Lecture 11 Various Write Transforms in Beam 6:0
Lecture 12 Map, FlatMap & Filter - Part 1 11:6
Lecture 13 Map, FlatMap & Filter - Part 2 2:36
Lecture 14 Branching Pipelines 6:23
Lecture 15 ParDo Transform 8:13
Lecture 16 Advanced Combiner of Beam 7:32
Lecture 17 Create Composite Transforms 5:42
Lecture 18 CoGroupBy for Joins 4:9
Lecture 19 How to access files from Google Drive Text

Section 3 : Side Inputs and Outputs

Lecture 20 Side Inputs 3:45
Lecture 21 Additional Outputs in Pipeline 5:20

Section 4 : Real Time Case Study - Identifying Bank's Defaulter Customers

Lecture 22 Introduction to Case Study 1:57
Lecture 23 Requirements and Data walk-through for Card skippers 4:12
Lecture 24 Identifying Credit card payment skippers 4:27
Lecture 25 Requirements and Data walk-through for Loan Deafulters 2:17
Lecture 26 Identifying Loan Defaulters - Part 1
Lecture 27 Identifying Loan Defaulters - Part 2 4:57

Section 5 : Data encoding & decoding

Lecture 28 Data encoding in Beam 6:28
Lecture 29 Coder class in Beam

Section 6 : Type Hints in Beam

Lecture 30 What is Type Safety and How Beam ensures it 7:22
Lecture 31 Using Type Hints 1:41

Section 7 : Build Streaming data Pipelines

Lecture 32 Introduction to Streaming 2:52
Lecture 33 PubSub Streaming Architecture 7:13
Lecture 34 Beam connection with Google Cloud Text
Lecture 35 Setting up GCP's PubSub Project 7:19
Lecture 36 Run a Demo streaming pipeline on GCP 6:10

Section 8 : Implementing Windows in Apache Beam

Lecture 37 Introduction to Windows in Beam 4:45
Lecture 38 Mobile Game Example 3:40
Lecture 39 Time Notions in Streaming Frameworks
Lecture 40 What are Tumbling & Sliding Windows 3:20
Lecture 41 Implementing Tumbling Windows 9:18
Lecture 42 Recommendation for Windowing 3:16
Lecture 43 Implementing Sliding Windows
Lecture 44 Session Windows & its implementation
Lecture 45 Global Windows & its implementation 4:16

Section 9 : Watermarks in Streaming environment

Lecture 46 What is Watermark 7:31

Section 10 : Triggers and its Implementation

Lecture 47 How Beam handles late elements using Triggers 6:36
Lecture 48 Types of Triggers & their implementation 8:7
Lecture 49 Composite Triggers 2:36

Section 11 : Real Time Case Study - Mobile Game Analysis

Lecture 50 Requirements & Code walk-through 5:8
Lecture 51 Pipeline for incrementing scores 4:30
Lecture 52 Pipeline to identify Player's skilled weapon 5:2

Section 12 : Deploy Beam pipeline on Google Cloud Dataflow

Lecture 53 Create Pipeline with Options 6:47
Lecture 54 Deploy it on GCP (Dataflow) 9:32

Section 13 : Write to BigQuery Tables

Lecture 55 Introduction 3:15
Lecture 56 Create BigQuery Dataset 3:18
Lecture 57 Create and Load BigQuery Table 6:34
Lecture 58 BigQuery Dashboard 2:2

Section 14 : BONUS

Lecture 59 About Certification Pdf
Lecture 60 Batch Vs Stream Processing 4:15
Lecture 61 Flink Vs Spark 11:20
Lecture 62 GCP Big data ecosystem 4:48