From early non-relational models to modern distributed systems, trace the rise of NoSQL in response to big data and web-scale challenges.
NoSQL (Not Only SQL) databases emerged in the early 2000s to handle unstructured data, massive scale, and high-velocity applications where traditional RDBMS fell short. They prioritize flexibility, scalability, and performance over strict ACID compliance, using models like key-value, document, column-family, and graph. Below is a detailed overview of milestones, key figures, and developments up to 2025.
Term coined in 1998 by Carlo Strozzi; popularized in 2009 amid Web 2.0 scalability needs.
Hybrid SQL/NoSQL systems and AI-driven multi-model databases in 2025.
Hierarchical (IMS) and network (CODASYL) databases handle enterprise data; term "NoSQL" coined by Carlo Strozzi in 1998.
Distributed storage for structured data, inspiring HBase and Cassandra.
Document-oriented database with JSON storage and MapReduce views.
Open-source BigTable implementation; scalable object storage influences NoSQL.
Key-value store for high availability, leading to Riak and Voldemort.
Wide-column store from Facebook; first graph database for relationships.
Document store with JSON-like queries; in-memory key-value for caching.
Blurring lines with SQL; rise of multi-model DBs and AI integrations.
As of October 10, 2025, NoSQL powers big data, real-time analytics, and microservices, with growing hybrid systems combining NoSQL flexibility and SQL ACID guarantees. Trends include edge computing and AI-optimized queries.
Dominant in web-scale apps; types include key-value (Redis), document (MongoDB), column (Cassandra), graph (Neo4j).
Multi-model databases, serverless NoSQL, and deeper SQL/NoSQL convergence.