Overview

Swiftecs is a backend-heavy logistics intelligence system designed to centralize and standardize order data from a single external platform.

The goal was to eliminate manual tracking workflows by building a fully automated pipeline that continuously extracts, processes, and exposes structured order data through a unified API.

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Problem

In logistics operations, order data is managed inside a single external platform with limited export capabilities and inconsistent internal structures.

This creates:

• Manual data extraction overhead
• Delayed visibility on order status
• High operational friction between teams
• Inconsistent and unstructured data handling

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Solution

I designed and implemented an automated data pipeline that:

• Continuously extracts order data from a single external system
• Normalizes and structures raw data into a unified schema
• Stores processed data in a centralized database
• Exposes it through a scalable REST API for downstream usage

The system runs fully in the background with scheduled synchronization.

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Architecture

The system is built around an asynchronous processing pipeline:

• Django + Django REST Framework for API layer
• Celery for distributed background tasks
• Redis as a message broker and task queue
• PostgreSQL as the primary data store
• Selenium for browser-based data extraction
• Docker for environment consistency and deployment

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Data Pipeline

1. Automated authentication into the external system
2. Extraction of raw order data using browser automation
3. Transformation and normalization into a structured schema
4. Persistence into PostgreSQL
5. Exposure through RESTful API endpoints
6. Periodic synchronization to ensure data freshness

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Challenges

1. Unstable external interface

The external system was not designed for programmatic access, requiring resilient automation strategies and retry handling.

2. Data inconsistency

Order data lacked a stable structure, requiring a strong normalization layer to enforce consistency.

3. Long-running task reliability

Some extraction processes required fault-tolerant background execution with retries and failure recovery.

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Key Engineering Decisions

• Isolated ingestion logic using Celery workers
• Used Redis for reliable task queuing and retries
• Built a normalized internal schema independent of external structure
• Containerized all services for reproducible deployment environments
• Separated API layer from data ingestion pipeline for scalability

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Impact

• Fully automated order synchronization pipeline
• Eliminated manual data extraction workflows
• Improved data consistency and reliability
• Enabled near real-time visibility into logistics operations

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Tech Stack

• Django / DRF
• Celery
• Redis
• PostgreSQL
• Selenium
• Docker
• Python

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What I Learned

This project strengthened my experience in:

• building resilient backend systems
• designing data ingestion pipelines
• handling unreliable external systems
• structuring scalable Django architectures
• working with asynchronous distributed processing