Modeling a digital twin of a food supply chain using BigQuery Graph

This article explores using BigQuery Graph to model a digital twin of a food supply chain, enabling restaurants to manage complex operations and dependencies.

• •BigQuery Graph creates a searchable map of nodes and edges representing items, recipes, and locations without moving data to a new database
• •The Graph View semantic layer defines relationships between existing tables for efficient dependency tracing
• •Graph Query language simplifies complex multi-domain queries, such as identifying affected menu items during food safety issues
• •Practical applications include surgical recalls when suppliers report contamination and weather risk analysis to reroute supplies
• •Key principles: use graphs for relationships, maintain clean data engineering, and store edge properties like lead times for increased utility