BL!NK GLOBAL ENTERTAINMENT ANALYTICS ENGINE
A polyglot engineering capstone combining Python/Django (with PostgreSQL) and Node.js/Express (with MongoDB) — utilizing automated web scrapers to harvest Netflix Tudum telemetry and track real-time Global Top 10 movies and series across 5 international countries.
PROBLEM :
Streaming viewership trends are typically locked behind proprietary paywalls, leaving media analysts and film students without accessible real-time data on how content performs across different global markets.
Building a platform to track these trends required integrating relational analytical data with flexible user preference stores across disparate database engines.
Traditional single-stack backends struggled to cleanly balance Python's computational and web-scraping libraries with Node's asynchronous real-time concurrency and fast authentication handling.
SOLUTION :
Architected a polyglot microservice system combining Python/Django (with PostgreSQL) and Node.js/Express (with MongoDB Atlas), connected to a dynamic React frontend.
Built automated web scrapers in Python that routinely harvest Top 10 rankings and metadata from Netflix Tudum, parsing and storing international performance data across 5 major global countries into PostgreSQL.
Developed the Node.js/Express microservice to manage user authentication, personalized tracking alerts, and preference stores in MongoDB, while the React UI renders comparative country-by-country viewership charts and ranking progressions.
CHALLENGE :
Harmonizing data pipelines between relational PostgreSQL analytics and document MongoDB user stores while engineering anti-detection logic to keep scrapers running reliably without IP rate bans.
SUMMARY :
Bl!nk successfully demonstrated advanced polyglot systems integration, bridging Python, Django, PostgreSQL, Node.js, Express, MongoDB, and React into a cohesive media intelligence platform.
Provided an automated window into international streaming trends that operates autonomously with scheduled scraping pipelines.
