Section 1 : Introduction to Flink
Lecture 1 Flink Introduction 00:04:15 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 3 Batch Processing Vs Stream Processing 00:04:15 Duration
Lecture 4 Hadoop Vs Streaming Engines (Spark & Flink)
Lecture 5 Spark Vs Flink 00:11:19 Duration
Lecture 6 Flink ArchitectureEcosystem 00:02:56 Duration
Lecture 7 Flink's programming model Flow of a Flink program 00:12:01 Duration
Lecture 8 Installing Flink 00:07:42 Duration

Section 2 : Transformation operations of DataSet API
Lecture 1 Default Code structure of a Flink Program 00:04:49 Duration
Lecture 2 WordCount using Map, Flatmap, Filter, groupby Part 1 00:11:31 Duration
Lecture 3 WordCount using Map, Flatmap, Filter, groupby Part 2 00:03:49 Duration
Lecture 4 Joins - Inner join 00:07:01 Duration
Lecture 5 Joins - Left, Right & Full Outer Join 00:05:30 Duration
Lecture 6 Join Hints for Optimization (Exclusive feature) 00:06:30 Duration

Section 3 : DataStream API Operations
Lecture 1 Data Sources & Sinks of Datastream API 00:10:06 Duration
Lecture 2 First program using Datastream API
Lecture 3 Reduce Operation 00:06:41 Duration
Lecture 4 Fold Operation 00:02:46 Duration
Lecture 5 Aggregation Operations in Flink 00:05:47 Duration
Lecture 6 Split Operation 00:03:30 Duration
Lecture 7 Iterate Operator 00:05:40 Duration

Section 4 : Windows in Flink
Lecture 1 Introduction to Windowing 00:04:40 Duration
Lecture 2 Window Assigners 00:01:55 Duration
Lecture 3 Various Time Notions of Windows in Flink 00:03:36 Duration
Lecture 4 Tumbling Windows Implementation 00:06:38 Duration
Lecture 5 Sliding Windows Implementation 00:02:43 Duration
Lecture 6 Session Windows Implementation 00:05:15 Duration
Lecture 7 Global Windows Implementation 00:03:41 Duration

Section 5 : Triggers & Evictors
Lecture 1 Triggers in Windows 00:07:48 Duration
Lecture 2 Evictors for Windows 00:05:42 Duration

Section 6 : Watermarks and Late elements
Lecture 1 Watermarks, Late Elements & Allowed Lateness 00:08:41 Duration
Lecture 2 How to generate Watermarks 00:05:49 Duration

Section 7 : State, Checkpointing and Fault tolerance
Lecture 1 What is a State in Flink 00:05:48 Duration
Lecture 2 Checkpointing and Barrier Snapshoting
Lecture 3 Incremental Checkpointing (New Feature) 00:04:18 Duration
Lecture 4 Types of States 00:02:37 Duration
Lecture 5 Value State Implementation 00:08:08 Duration
Lecture 6 List State Implementation 00:02:26 Duration
Lecture 7 Reducing State Implementation 00:02:22 Duration
Lecture 8 Managed Operator State Implementation 00:06:50 Duration
Lecture 9 Implement Checkpointing in a Flink Program 00:08:38 Duration
Lecture 10 The Broadcast State Implementation
Lecture 11 Queryable State (Beta Version) 00:10:47 Duration

Section 8 : Interacting with Real-Time Data using Kafka
Lecture 1 Getting Twitter data using its APIs 00:13:39 Duration
Lecture 2 Adding Kafka to Flink as a Data source

Section 9 : Case Study 1 - Twitter data analysis using Flink
Lecture 1 Twitter data analysis using Flink 00:12:35 Duration

Section 10 : Case Study 2 - Bank Real-Time Fraud detection
Lecture 1 Identifying Fraud transactions in Real-Time 00:14:48 Duration

Section 11 : Case Study 3 - Stock data processing in Real-Time
Lecture 1 Stock Real-Time Data Processing Part 1 00:05:19 Duration
Lecture 2 Stock Real-Time Data Processing Part 2 00:11:08 Duration

Section 12 : Table & Sql API Relational APIs of Flink
Lecture 1 Introduction to Table & Sql API 00:03:22 Duration
Lecture 2 How to register a Table in Relational APIs 00:07:37 Duration
Lecture 3 Writing Queries in Table & Sql API 00:05:00 Duration

Section 13 : Gelly API for Graph Processing
Lecture 1 What is a Graph 00:06:24 Duration
Lecture 2 Calculate Friends of Friends of a Person using GELLY Api 00:06:44 Duration