loxal DEV

The biggest gotcha we both found when moving from RDBMS to graphs some years back (when we were users of early Neo4j versions, way before we were developers on the product) was the years of entrenched learning of relational modelling. We found it difficult to leave behind the design process of creating a normalized model and then denormalizing it for performance — we didn’t feel like we’d worked hard enough with the graph to be successful.

A graph database is purpose-built to handle highly connected data, and the increase in the volume and connectedness of today’s data presents a tremendous opportunity for  sustainable competitive advantage . When it comes to applying a graph database to a real-world problem, with real-world technical and business constraints, enterprise organizations choose graph databases for the following reasons: Minutes-to-Milliseconds Performance Query performance and responsiveness are at the top of many organizations’ concerns with regard to their data platforms. Online transactional systems – large web applications in particular – must respond to end users in milliseconds if they are to be successful.  In the relational world, as an application’s dataset size grows, JOIN pains begin to manifest themselves, and performance deteriorates. Using index-free adjacency, a graph database turns complex JOINs into fast graph traversals – which are constant time operations – thereby mainta...