Learn By Example : Apache Flink
30 solved examples on Stream and Batch processing
Ratings 4.35 / 5.00
What You Will 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
Description
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
Who Should Attend!
- 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
