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.

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.

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

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.

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.

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.

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.