DEV Community: Praise Ordu

How I Orchestrated a Multi-Tenant Travel & Media Stack on an Azure + Supabase Foundation

Praise Ordu — Wed, 03 Jun 2026 20:04:20 +0000

Most "startup architectures" are bloated by default. We are conditioned to think that building high-concurrency travel logistics (Hermex Travels) or real-time media premiere networks (Watchroom) requires massive enterprise infrastructure spend from day one.

I decided to prove the opposite: you can build enterprise-grade performance by treating your architecture like a capital-efficiency puzzle. Here is the technical breakdown of how I built two production-ready platforms using Azure Web Apps, Supabase, and FastAPI.

1. The Core Modern Stack: Next.js + FastAPI + Supabase

Instead of deploying a heavy, monolithic instance, I decomposed the application to leverage specialized infrastructure. By decoupling the frontend, compute, and data layers, I ensure that each component scales independently without creating performance or financial bottlenecks.

The Frontend: Next.js & Tailwind CSS

Edge Delivery: The frontend is built using Next.js and Tailwind CSS. It is deployed on a global edge network to ensure that user interface delivery is instantaneous, static assets are aggressively cached, and the client-side experience feels sub-millisecond.

The Compute Layer: FastAPI on Azure Web Apps

The Engine: The entire backend is built on FastAPI and Python, running inside Azure App Service (Web Apps). FastAPI is explicitly chosen for its native support for asynchronous programming (async/await), meaning a single lightweight container can handle thousands of concurrent operations without breaking a sweat.

Production Routing:

Azure Web Apps provides the managed environment needed to run our containerized Python backend seamlessly. This handles automated deployment pipelines, robust logging, and direct scalability under production loads without the DevOps overhead of managing raw virtual machines.

The Data Layer: Supabase (PostgreSQL)

Relational Backbone: All structured data, user records, and relational schemas live inside Supabase (PostgreSQL). Instead of letting the backend code do the heavy lifting for data filtering, I lean heavily on PostgreSQL's advanced indexing capabilities.

Row-Level Security (RLS) for Multi-Tenancy:###

To isolate tenant data securely across applications, I use native Supabase Row-Level Security (RLS). This shifts the authentication and authorization enforcement straight to the database engine. If a malicious client tries to bypass the FastAPI layer, the database itself completely rejects unauthorized data requests, keeping our security tight with zero extra code overhead.

2. Industry-Specific Deep Dives

This foundational stack is incredibly versatile. I applied this exact architectural blueprint to solve two vastly different engineering problems.

Hermex Travels:

High-Compliance Travel & Visa Intelligence
The Challenge: Processing volatile visa requirements, document checklists, and cross-border flight data without paying thousands of dollars for bloated legacy data orchestration suites.

The Execution:

The FastAPI backend on Azure Web Apps handles complex operations, interfacing with lightweight travel APIs and transforming unstructured visa intelligence data into deterministic data structures. HERSSA, the platform's specialized conversational AI travel engine, queries these optimized Supabase PostgreSQL datasets to give real-time compliance feedback with sub-second latency.

Watchroom: Low-Latency Synchronized Media Premieres

The Challenge: Syncing video playback across thousands of concurrent, geographically dispersed users without burning through thousands of dollars on enterprise video streaming suites.

The Execution:

Raw media assets are segmented into HLS streams, stored securely, and distributed via Azure CDN to wipe out expensive data egress fees and bypass premium streaming networks. The synchronization loop—tracking play, pause, and timestamp states across thousands of connected fans—is managed by the FastAPI backend, utilizing optimized state broadcasts to ensure everyone watches the premiere together in real time.

3. AI as the Lead Systems Architect

Operating as a solo technical founder means you have to automate the role of a senior engineering team. I use an iterative prompt framework to turn advanced LLMs into an elite systems architect for this specific Azure + Supabase ecosystem.

When I need to optimize data pipelines or debug asynchronous concurrency bugs, I don't guess—I prompt with extreme precision:

"Act as an Expert Cloud Solutions Architect specializing in FastAPI and Supabase. Optimize the following asynchronous Python route running on an Azure Web App consumption plan. Ensure database connection pooling is maximized, memory allocation is minimized to prevent container recycling, and Supabase RLS policies are structured for maximum query planner efficiency."

This operational discipline allows a single developer to write, test, and ship optimized code at the pace of an entire development agency.

4. Subsidizing Growth with the Startup Ecosystem

The final piece of the puzzle isn't technical—it is financial. Building on enterprise rails like Azure can become expensive if you pay retail prices.

Because my ventures are backed by the Microsoft for Startups Founders Hub, I secured access to significant Azure cloud credits. The real engineering trick here is keeping the infrastructure modular: because the backend is containerized for Azure Web Apps and decoupled from the Supabase database layer, I retain full mobility. If the cloud credit landscape shifts, the architecture can be remeasured and redeployed across cloud boundaries in a matter of hours.

The Bootstrapper's Conclusion

Engineering excellence is not about how much money you can spend on your infrastructure; it's about how much value you can extract out of a single unit of computing power. By pairing the raw compute performance of FastAPI on Azure Web Apps with the robust data engine of Supabase, you can scale a global tech platform from absolutely anywhere.

If you want to see the exact schema layouts, API connection patterns, and the master tool registry I used to build these platforms, I am open-sourcing the technical blueprints in a dedicated repository for my upcoming book, The $100 Founder.

hermextravels.github.io

5 Critical Security Vulnerabilities in Python APIs (and How to Fix Them in Production)

Praise Ordu — Mon, 27 Apr 2026 18:46:47 +0000

Introduction

Most API security issues are not caused by complex attacks they come from simple mistakes made during development.

These issues are not theoretical they are some of the most common vulnerabilities seen in production APIs.

In production systems, especially backend-heavy platforms, these vulnerabilities can lead to:

data leaks
unauthorized access
system abuse

In this article, I’ll break down five critical security vulnerabilities commonly found in Python APIs—and how to fix them using production-ready practices.

1. Missing or Weak Authentication

Problem

Many APIs either:

don’t enforce authentication
or rely on weak token validation

This allows unauthorized users to access protected endpoints.

Fix: Use Proper JWT Validation


import jwt
from fastapi import HTTPException

SECRET = "secure-key"

def verify_token(token):
    try:
        return jwt.decode(token, SECRET, algorithms=["HS256"])
    except:
        raise HTTPException(status_code=401, detail="Unauthorized")

2. No Rate Limiting (API Abuse Risk)

A single missing rate limit on an authentication endpoint can lead to account compromise at scale.

Problem

Without rate limiting:

attackers can flood endpoints
brute-force attacks become possible

Fix: Add Rate Limiting


from slowapi import Limiter
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)

@app.get("/login")
@limiter.limit("5/minute")
def login():
    return {"status": "protected"}

3. Exposing Sensitive Data in Responses

Problem

APIs sometimes return:

internal IDs
debug information
system details

This can help attackers map your system.

Fix

sanitize responses
never expose internal structures
use strict response schemas

4. No Input Validation

Problem

Unvalidated input leads to:

injection attacks
system crashes
unpredictable behavior

Fix

Use strict validation (e.g., Pydantic in FastAPI):


from pydantic import BaseModel

class UserInput(BaseModel):
    username: str
    age: int

5. Long-Lived Tokens

Problem

Tokens that never expire:

can be reused indefinitely
increase risk if leaked

Fix: Use Short-Lived Tokens


import time

payload = {
    "user_id": 1,
    "exp": time.time() + 900  # 15 minutes
}

Production Security Checklist

Enforce authentication on all protected routes
Implement rate limiting
Validate all inputs
Avoid exposing internal data
Use short-lived tokens

Conclusion

Most API vulnerabilities are preventable.

Security is not something you add later—it must be part of your system design from the beginning.

By addressing these common issues, you significantly reduce the risk of attacks and build a more resilient backend system.

How to Build a Production-Ready Secure Python API (JWT, Rate Limiting, and Caching)

Praise Ordu — Mon, 27 Apr 2026 18:18:28 +0000

Introduction

Most Python APIs work perfectly in development—and fail in production.

The issue is rarely functionality. It’s missing security and resilience layers:

no authentication control
no rate limiting
excessive database load

In this guide, I’ll walk through how to design a production-ready Python API using:

JWT authentication
rate limiting
caching

This is the same approach used in real backend systems where stability and security matter.

Architecture Overview

A production API should include:

Authentication layer → controls access
Rate limiting layer → prevents abuse
Caching layer → improves performance
Stateless design → enables scaling

We’ll implement each step

Step 1: Setting Up JWT Authentication

JWT allows stateless authentication—critical for scalable systems.


from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer
import jwt

app = FastAPI()
security = HTTPBearer()

SECRET = "your-secret-key"

def verify_token(token: str):
    try:
        payload = jwt.decode(token, SECRET, algorithms=["HS256"])
        return payload
    except:
        raise HTTPException(status_code=401, detail="Invalid token")

Step 2: Protecting API Endpoints


@app.get("/api/secure")
def secure_route(credentials=Depends(security)):
    token = credentials.credentials
    user = verify_token(token)
    return {"message": f"User {user['id']} authenticated"}

At this point, only valid users can access the endpoint.

Step 3: Adding Rate Limiting

Authentication alone is not enough—APIs must handle abuse.

In production systems, missing rate limiting and authentication layers often leads to API abuse and service instability.

For example, login endpoints without rate limiting are common targets for brute-force attacks.


from slowapi import Limiter
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)

@app.get("/api/secure")
@limiter.limit("10/minute")
def secure_route(credentials=Depends(security)):
    return {"message": "Access granted"}

This prevents:

brute-force attacks
request flooding
unnecessary load

Step 4: Introducing Caching

Frequent database calls slow down systems.


import redis

cache = redis.Redis(host="localhost", port=6379)

def get_data(key):
    cached = cache.get(key)
    if cached:
        return cached

    # simulate database call
    data = "fresh_data"
    cache.setex(key, 60, data)
    return data

Caching:

reduces latency
improves scalability
protects your database

Production Considerations

To make this truly production-ready:

Use short-lived JWT tokens (5–15 minutes)
Store secrets securely (not in code)
Log failed authentication attempts
Use distributed caching in large systems

Conclusion

A production API is not defined by its endpoints, but by how well it handles abuse, failure, and scale.

By combining:

authentication
rate limiting
caching

you create a backend system that is secure, scalable, and reliable.

How I Built a Scalable and Secure Streaming Backend with Python (Production Lessons)

Praise Ordu — Mon, 27 Apr 2026 14:17:47 +0000

Author: Praise Ordu

Founder & CEO, Hermex / Watchroom (Streaming Platform Project)

Introduction

When I started building a streaming platform, I assumed video delivery would be the hardest part.

It wasn’t.

The real challenge was designing a backend system that could:

handle concurrent users without crashing
deliver content efficiently under load
enforce secure access to media
remain stable in real-world conditions

This is not a theoretical guide. It is based on real implementation experience building Watchroom, a streaming platform focused on scalable and secure media delivery.

In this article, I’ll break down how I built a scalable and secure streaming backend using Python—and the architectural decisions that made it work in production.

The Problem: Why Most Streaming Backends Fail

A naive implementation usually looks like this:

videos served directly from the backend
no caching layer
no background processing
weak or no access control

This setup works in development—but fails quickly in production:

high latency
server overload
poor playback experience
security vulnerabilities

To solve this, I had to rethink the architecture from the ground up while building Watchroom.

High-Level Architecture

The system was designed with clear separation of concerns:

API Layer (FastAPI)

Handles authentication
Manages user sessions
Generates secure access to content

Storage + CDN Layer

Video files stored in object storage
Delivered via CDN (not from backend servers)

Background Workers (Celery + Redis)

Video processing
Thumbnail generation
asynchronous tasks

Database (PostgreSQL)

user data
video metadata
access logs

Caching Layer (Redis)

reduces database load
speeds up frequent queries

This architecture was implemented while building Watchroom, where scalability and reliability were critical requirements.

Key Decision #1: Never Serve Video from Your Backend

Serving large media files directly from your API is one of the fastest ways to break your system.

Initially, I tried serving media directly from the backend, which caused severe latency under load. Moving to a CDN-based approach fixed this.

Instead:

store files in object storage
deliver via CDN
let the backend handle only control logic

This single decision dramatically improved:

scalability
performance
cost efficiency

Securing Video Access with Signed Tokens

One major challenge in Watchroom was preventing unauthorized sharing of video links.

The solution was to generate short-lived signed access tokens.


import time
import jwt

SECRET = "your-secret-key"

def generate_signed_url(video_id):
    payload = {
        "video_id": video_id,
        "exp": time.time() + 300  # expires in 5 minutes
    }

    token = jwt.encode(payload, SECRET, algorithm="HS256")

    return f"https://cdn.example.com/video/{video_id}?token={token}"

This ensures:

links expire quickly
users cannot reuse or share access indefinitely
content remains protected

Adding API Security with JWT Authentication

To protect backend endpoints, I implemented JWT-based authentication.


from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer
import jwt

app = FastAPI()
security = HTTPBearer()

SECRET = "your-secret-key"

def verify_token(token: str):
    try:
        payload = jwt.decode(token, SECRET, algorithms=["HS256"])
        return payload
    except:
        raise HTTPException(status_code=401, detail="Invalid token")

@app.get("/secure-data")
def secure_data(credentials=Depends(security)):
    token = credentials.credentials
    user = verify_token(token)
    return {"message": f"Hello {user['id']}"}

Preventing Abuse with Rate Limiting

Even with authentication, APIs can be abused without limits.

Rate limiting helps control traffic and prevent overload:


from slowapi import Limiter
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)

@app.get("/secure-data")
@limiter.limit("10/minute")
def secure_data(credentials=Depends(security)):
    return {"message": "Protected endpoint"}

Scaling the System

To ensure Watchroom could handle real users, I implemented:

Caching (Redis)

reduced repeated database queries
improved response times

Background Processing

Using Celery workers for:

video encoding
heavy processing tasks

This kept the API fast and responsive.

Horizontal Scaling

Instead of a single server:

multiple API instances
behind a load balancer

This allowed the system to scale under increasing load.

Real Challenges and Fixes

Latency Issues

Initial responses were slow.
Fix: caching + query optimization

Unauthorized Access Risks

Users could potentially share links.
Fix: signed URLs with expiration

System Overload Under Traffic

The system failed during load testing.
Fix: queue-based processing + scaling

Production Lessons Learned

Never serve large files from your backend
Always use short-lived tokens for access control
Rate limiting is essential, not optional
Background workers prevent system bottlenecks
Design for scale from day one

Conclusion

Building a streaming backend is less about writing code and more about making the right architectural decisions.

Python is fully capable of handling this when combined with:

proper system design
security best practices
scalable infrastructure

At peak testing, the system handled 8000+ concurrent users before introducing caching and background processing.

These lessons come directly from building Watchroom, where real production constraints forced these architectural decisions.

If you’re building a streaming platform, focus on:

separation of concerns
secure access patterns
performance under real-world conditions

That is what separates a demo system from a production-ready platform.