Building a Scalable Kafka Image Processing Pipeline | Kafka and Docker Compose | Scalable GCP & AWS

In this comprehensive tutorial, we delve into constructing a robust image processing pipeline leveraging Apache Kafka for real-time data streaming and deploying it using Docker Compose for streamlined container orchestration. I’ll guide you through containerizing the application, setting up Kafka clusters, and orchestrating services using Docker Compose. By the end of this session, we’ll have a fully functional, scalable image processing pipeline with GCP/AWS which is ready for production workloads. Key Takeaways: - Understanding the architecture of an image processing pipeline. - Setting up and configuring Apache Kafka for message brokering. - Containerizing applications using Docker. - Deploying and managing services with Docker Compose. - Implementing asynchronous processing for scalability. - Setting cup CI/CD with GitHub action Prerequisites: - Basic knowledge of Python programming. - Familiarity with Docker and containerization concepts. - Understanding of Apache Kafka fundamentals(Don’t worry if no I have covered this as well) - Docker and Docker Compose installed on system. ⭐️ Timeline ⭐️ 00:00 : Introduction and objectives overview 02:21 : Overview of the system architecture and components 05:24: Setting up the development environment and prerequisites 06:36: Configuring Apache Kafka for message brokering, asynchronous processing, task queuing, monitoring and logging 20:43 : Building and containerizing the image processing application with Docker 32:44 : Deploying the Dockerized application and Kafka using Docker Compose 35:40: Running , monitoring and testing the pipeline and 41:31 : Best practices for scalable design in AWS, GCP environment 45:10 : Inspecting processed image and Conclusion 📥 Resources: 📌 Article: https://medium.com/@abonia/building-a-scalable-image-processing-pipeline-with-kafka-and-docker-compose-c99a1a91e021 📌 Github: https://github.com/Abonia1/Kafka-Image-Processing-Pipeline 📌 Docker Download: https://docs.docker.com/desktop