Case study

Python Backend Services & Data Extraction

High-performance Python services across client projects — REST APIs in Django and FastAPI, data on MongoDB and PostgreSQL, and scraping pipelines feeding analytics.

Problem

Client products needed fast, reliable backends and a steady supply of structured data — from APIs serving distributed systems to unstructured web sources that had to become analyzable datasets.

Approach

• High-performance REST APIs in Django REST Framework, with data serialization and request-response cycles tuned for rapid transmission across distributed systems.
• Additional client services built on FastAPI with MongoDB, alongside PostgreSQL-backed Django systems.
• Robust web scrapers and data-extraction pipelines with Selenium and BeautifulSoup, automating the extraction, preprocessing, and structuring of multi-source unstructured data.
• Automated backend testing workflows — reusable test suites and integration scripts replacing manual verification.

Outcome

• Faster, more responsive data transmission across distributed systems.
• Unstructured web sources turned into analytics-ready datasets automatically.
• Manual verification eliminated from backend delivery through reusable test automation.

Stack