1. Relational Databases (e.g., MySQL, Oracle, SQL Server):

   • Uses structured tables to store data.
   • Offers data integrity and complex querying capabilities.
   • Known for ACID compliance, ensuring reliable transactions.
   • Includes features like foreign keys and security control, making them ideal for applications needing consistent data relationships.

2. Document Databases (e.g., CouchDB, MongoDB):

   • Stores data as JSON documents, providing flexible schemas that can adapt to varying structures.
   • Popular for semi-structured or unstructured data.
   • Commonly used in content management and automated sharding for scalability.

3. In-Memory Databases (e.g., Apache Geode, Hazelcast):

   • Focuses on real-time data processing with low-latency and high-speed transactions.
   • Frequently used in scenarios like gaming applications and high-frequency trading where speed is critical.

4. Graph Databases (e.g., Neo4j, OrientDB):

• Best for handling complex relationships and networks, such as social networks or knowledge graphs.
• Features like pattern recognition and traversal make them suitable for analyzing connected data structures.

1. Time-Series Databases (e.g., Timescale, InfluxDB):

• Optimized for temporal data, IoT data, and fast retrieval.
• Ideal for applications requiring data compression and trend analysis over time, such as monitoring logs.

1. Spatial Databases (e.g., PostGIS, Oracle, Amazon Aurora):
   • Specializes in geographic data and location-based queries.
   • Commonly used for applications involving maps, GIS, and geospatial data analysis, including earth sciences.

Different types of databases are optimized for specific tasks. Relational databases excel in structured data management, while document, graph, in-memory, time-series, and spatial databases each have distinct strengths suited for modern data-driven applications.

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