This system was deployed and actively used by Mira Mahila Nagari Pathsanstha for over 6 months, supporting daily loan operations, staff management, and financial tracking.
During the development of this project, one of the major challenges was working with a limited backend stack, as my experience at that time was primarily with Flutter, React, and Firebase, without exposure to relational databases like PostgreSQL. As the system started handling real-world usage, Firebase costs began increasing significantly due to high read and write operations. Additionally, managing and structuring large volumes of loan and transaction data efficiently, while maintaining performance for real-time dashboards and analytics, became a key challenge. Designing a scalable architecture within Firebase’s constraints required careful planning and optimization.
To address these challenges, I restructured the entire database architecture to reduce unnecessary reads and writes, focusing on cost-efficient design. I implemented optimized data indexing and filtering strategies within Firebase, along with caching mechanisms to prevent repeated data fetching. Pagination and selective querying were introduced to handle large datasets more efficiently. I also redesigned the data flow and collection structure to ensure better scalability and performance, allowing the system to operate smoothly even with increasing usage.
As a result of these optimizations, Firebase operational costs were reduced by approximately 80–90%, making the system significantly more cost-efficient. The application’s performance improved noticeably, with faster load times and smoother data handling. The platform successfully supported real-world operations for over 6 months, handling financial transactions, loan tracking, and user activities reliably. This project demonstrated the ability to identify scalability issues and implement practical, production-level solutions under real constraints.
