Cloud-Native AKS ETL Pipeline
Cloud-Native Kubernetes Data Processing System
Overview
A scalable cloud-native ETL pipeline deployed on Azure Kubernetes Service with automated CI/CD workflows and containerized infrastructure.
This project involved building a production-grade ETL pipeline capable of processing structured datasets within a scalable Kubernetes environment. The application was containerized using Docker and deployed on Azure Kubernetes Service (AKS) to ensure high availability and scalability. GitHub Actions powered the CI/CD workflow, automating testing, builds, and deployments while enforcing DevOps best practices. The system architecture emphasized reliability, scalability, and streamlined cloud deployment operations.
- Category
- Cloud & DevOps
- Year
- 2025
- Role
- Full Stack Developer
Technologies
Tech Stack
14 technologies across 4 layers
frontend
2- Dash
- Plotly
backend
3- Python
- FastAPI
- Microservices Architecture
database
2- PostgreSQL
- Star Schema Data Warehouse
tools
7- Docker
- Kubernetes
- yfinance
- GitHub Actions
- Scheduler Service
- Pandas
- NumPy
Key Features
Cloud-native ETL pipeline architecture
Dockerized Python data processing services
Kubernetes orchestration on Azure AKS
Automated CI/CD workflows using GitHub Actions
Scalable container deployment infrastructure
High availability with Kubernetes scaling policies
Automated testing and deployment pipelines
Infrastructure optimized for production workloads
Challenges & Solutions
01
Managing deployment consistency and scalability across multiple Kubernetes environments.
Implemented Kubernetes deployment manifests and automated CI/CD pipelines using GitHub Actions to standardize deployments and reduce manual configuration errors.
02
Ensuring application reliability and availability under variable processing workloads.
Configured Kubernetes auto-scaling policies and optimized container resource allocation to dynamically handle workload spikes efficiently.
Gallery
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