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Retail & Consumer GoodsSourced

Case Study: Casey's halves daily operational data delivery from 8 hours to 4 with Databricks SQL

Casey's Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Casey's
Industry
Retail & Consumer Goods
Challenge
A legacy warehouse missed SLAs for early-morning insights.
Headline result
Migrating mixed workloads to a cloud-native lakehouse

Key results

8h to 4h
Daily operational data delivery time
>50%
Faster than planned migration completion
100%
Analytics workloads migrated to Databricks SQL

The challenge

Casey's, a US convenience store and fuel retailer, ran a legacy on-premises warehouse with mixed workloads that created performance bottlenecks, missed SLAs, and delayed operational insights needed before 6:00 a.m.

The solution

Casey's migrated to a cloud-native lakehouse using Databricks SQL as the analytics layer, eliminating redundant data copies and separating ingestion from query workloads.

We just weren't fast enough. As data volumes grew and insights became more critical, that wasn't acceptable anymore.

RF
Ryan Fye
Director of Data Strategy, Casey's

The results, in context

Casey's cut the time to deliver daily operational data from 8 hours to 4 hours, completed its migration more than 50% faster than planned, and migrated 100% of its analytics workloads to Databricks SQL.

Products used

Databricks Databricks SQLDatabricks Databricks Data Intelligence Platform