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Structured querying and transactions, ideal for banking systems and CRM applications.

Scalability and flexibility, suitable for big data analytics and real-time web apps.

High throughput and low-latency, perfect for real-time analytics and caching.

Relationship analysis, ideal for social networks and recommendation engines.

Flexible data models, suitable for content management systems and e-commerce platforms.

Highly available and write-efficient, good for Internet of Things (IoT) and time-series data.

Simple, fast, and horizontally scalable, used for session storage and cache layers.

Efficient for chronological data, optimal for financial market data and sensor data.

Aligns with object-oriented programming models, great for complex data applications.

Hierarchical data representation, well-suited for configuration management and web services.

Offers high availability and disaster recovery, suitable for large-scale online services.

Optimized for analysis and reporting, used in business intelligence and data mining.

Real-time CRUD operations, ideal for order processing and employee management.

Versatile use cases, suitable for businesses that need document, graph, and key-value data.

Scalable and cost-effective, great for startups and businesses with fluctuating demands.

Intended for single-user applications, used in desktop applications and solitary research projects.

Easy to manage and maintain, suitable for small to medium-sized enterprises with a single location.

Structure similar to a tree, perfect for organizational structures and file systems.

Allowing complex relationships, good for telecommunications and academic research networks.