Case Study: Condé Nast achieves personalized content at scale and 60% faster ETL with Databricks

Crafting bespoke content experiences for every customer

Condé Nast, the publisher behind Vogue, The New Yorker and Wired, needed to turn massive traffic and data (100M+ visits and 800M+ page views monthly, growing to ~1 trillion data points) into personalized content and targeted ads. Complex, maintenance-heavy Spark infrastructure, fragmented tools, and scaling limits hindered data science productivity and collaboration across its 20+ brands.

By moving to Databricks’ managed platform—cluster automation, Delta Lake, an interactive workspace and MLflow—Condé Nast simplified ETL and the ML lifecycle, scaled reliably, and unified engineering and data science. The platform delivered a 60% reduction in processing times, 50% cut in IT operational costs, over 1,200 models in production, and faster time-to-insight that powers improved content recommendations and customer engagement.

Condé Nast

Nana Essuman

Senior Director of Data Engineering & Data Warehouse