Database Systems

Document, key-value, wide-column, and other non-relational data stores built for flexibility and horizontal scale.

NewSQL systems delivering relational semantics and ACID compliance at global, multi-node scale.

Ultra-fast data stores that keep entire datasets in RAM for sub-millisecond read and write performance.

Purpose-built engines optimised for ingesting, storing, and querying timestamped data at high velocity.

Databases designed to handle the massive, continuous streams of sensor and device data from IoT deployments.

Triple stores and semantic databases for storing and querying knowledge represented as subject–predicate–object triples.

Unified engines that support multiple data models — relational, document, graph, and key-value — within a single platform.

Real-time data processing systems that combine the power of a database with continuous event stream processing.

Databases that model data as nodes and edges, enabling powerful traversal and analysis of deeply connected relationships.

Fully managed, cloud-native DBMS offerings from major providers delivering elastic scaling and zero-ops operation.

Lightweight databases that run inside a Java application process — no server required, ideal for embedded and edge deployments.

Purpose-built stores for high-dimensional embedding vectors, powering semantic search and AI-driven retrieval in LLM applications.

A verified directory of platforms that unify relational, document, graph, vector, and analytical workloads into a single engine for the AI era.

Databases for agentic AI: PostgreSQL, document, in-memory and vector stores, plus pgEdge Starfleet's Postgres branch per coding agent.

The 12 database types every AI engineer needs — SQL, graph, document, key-value, time-series, vector and more — with real tools and agent use cases.

Eight leading AI-ready data platforms, including ClickHouse, MongoDB, SingleStore, DataStax and Apache Cassandra, for fast ingest, vector search and analytics at scale.

Pay-per-use similarity search for AI and RAG applications, from fully managed platforms to object-storage-native and Postgres-integrated options.

How embeddings, ANN indexing and hybrid search work, and how to choose a vector layer for production RAG and agentic systems.

Six immutable-ledger and decentralised database platforms, including BigchainDB, Amazon QLDB, Alibaba LedgerDB, CovenantSQL and Postchain.

A hand-checked directory of biological and sequence databases, genome technology, gene expression and gene mapping resources.

Twelve leading key-value NoSQL stores — DynamoDB, Redis, Memcached, etcd, RocksDB, Couchbase, Aerospike and more — compared on performance, use cases and architecture.

Small-footprint databases such as YottaDB, eXtremeDB, Actian Zen, ObjectBox and FairCom Edge for real-time data management on edge devices.

Twenty leading DBaaS and cloud database management providers spanning relational, NoSQL, distributed SQL and analytics databases.

A curated list of leading cloud data warehouse platforms for large-scale analytical workloads.

Snowflake, Databricks, Amazon Redshift, Google BigQuery and other lakehouse and warehouse platforms compared on features, architecture and capabilities.

Fifteen platforms that unify transactional and analytical processing in a single engine across cloud and hybrid environments.

Property graph databases, graph analytics engines, managed cloud graph services and query frameworks for connected-data applications.

Twelve tools for graph databases, knowledge graphs, GraphRAG, decision intelligence and threat-intelligence graphs.

Seven leading property graph database platforms, including ArangoDB, NebulaGraph, Neo4j, TigerGraph and Memgraph.

Fourteen semantic graph and RDF platforms — AllegroGraph, Apache Jena, GraphDB, MarkLogic, Stardog, Virtuoso, Amazon Neptune and more.

A hub for every graph-related directory: knowledge graphs, graph databases, semantic graphs and RDF, query languages, graph neural networks and GraphRAG.

Standards, editors, reasoners, libraries, registries and platforms for building the semantic layer of knowledge graphs — OWL, RDFS, SKOS, SHACL and more.