Why a developer ditched SQL and Elasticsearch for a graph database to map supply chains

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A developer building an investing knowledge graph found that traditional tools like Elasticsearch and SQL fell short when tracing multi-hop relationships between companies. Elasticsearch returned only keyword-matched documents and could not follow edges between entities, while SQL required knowing the exact number of relationship hops in advance. Recursive SQL CTEs offered a partial workaround but introduced performance concerns and schema limitations that made the approach impractical. The developer ultimately turned to Neo4j and Cypher queries, which natively traverse variable-depth paths and carry evidence — such as article IDs and event dates — at every link in the chain. The knowledge graph, built on top of 9,414 articles indexed in Elasticsearch, can now answer questions like how many steps connect ASML to NVIDIA through supply disruption events.

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