Databases
Relational, NoSQL, and time-series databases
Relational Databases
Structured databases that define relationships between tables with ACID properties. Using SQL language for data manipulation and providing transaction processing and integrity constraints.
Document Databases
NoSQL databases that store documents in JSON or BSON format. Supporting flexible data structures through schema-less design with easy horizontal scaling.
Key-Value Databases
Simple databases that store data as key-value pairs. Designed specifically for high-speed access, memory-based processing, and caching functionality.
Graph Databases
Databases that represent relationships between data using nodes and edges. Optimal for complex relationship analysis, recommendation systems, and social network analysis.
Columnar Databases
Databases that store data by columns, optimized for analytical queries. Delivering high performance in large data aggregation, OLAP processing, and data warehousing.
Time-Series Databases
Databases specialized for storing and processing time-stamped data. Providing high compression rates and search performance for IoT sensor data, metrics monitoring, and log analysis.
Search Engine Databases
Databases optimized for full-text search, faceted search, and complex search queries. Used in website search, log analysis, and text analytics.
In-Memory Databases
Databases that store data in memory for ultra-fast access. Playing crucial roles in real-time processing, caching, and session management.
Vector Search Databases
Databases specialized for storing and searching high-dimensional vector data. Playing central role in AI applications like LLMs, RAG, image search, and recommendation systems.