How Appinventiv Transformed Sonny's
Enterprises’ Data Infrastructure, Reducing
Report Load Times from Hours to Seconds

The cost of Alfred-like car wash monitoring app development typically ranges between $50,000 to $400,000. This range depends on the complexity of the dashboard features, the number of integrated sensing modules, the extent of customization, and the robustness of API integration. For a detailed quote tailored to your specific requirements, reach out to our team of experts.

Also Read: A Complete Guide on Data Science and Analytics for Businesses

The development timeline to build an Alfred-like car wash app varies based on the scope and technical intricacies. On average, a standard rollout takes 6–8 months; feature-rich builds can run 12–18 months. This estimate includes discovery, design, development, QA, and deployment. Our compressed-schema strategy and Python ETL pipelines accelerate delivery while securing quality. For a custom timeline, connect with our specialists.

Our data engineering team performed a meticulous analysis to identify data redundancies, and relationships, and define a new, optimized schema. We used a phased migration approach with rigorous validation checks at each step to ensure that every piece of critical data was preserved and correctly mapped, guaranteeing 100% data integrity post-transformation.

We treat legacy systems as a core part of the discovery process. Our approach involves creating an "anti-corruption layer"—a middleware that interfaces with your legacy APIs, translating and cleaning the data before it enters the new system. This isolates the new application from legacy complexities and allows for modern development without being hindered by old infrastructure.

While the React frontend was highly performant, the most critical factor was the backend schema optimization combined with optimized SQL queries. By reducing table joins and indexing the database intelligently, we drastically cut down the time the server needed to fetch and process data, which was the primary bottleneck. The use of Azure Service Bus for async operations further reduced the load on the main application thread.

The solution was built for scalability from day one. The optimized database schema is inherently more scalable than the previous version. The use of a modern stack (Node.js, React) and a cloud-based queueing system (Azure Service Bus) allows the architecture to handle increased loads by scaling resources horizontally, ensuring consistent performance as Sonny's adds thousands of new operators to the platform.