https://github.com/jaehyeon-kim/flink-demos

Apache Flink (Pyflink) and Related Projects
https://github.com/jaehyeon-kim/flink-demos

aws data-streaming docker docker-compose flink kafka kinesis-data-analytics pyflink python real-time-analytics

Last synced: about 2 months ago
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Apache Flink (Pyflink) and Related Projects

• Host: GitHub
• URL: https://github.com/jaehyeon-kim/flink-demos
• Owner: jaehyeon-kim
• Created: 2023-02-16T21:14:11.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2024-06-10T23:00:02.000Z (7 months ago)
• Last Synced: 2024-06-11T01:42:29.328Z (7 months ago)
• Topics: aws, data-streaming, docker, docker-compose, flink, kafka, kinesis-data-analytics, pyflink, python, real-time-analytics
• Language: Python
• Homepage: https://jaehyeon.me/tags/apache-flink/
• Size: 2.18 MB
• Stars: 16
• Watchers: 3
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Flink Demos

- [Getting Started With Pyflink on AWS](https://jaehyeon.me/blog/2023-08-17-getting-started-with-pyflink-on-aws-part-1/)

  - Apache Flink is widely used for building real-time stream processing applications. On AWS, Amazon Managed Service for Apache Flink is the easiest option to develop a Flink app as it provides the underlying infrastructure. Updating a guide from AWS, this series of posts discuss how to develop and deploy a Flink (Pyflink) application on AWS where the data source and sink are Kafka topics.

- [Kafka, Flink and DynamoDB for Real Time Fraud Detection](https://jaehyeon.me/blog/2023-08-10-fraud-detection-part-1/)

  - Re-implementing a solution from an AWS workshop, this series of posts discuss how to develop and deploy a fraud detection app using Kafka, Flink and DynamoDB. Part 1 covers local development using Docker while deployment on AWS will be discussed in part 2.

- [Building Apache Flink Applications in Python](https://jaehyeon.me/blog/2023-10-19-build-pyflink-apps/)

  - Building Apache Flink Applications in Java by Confluent is a course to introduce Apache Flink through a series of hands-on exercises. Utilising the Flink DataStream API, the course develops three Flink applications from ingesting source data into calculating usage statistics. As part of learning the Flink DataStream API in Pyflink, I converted the Java apps into Python equivalent while performing the course exercises in Pyflink. This post summarises the progress of the conversion and shows the final output.