Virgin Australia modernizes data infrastructure on Databricks to improve baggage handling and loyalty
Virgin Australia replaced a constrained on-premises data warehouse with the Databricks Data + AI Platform, implementing a Delta Lake medallion architecture with MLflow and Unity Catalog to power recommendation engines and propensity models, achieving a 75% increase in real-time data availability, 50% decrease in data ingestion time, 90% faster ML model deployment, and a 44% reduction in mishandled baggage.
Overview
Virgin Australia replaced a constrained on-premises data warehouse with the Databricks Data + AI Platform, implementing a Delta Lake medallion architecture with MLflow and Unity Catalog to power recommendation engines and propensity models, achieving a 75% increase in real-time data availability, 50% decrease in data ingestion time, 90% faster ML model deployment, and a 44% reduction in mishandled baggage.
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Inspect the highlighted sourceThe challenge
Virgin Australia's data was siloed and managed separately across teams, and a constrained legacy on-premises data warehouse with disparate ETL tools caused inconsistent information, communication gaps and slow operational decision-making.
The solution
Virgin Australia implemented the Databricks Data + AI Platform on AWS with a Delta Lake medallion architecture (bronze/silver/gold layers), replaced SAS with MLflow to manage the ML lifecycle for recommendation engines and propensity models, added Unity Catalog for governance and federated queries, and plugged in Power BI for reporting and Databricks Assistant/Genie Code to boost productivity.
Reported business value
Virgin Australia achieved a 75% increase in real-time data availability, a 50% decrease in data ingestion time, 90% faster deployment of ML models, and a 44% reduction in mishandled baggage.
Sources
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This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
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