Why Graph Databases Are Replacing SQL in Travel Route Planning

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Travel technology teams have long struggled to model complex, multi-modal journeys using relational databases, which become slow and inefficient when joining multiple tables to trace city connections. Graph databases like Neo4j and TigerGraph offer a fundamentally different approach by treating airports as nodes and flights as edges, making connections first-class data structures rather than costly JOIN operations. Path-finding algorithms such as Dijkstra's shortest path run significantly faster on graph structures, enabling real-time queries like finding a three-hop journey from Manchester to Bali with layover constraints. Neo4j's Cypher query language allows travel planners to express route patterns declaratively, closely mirroring how humans naturally think about multi-leg journeys. The shift is particularly valuable for airline alliance route optimisers and multi-modal journey planners that must reconcile data from different operators and booking systems into a single, continuous trip.

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