Spatial/ Geospatial Databases

[roseline371274]

Concept: Designed to store, query, and manipulate data representing objects in a geometric space, often (but not exclusively) related to Earth's surface. They support spatial data types (points, lines, polygons) and spatial indexing (e.g., R-trees).
Specialty: Performing geometric calculations, topological relationships (e.g., intersection, containment), proximity searches, and managing different coordinate reference systems.
Use Cases: Geographic Information Systems (GIS), location-based services (LBS), ride-sharing apps, urban planning, logistics, environmental modeling.
Examples: PostGIS (for PostgreSQL), Oracle Spatial and Graph, Microsoft special database SQL Server (Spatial), SpatiaLite (for SQLite), MongoDB (with GeoJSON support).
Vector Databases:

Concept: Store, index, and query high-dimensional vectors (embeddings) that represent the semantic meaning of unstructured data like text, images, or audio.
Specialty: Performing fast "similarity searches" (nearest neighbor searches) based on mathematical distance between vectors, enabling semantic understanding.
Use Cases: Semantic search, recommendation systems, AI-powered chatbots (for Retrieval Augmented Generation - RAG), anomaly detection, facial recognition.
Examples: Pinecone, Weaviate, Milvus, Chroma, Qdrant.
Ledger Databases:

Concept: Provide a central, immutable, and cryptographically verifiable record of all data changes. Every modification is recorded as a new, unalterable entry in a sequential log.
Specialty: Ensuring data integrity, providing provable audit trails, and preventing tampering. They offer transparency and trust by design.
Use Cases: Supply chain tracking, financial transaction auditing, healthcare records, compliance logging, intellectual property management.