Published February 27, 2026 | Version v1
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A Comparative Study of SQL and NoSQL Databases for Cloud-Based Web Applications: Performance, Scalability, and Use Case Analysis

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Picking the right database for a cloud application sounds simple until you actually have to do it. SQL systems like MySQL have been the go-to for decades, and for good reason — but as applications started dealing with more varied data at larger scale, NoSQL systems like MongoDB began showing up in more and more production stacks. The question was never really which one is better. It’s which one actually fits your situation. This paper grew out of that frustration. We wanted something more concrete than the usual “it depends” answer, so we put both systems through real tests on the same dataset — 100,000 e-commerce records — and measured what actually happened: query speeds, read and write throughput, storage consumption, and how each system held up as data volume grew. MySQL was faster when queries got complicated, particularly joins and aggregations. MongoDB pulled ahead on write speed and handled document-style data more naturally, though it used noticeably more storage for the same records. We also cover the theory — ACID, CAP, BASE — not to pad the paper, but because those concepts genuinely explain why the benchmarks turned out the way they did. The goal is simple: give developers and architects a clear enough picture to make this call confidently for their own application.

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Submitted
2026-02-27
If you were building a web application ten years ago, the database question was mostly settled before you even asked it. You picked MySQL, or maybe PostgreSQL, and you moved on. Those systems had earned their reputation — they were reliable, well-understood, and backed by decades of tooling. The relational model fit most applications well enough that questioning it felt unnecessary. That's changed. Today's applications — e-commerce platforms with millions of concurrent users, social networks generating billions of events per day, healthcare systems that need real-time access across distributed infrastructure — put pressures on a database that the traditional relational model wasn't really designed to handle. Vertical scaling, the main lever SQL systems offer, works until it doesn't, and at that point the costs get serious. The NoSQL movement emerged largely because some of the biggest technology companies in the world hit exactly that wall. Google, Amazon, and Facebook couldn't just keep buying bigger servers. So they built different kinds of databases — systems that prioritized horizontal scalability and fault tolerance over strict consistency — and eventually published the research that let the rest of the industry follow. Document stores, key-value stores, column-family databases, and graph databases each took a different approach to the same underlying problem.

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