Graph Engines
Graph engines run graph computation over data stored in other systems, projecting nodes and relationships on the fly instead of requiring you to move everything into a standalone graph database.
This catalog focuses on current graph engines and co-processors that operate over existing data platforms. To be included, an offering has to meet three criteria:
No primary graph storage of its own
- It is not a general-purpose graph DBMS and does not act as the primary system of record for graph data. Instead, it runs graph computations over data stored in other systems, such as warehouses, relational databases, event streams, or embedded platforms.
Graph computation layer over existing platforms
- Its main role is to project a graph model onto existing data and run graph algorithms, traversals, or reasoning there, typically with minimal or no ETL. Examples include Snowflake-native coprocessors, graph engines running on SQL warehouses, and streaming graph engines running on Kafka or Kinesis.
Exposes graph query and analytics as a service
- It provides a programmable graph engine interface, via query languages and/or graph algorithm APIs, for pattern matching, traversals, centrality, community detection, pathfinding, and related workloads, independent of any particular visualization UI.
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The table below summarizes these products across key dimensions, including backend coupling, availability and licensing, languages, number of algorithms, representative built‑in algorithms, and data sources, so you can quickly see where each player fits and which ones merit a deeper look.