NoSQL Databases
Document-Oriented Databases
Self-contained with flexible schema
Very flexible schema with complex querying JSON/BSON or XML data formats Leverage complex indexes (multi-key, geospatial, full text search etc) High performance and balanced R:W ratio MongoDB, Couchbase, Elasticsearch, CouchDB, Firebase, Firestore
Column-Oriented
Extreme write speeds of high volume of data
High volume of data Extreme write speeds with relatively less velocity reads Data extractions by columns using row keys No ad-hoc query patterns, or complex indexes HBase, Cassandra, ScyllaDB
Labeled-Property Graph
Reveal insights from data
Perfect for interconnected data Ability to store properties for each data point as well as for relationship between them Complex queries to determine relationships between data points Neo4j, TinkerPop Gremlin, Neptune, DSE Graph, TigerGraph
Semantic Knowledge Graph
Deep semantics for conceptual meaning
Structured and formal representation of knowledge Semantic descriptions of entities and their relationships Stable URI identifiers in the open data world AllegroGraph, OntoText GraphDB, Stardog
Key-Value Stores
Fastest possible method of writing and reading data from/to memory or disk
Simple schema High velocity read/write with no frequent updates High performance and scalability No complex queries involving multiple keys or joins Aerospike, DynamoDB, Redis, Riak
Multi-Model Databases
Store, index and query data in more than a single model
While most database management systems are organized around a single data model that determines how data can be organized, stored, and manipulated, a multi-model database is designed to support multiple data models against a single, integrated backend.
You may combine document, graph, key-value stores, maybe relational as well. Multi-model databases are intended to offer the data modeling advantages of polyglot persistence, without its disadvantages. Operational complexity, in particular, is reduced through the use of a single data store.