A Comparison of Developer Performance Across Raw SQL, ORM-Inspired Interfaces, and a Cosmos-Specific ODM
Sanjyoti Kumar Tarai1, Rishabh Sharma2*, Priyanshu Sharma2, Ayush Godiyal2, Lakshay Kumar Signh2
1. Assistant Professor,
Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India
sanju1463@gmail.com
2. Department of Information technology,
Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India
*rishabhsharma17aug2004@gmail.com
Abstract
Anyone who's worked with Azure Cosmos DB knows the frustration. The platform has its own SQL dialect that doesn't quite match what you're used to. You need to constantly think about partitions when writing queries. And there's no schema enforcement which sounds great until you realize how easy it is to accidentally break things. sure, we have tools like Prisma and Mongoose that make database work easier. They're genuinely great at what they do. But here's the problem: they were designed for completely different databases. Prisma targets relational databases, Mongoose was built for MongoDB. Neither one really gets what makes Cosmos DB different, so developers are left figuring things out on their own. We wanted to see if we could do better. This paper looks at four different ways to work with Cosmos DB: writing raw SQL queries, using Prisma-style interfaces, following Mongoose's patterns, and using a new ODM we built specifically for Cosmos. Our version has Zod validation for catching schema issues, typed field references so you don't misspell things, automatic parameterization to prevent injection attacks, and a visual Explorer that actually understands Cosmos DB's structure. We got several developers together and had them complete the same tasks using each method. We measured everything how accurate they were, how long tasks took, how much mental energy they burned, and how many errors they made. The difference was pretty clear. With the Cosmos-specific ODM, developers made far fewer mistakes. No more typos in field names, no more schema mismatches, way fewer syntax errors. Tasks got done faster, and the queries people wrote were more reliable and predictable. What this really tells us is that one-size-fits-all tools don't work for specialized platforms. Cosmos DB has its own personality, its own quirks. It needs tools that understand those quirks. When developers have the right tools for the job, everything gets easier they write cleaner code, make fewer mistakes, and ship more reliable features.
