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E-Commerce DevOps Platform

Production e-commerce with full DevOps stack

In short: E-Commerce DevOps Platform (E-Commerce / DevOps) is a case study from Daniyal Alam’s portfolio: Production e-commerce with full DevOps stack. Core stack: Python, Django, DRF.

A full-featured Django e-commerce application engineered as a showcase of enterprise DevOps: CI/CD, containerisation, monitoring, security scanning and multi-platform deployment.

Industry
E-Commerce / DevOps
Scope
Flagship, multi-system platforms
Core stack
Python · Django · DRF · PostgreSQL

The problem

Most e-commerce builds skip the operational reality — containerisation, monitoring, security scanning and automated delivery that production demands.

The solution

A deployable store with multi-stage Docker builds, a GitHub Actions pipeline, Prometheus/Grafana monitoring, ELK logging and automated security scanning.

Technical challenges

The engineering problems this build had to get right.

  • Multi-container orchestration with health checks
  • Secure CI/CD with rollback
  • Image-size vs security optimisation

Architecture

Frontend
Django server-rendered templates
Backend
Django 4.2 + Django REST Framework
Database
PostgreSQL / SQLite
Infrastructure
Docker Compose, Nginx, Gunicorn, Prometheus, Grafana, ELK

Implementation — what shipped

Catalog, cart, checkout, orders, wishlists, reviews
Multi-stage Docker builds
GitHub Actions CI/CD (Black, Flake8, isort, MyPy)
Unit, integration & performance tests (Locust)
Prometheus metrics + Grafana dashboards
ELK structured logging
Redis caching
Security scanning (Bandit, Trivy, OWASP)

Engineering notes

Lesson for similar E-Commerce builds: design for multi-container orchestration with health checks and secure CI/CD with rollback up front — these are the parts that are expensive to retrofit once the feature list grows.

Technologies

  • Python
  • Django
  • DRF
  • PostgreSQL
  • Redis
  • Docker
  • GitHub Actions
  • Prometheus
  • Grafana
  • Nginx

Building something similar?

A 30-minute discovery call is enough to scope the approach and a realistic budget. Related at DanixSoft: Python Engineering, Cloud & DevOps.