What are the main use cases for document databases?

Document databases are primarily used for scenarios requiring flexible data models, handling semi-structured or hierarchical data, and scaling horizontally. They store data in formats like JSON or BSON, allowing each document to have a unique structure without a fixed schema. This makes them ideal for applications where data formats vary frequently or evolve over time.

One common use case is content management systems (CMS) and applications with dynamic data schemas. For example, a blog platform might store articles with varying metadata (tags, authors, categories) or nested comments. A document database allows each article to have its own structure, eliminating the need for complex joins or schema migrations. Similarly, e-commerce platforms use document databases to manage product catalogs where items have different attributes (e.g., a shirt with size/color options vs. a laptop with technical specs). This flexibility simplifies handling diverse product types in a single database.

Another key use case is real-time data processing and user profiles. Applications like social networks or gaming platforms often store user profiles with varying data points (preferences, activity logs, friend lists). Document databases enable developers to update profiles atomically and retrieve them in a single query, improving performance. For example, a fitness app might store workout history, goals, and device integrations in a single user document. This approach avoids scattering data across multiple tables, reducing latency for read-heavy workloads.

Document databases also excel in scenarios involving event logging, IoT data, or semi-structured analytics. IoT devices generate diverse telemetry data (temperature, location, battery levels) that can be stored as self-contained documents with timestamps. This simplifies querying time-series data or aggregating metrics. Similarly, event-driven architectures use document databases to store logs with varying payloads (e.g., user clicks, API errors, transaction records). Developers can query these logs without predefined schemas, making it easier to adapt to new event types as systems evolve.