Case Study: ShopRunner achieves fast, scalable personalized shopping experiences with Databricks

ShopRunner - Customer Case Study

ShopRunner connects high-value online shoppers to top retailers by delivering personalized, data-driven shopping experiences. Their challenge was ingesting and harmonizing data from 100+ retailers — product feeds, logistics and behavioral logs — while keeping data quality high, meeting data science speed requirements, supporting many scheduled Spark jobs, and controlling costs.

By adopting Databricks and Snowflake to decouple storage and compute, ShopRunner simplified ETL and feature generation, improved job reliability, and enabled self-service analytics. The integrated stack powers Spark-based machine learning (including visual similarity recommendations), daily personalized feeds and trending-product emails, and scalable, lower-cost data storage — boosting data science productivity and recommendation quality.

ShopRunner

Greg Ball

Chief Technology Officer