From SQL to Graph: 5 Questions to Ask Before You Get Started

Relational databases have long been foundational for organizations, but transitioning from SQL to graph models is gaining traction due to the latter's ability to reveal context and connectedness that SQL struggles to express. The SQL2Graph process, supported by tools like HyGM and Memgraph, offers an interactive approach to schema modeling, allowing for a guided transition rather than a complete manual overhaul. This transition involves automated and incremental modes, enabling users to either quickly migrate or maintain fine-grained control over complex schemas. Keeping SQL and graph data in sync poses challenges, especially in dynamic environments, but solutions like Kafka-based change data capture aid in maintaining real-time updates. The choice of LLM models for generating Cypher queries is critical, with agent-enabled models offering improved quality and adaptability. Crucially, SQL2Graph allows users to retain their existing SQL schemas, proposing graph models based on current structures, and facilitates iterative refinement to better represent data relationships, thereby enhancing LLM applications through graph-native retrieval without altering the original relational schema.