A broad category of databases that store data in formats other than traditional relational tables, such as documents, key-value pairs, or graphs.
Relational databases organize data into tables with fixed columns and use SQL for queries. NoSQL databases emerged to handle needs such as flexible schemas, very large scale, and high write speeds that rigid tables made difficult.
Main types include document databases like MongoDB, which store JSON-like records; key-value stores like Redis, used for caching and sessions; wide-column stores like Apache Cassandra, designed for huge distributed datasets; and graph databases like Neo4j, ideal for relationships such as social networks. The name is often read as not only SQL, since many NoSQL systems now support SQL-like queries. Choosing between relational and NoSQL depends on data shape, consistency needs, and query patterns.
JavaScript Object Notation: a lightweight, text-based format for storing and exchanging structured data.
Knowledge GraphA structured network of entities, such as people, places, and concepts, and the relationships between them.
Relational DatabaseA database that stores data in tables of rows and columns and links related tables through shared keys, usually queried with SQL.
SQLStructured Query Language: the standard language for storing, querying, and managing data in relational databases.
Vector DatabaseA database designed to store embeddings and quickly find the items most similar to a given query vector.
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SQL vs NoSQL explained by data shape and access pattern, with concrete examples for relational, document, key-value, wide-column and graph databases.
A neutral, use-case-by-use-case comparison of AWS, Azure and Google Cloud, including how their pricing and discount models really work.