Authors - Rohan Dafare, Supriya Narad Abstract - The quick spread of big data and the rising need for instant analytics have shown the built-in limits of old-school relational database management systems (RDBMS). NoSQL ("Not SQL") databases give schema-less design, side-to-side growth, and adaptable data shaping making them a better fit for handling messy and semi-messy data on a big scale. This paper looks at the edge NoSQL has over SQL systems by checking out key traits like how flexible the data model is how well it works under high output how easy it is to grow sideways, and how well it fits with cloud-native setups. Using a careful review of NoSQL teaching and use, we boil down real-world findings and suggest ways to pick the right database tech based on what the app needs. Our talk ends with a plan to help pros and teachers get when and why to use NoSQL fixes instead of, or along with classic SQL databases. Modern data intensive workloads driven by real time analytics, large scale user interactions, IoT streams, and unstructured content. It demands storage system capable of delivering high throughput, scalability and flexible data models. Traditional SQL databases continue to offer strong consistency, ACID guarantees and structured schema support, making them ideal for transactional applications and environments requiring strict data integrating. However, as data volume, variety and velocity increase, NOSQL databases have emerged as powerful alternative, providing horizontal scalability, schema-less design and optimized performance for distributed and semi-structured data processing.
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