Learn By Example : Apache Flink
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Learn By Example : Apache Flink

30 solved examples on Stream and Batch processing
Best Selling
4.2 (52 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,604 students enrolled
Created by Loony Corn
Last updated 3/2017
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Current price: $10 Original price: $50 Discount: 80% off
30-Day Money-Back Guarantee
  • 3 hours on-demand video
  • 5 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Use the DataStream API for transforming streaming data
  • Use the DataSet API for batch processing
  • Apply window operations on Streaming data
  • Use Flink-ML for Machine Learning
  • Use Gelly for Graph processing
View Curriculum
  • Experience in Java programming and familiarity with using Java frameworks
  • Building Jars with Maven, Compiling Java code and debugging
  • Install an IDE like IntelliJ IDEA or Eclipse for Java and Scala programming

Flink is a stream processing technology with added capability to do lots of other things like batch processing, graph algorithms, machine learning etc.  Using Flink you can build applications which need you to be highly responsive to the latest data such as monitoring spikes in payment gateway failures or triggering trades based on live stock price movements. 

This course has 30 Solved Examples on building Flink Applications for both Streaming and Batch Processing

What's covered?

1) Transformations in the DataStream API : filter, map, flatMap and reduce

2) Operations on multiple streams : union, cogroup, connect, comap, join and iterate

3) Window operations : Tumbling, Sliding, Count and Session windows; the notion of time and how to implement custom Window functions 

4) Managing fault-tolerance with State and Checkpointing 

5) Transformations in the DataSet API : filter, map, reduce, reduceGroup

6) Applying ML algorithms on the fly using Flink-ML

7) Representing Graph data using Gelly

Using discussion forums

Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

It is a hard trade-off.

Thank you for your patience and understanding!

Who is the target audience?
  • Yep! Engineers looking to set up end-to-end data processing pipelines that react to changes in real time
  • Yep! Folks familiar with Batch processing technologies like Hadoop who want to learn more about Stream processing
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Curriculum For This Course
41 Lectures
1 Lecture 02:05
Flink's Stream Processing Architecture
4 Lectures 28:49

Flink is billed as a stream processing framework. Let's understand the idea of stream processing

Preview 03:47

Stream processing and batch processing are 2 different data processing paradigms, each with its own set of requirements.

Preview 06:31

Requirements of a Streaming Architecture

Understand how Flink supports stream  processing, and the other functionality it comes with. 

Stream processing with Apache Flink
Getting Started with Flink
2 Lectures 08:18

Setting up your Flink project with Maven
Hello World!
2 Lectures 14:07
Data Representation and Programming Model

Example 1: Writing a Flink program
Transformations using the DataStream API
9 Lectures 33:21
Example 2: The Filter operation

Example 3: The Map operation

Example 4: The FlatMap operation

Stateless and Stateful Transformations

Keyed Streams

Example 5: Creating a stream of Tuples

Example 6: Transformations on Keyed Streams

Example 7: Number aggregations

Example 8: The Reduce Operation
Window Operations
8 Lectures 34:26
Windows Transformation

Example 9 and 10: Keyed vs NonKeyed, Sliding and Tumbling Windows

Example 11: Count Windows

Example 12: Session Windows

Understanding the Window API

Example 13: Implementing a Custom Window Function

Example 14: Changing the time characteristic

Example 15: Twitter Streaming Window
Custom Sources
1 Lecture 03:23
Example 16: Custom Sources
State and Checkpointing
4 Lectures 15:05
Example 17: Value State

Example 18: List State

Example 19: Reducing State

Example 20: Checkpointing and Restart strategies
Operations on Multiple Streams
6 Lectures 18:56
Example 21: Unions

Example 22: Joining Streams

Example 23: coGroup

Example 24: coMap

Example 25: Iterate

Example 26: Split
Transformations in the DataSet API
1 Lecture 06:28
Example 27: Applying Transformations on DataSets
2 More Sections
About the Instructor
Loony Corn
4.3 Average rating
5,071 Reviews
39,359 Students
78 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)