Learn By Example: Apache Flink
Self-Paced LearningLearning Style
3 HoursCourse Duration
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About This Course:
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
- Transformations in the DataStream API : filter, map, flatMap and reduce
- Operations on multiple streams : union, cogroup, connect, comap, join and iterate
- Window operations : Tumbling, Sliding, Count and Session windows; the notion of time and how to implement custom Window functions
- Managing fault-tolerance with State and Checkpointing
- Transformations in the DataSet API : filter, map, reduce, reduceGroup
- Applying ML algorithms on the fly using Flink-ML
- Representing Graph data using Gelly
- 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
- 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
Self-Paced Learning Outline
Flink's Stream Processing Architecture
Getting Started with Flink
Transformations using the DataStream API
State and Checkpointing
Operations on Multiple Streams
Transformations in the DataSet API
Graph representation with Gelly
Machine Learning with Flink ML
|Learning Style||Self-Paced Learning|
|Course Duration||3 Hours|
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