Case Study: Starbucks achieves 50–100x faster data processing and rapid ML deployment with Databricks

Brewing data and AI at scale

Starbucks, serving millions of customers across 30,000+ stores, faced a data-scale and fragmentation challenge: petabytes of fast-changing structured and unstructured data siloed across systems, slow cluster provisioning, and a lack of unified tooling that blocked experimentation, reproducibility, and timely insights for inventory, forecasting and personalization.

The company built BrewKit on Azure Databricks and Delta Lake to create a single source of truth and a unified analytics platform—enabling batch and real-time pipelines, interactive notebooks, and MLflow-driven model experimentation. The result: democratized, collaborative data science with 1,000+ pipelines, 50–100x faster processing, ML deployments in ~15 minutes, and tangible improvements in demand forecasting, inventory management and personalized customer experiences.

Starbucks

Vishwanath Subramanian

Director of Data Engineering and Analytics