Section 1 : Introduction to Flink

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

Section 2 : Transformation operations of DataSet API

Lecture 9 Default Code structure of a Flink Program 4:49
Lecture 10 WordCount using Map, Flatmap, Filter, groupby Part 1 11:31
Lecture 11 WordCount using Map, Flatmap, Filter, groupby Part 2 3:49
Lecture 12 Joins - Inner join 7:1
Lecture 13 Joins - Left, Right & Full Outer Join 5:30
Lecture 14 Join Hints for Optimization (Exclusive feature) 6:30

Section 3 : DataStream API Operations

Lecture 15 Data Sources & Sinks of Datastream API 10:6
Lecture 16 First program using Datastream API
Lecture 17 Reduce Operation 6:41
Lecture 18 Fold Operation 2:46
Lecture 19 Aggregation Operations in Flink 5:47
Lecture 20 Split Operation 3:30
Lecture 21 Iterate Operator 5:40

Section 4 : Windows in Flink

Lecture 22 Introduction to Windowing 4:40
Lecture 23 Window Assigners 1:55
Lecture 24 Various Time Notions of Windows in Flink 3:36
Lecture 25 Tumbling Windows Implementation 6:38
Lecture 26 Sliding Windows Implementation 2:43
Lecture 27 Session Windows Implementation 5:15
Lecture 28 Global Windows Implementation 3:41

Section 5 : Triggers & Evictors

Lecture 29 Triggers in Windows 7:48
Lecture 30 Evictors for Windows 5:42

Section 6 : Watermarks and Late elements

Lecture 31 Watermarks, Late Elements & Allowed Lateness 8:41
Lecture 32 How to generate Watermarks 5:49

Section 7 : State, Checkpointing and Fault tolerance

Lecture 33 What is a State in Flink 5:48
Lecture 34 Checkpointing and Barrier Snapshoting
Lecture 35 Incremental Checkpointing (New Feature) 4:18
Lecture 36 Types of States 2:37
Lecture 37 Value State Implementation 8:8
Lecture 38 List State Implementation 2:26
Lecture 39 Reducing State Implementation 2:22
Lecture 40 Managed Operator State Implementation 6:50
Lecture 41 Implement Checkpointing in a Flink Program 8:38
Lecture 42 The Broadcast State Implementation
Lecture 43 Queryable State (Beta Version) 10:47

Section 8 : Interacting with Real-Time Data using Kafka

Lecture 44 Getting Twitter data using its APIs 13:39
Lecture 45 Adding Kafka to Flink as a Data source

Section 9 : Case Study 1 - Twitter data analysis using Flink

Lecture 46 Twitter data analysis using Flink 12:35

Section 10 : Case Study 2 - Bank Real-Time Fraud detection

Lecture 47 Identifying Fraud transactions in Real-Time 14:48

Section 11 : Case Study 3 - Stock data processing in Real-Time

Lecture 48 Stock Real-Time Data Processing Part 1 5:19
Lecture 49 Stock Real-Time Data Processing Part 2 11:8

Section 12 : Table & Sql API Relational APIs of Flink

Lecture 50 Introduction to Table & Sql API 3:22
Lecture 51 How to register a Table in Relational APIs 7:37
Lecture 52 Writing Queries in Table & Sql API 5:0

Section 13 : Gelly API for Graph Processing

Lecture 53 What is a Graph 6:24
Lecture 54 Calculate Friends of Friends of a Person using GELLY Api 6:44