Section 1 : Introduction
Lecture 1 Introduction to Apache Beam 00:05:31 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 3 Evolution of Big data Frameworks 00:06:04 Duration
Lecture 4 Architecture of Apache Beam 00:05:53 Duration
Lecture 5 Flow of Beam's Programming Model 00:03:05 Duration
Lecture 6 Basic Terminologies in Beam 00:05:54 Duration
Lecture 7 Installation 00:08:07 Duration

Section 2 : Transformations in Beam
Lecture 1 Structure of a Beam Pipeline 00:05:25 Duration
Lecture 2 Various Read Transforms in Beam 00:08:42 Duration
Lecture 3 Create Transform 00:03:18 Duration
Lecture 4 Various Write Transforms in Beam 00:06:00 Duration
Lecture 5 Map, FlatMap & Filter - Part 1 00:11:06 Duration
Lecture 6 Map, FlatMap & Filter - Part 2 00:02:36 Duration
Lecture 7 Branching Pipelines 00:06:23 Duration
Lecture 8 ParDo Transform 00:08:13 Duration
Lecture 9 Advanced Combiner of Beam 00:07:32 Duration
Lecture 10 Create Composite Transforms 00:05:42 Duration
Lecture 11 CoGroupBy for Joins 00:04:09 Duration
Lecture 12 How to access files from Google Drive

Section 3 : Side Inputs and Outputs
Lecture 1 Side Inputs 00:03:45 Duration
Lecture 2 Additional Outputs in Pipeline 00:05:20 Duration

Section 4 : Real Time Case Study - Identifying Bank's Defaulter Customers
Lecture 1 Introduction to Case Study 00:01:57 Duration
Lecture 2 Requirements and Data walk-through for Card skippers 00:04:12 Duration
Lecture 3 Identifying Credit card payment skippers 00:04:27 Duration
Lecture 4 Requirements and Data walk-through for Loan Deafulters 00:02:17 Duration
Lecture 5 Identifying Loan Defaulters - Part 1
Lecture 6 Identifying Loan Defaulters - Part 2 00:04:57 Duration

Section 5 : Data encoding & decoding
Lecture 1 Data encoding in Beam 00:06:28 Duration
Lecture 2 Coder class in Beam

Section 6 : Type Hints in Beam
Lecture 1 What is Type Safety and How Beam ensures it 00:07:22 Duration
Lecture 2 Using Type Hints 00:01:41 Duration

Section 7 : Build Streaming data Pipelines
Lecture 1 Introduction to Streaming 00:02:52 Duration
Lecture 2 PubSub Streaming Architecture 00:07:13 Duration
Lecture 3 Beam connection with Google Cloud
Lecture 4 Setting up GCP's PubSub Project 00:07:19 Duration
Lecture 5 Run a Demo streaming pipeline on GCP 00:06:10 Duration

Section 8 : Implementing Windows in Apache Beam
Lecture 1 Introduction to Windows in Beam 00:04:45 Duration
Lecture 2 Mobile Game Example 00:03:40 Duration
Lecture 3 Time Notions in Streaming Frameworks
Lecture 4 What are Tumbling & Sliding Windows 00:03:20 Duration
Lecture 5 Implementing Tumbling Windows 00:09:18 Duration
Lecture 6 Recommendation for Windowing 00:03:16 Duration
Lecture 7 Implementing Sliding Windows
Lecture 8 Session Windows & its implementation
Lecture 9 Global Windows & its implementation 00:04:16 Duration

Section 9 : Watermarks in Streaming environment
Lecture 1 What is Watermark 00:07:31 Duration

Section 10 : Triggers and its Implementation
Lecture 1 How Beam handles late elements using Triggers 00:06:36 Duration
Lecture 2 Types of Triggers & their implementation 00:08:07 Duration
Lecture 3 Composite Triggers 00:02:36 Duration

Section 11 : Real Time Case Study - Mobile Game Analysis
Lecture 1 Requirements & Code walk-through 00:05:08 Duration
Lecture 2 Pipeline for incrementing scores 00:04:30 Duration
Lecture 3 Pipeline to identify Player's skilled weapon 00:05:02 Duration

Section 12 : Deploy Beam pipeline on Google Cloud Dataflow
Lecture 1 Create Pipeline with Options 00:06:47 Duration
Lecture 2 Deploy it on GCP (Dataflow) 00:09:32 Duration

Section 13 : Write to BigQuery Tables
Lecture 1 Introduction 00:03:15 Duration
Lecture 2 Create BigQuery Dataset 00:03:18 Duration
Lecture 3 Create and Load BigQuery Table 00:06:34 Duration
Lecture 4 BigQuery Dashboard 00:02:02 Duration

Section 14 : BONUS
Lecture 1 About Certification
Lecture 2 Batch Vs Stream Processing 00:04:15 Duration
Lecture 3 Flink Vs Spark 00:11:20 Duration
Lecture 4 GCP Big data ecosystem 00:04:48 Duration