Case Study: Monsanto achieves real-time, scalable genomic analysis for faster seed development with Neo4j

Monsanto - Customer Case Study

Monsanto’s data science team, led by Tim Williamson, needed to extract real-time, reproducible insights from massive genomic and pedigree datasets accumulated over decades. Their legacy Oracle relational systems made common genetic queries and pipeline-wide analyses take seconds to hours, preventing scalable inference and risking inconsistent results for critical parentage and breeding decisions.

They re-modeled the data as a graph and implemented a Neo4j cluster (chosen over Titan for real-time performance and ACID guarantees), built a rich API, and synced legacy writes via Oracle GoldenGate → Kafka → a custom adapter so updates appear in seconds. In production for two years, the platform supports ~120–130 apps and data scientists, has handled about 700 million REST requests, and reduced analyses that once took minutes or hours to seconds, accelerating genetic selection and enabling broad internal innovation.

Monsanto

Tim Williamson

Data Scientist