ACID Compliance is Mandatory: If you are handling financial transactions or sensitive billing data where a partial update could cause a catastrophe, the Atomic, Consistent, Isolated, and Durable (ACID) properties of SQL are essential.
Structured and Stable Data: Use RDBMS when your data model is well-defined and unlikely to change frequently. You benefit from a fixed schema that enforces data quality.
When you need to ensure that a 'Post' cannot exist without an 'Author' (Foreign Key constraints), SQL handles this at the database level.
Complex Joins and Relationships: If your application requires complex queries that pull data from many different entities (e.g., Find all users in India who bought a specific product and have an active subscription), SQL’s JOIN capabilities are highly optimized for this.
Dynamic or Unstructured Data: If you are dealing with data that doesn't have a consistent shape—like diverse product catalogs, social media feeds, or IoT sensor logs—the schema-less nature of NoSQL (like MongoDB) allows you to store JSON-like documents without migrations.
Massive Scaling (Horizontal): RDBMS typically scales up (adding more RAM/CPU to one server), which hits a ceiling. NoSQL is designed to scale out by partitioning data across many cheap servers (sharding).
High Write Throughput: If your application needs to log thousands of events per second (e.g., real-time tracking, gaming leaderboards, or clickstream data), NoSQL databases like Cassandra or DynamoDB are built for these heavy write loads.
Rapid Prototyping: In the early stages of a project where the requirements are changing daily, not having to run a schema migration every time you add a feature can significantly speed up development.
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