Hi, glad to help! I'm a DevRel advocate at Kuzu, and have spent a decent amount of time in other database paradigms thinking about these things. I'm familiar with SurrealDB too.
Although I cannot comment too much SurrealDB's exact capabilities and performance at this point, I can definitely highlight that at the data modeling and query language-level: Kuzu's data model is a property graph model (so an actual "graph" model rather than a multi-model database) and Kuzu implements Cypher as its query language, which is already widely adopted in the industry and is very intuitive to write (for both humans and LLMs).
Although Surreal DB does indeed offer an embedded mode, Kuzu is by design 100% embedded, is super-lightweight and can run natively in many environments, such as browsers, Android applications, AWS Lambda (serverless) and we're especially designed to be a VERY Python-friendly graph database that integrates with pretty much all well-known Python libraries. Because of its columnar storage layer, Kuzu can seamlessly read and write different data formats, such as Panda/Polars DataFrame, Arrow tables, Iceberg or Delta Lake tables and seamlessly move data between advanced graph analytics libraries like NetworkX. For anything related to graph computation, Kuzu is likely to have all the right tools and utilities to help you solve the problem at hand.
In my opinion, it's a myth that databases are heavy, monolithic pieces of software, and hopefully, using Kuzu will demonstrate that it's totally possible to have data in your primary store but seamlessly move it to a performant graph storage layer when required, and move the results back with minimum friction and cost. Hope that helps!
laminarflow027|11 months ago
Although I cannot comment too much SurrealDB's exact capabilities and performance at this point, I can definitely highlight that at the data modeling and query language-level: Kuzu's data model is a property graph model (so an actual "graph" model rather than a multi-model database) and Kuzu implements Cypher as its query language, which is already widely adopted in the industry and is very intuitive to write (for both humans and LLMs).
Although Surreal DB does indeed offer an embedded mode, Kuzu is by design 100% embedded, is super-lightweight and can run natively in many environments, such as browsers, Android applications, AWS Lambda (serverless) and we're especially designed to be a VERY Python-friendly graph database that integrates with pretty much all well-known Python libraries. Because of its columnar storage layer, Kuzu can seamlessly read and write different data formats, such as Panda/Polars DataFrame, Arrow tables, Iceberg or Delta Lake tables and seamlessly move data between advanced graph analytics libraries like NetworkX. For anything related to graph computation, Kuzu is likely to have all the right tools and utilities to help you solve the problem at hand.
In my opinion, it's a myth that databases are heavy, monolithic pieces of software, and hopefully, using Kuzu will demonstrate that it's totally possible to have data in your primary store but seamlessly move it to a performant graph storage layer when required, and move the results back with minimum friction and cost. Hope that helps!