Case Study: Condé Nast achieves greater data collaboration and scalability with Databricks

Serving up multimedia content on a global scale

Condé Nast, the global media company behind brands like Vogue, GQ, Wired, and The New Yorker, was struggling with a data architecture that had become too complex to support its global expansion. To improve scalability and collaboration, the company turned to Databricks and dbt Cloud alongside Databricks Lakehouse so its data teams could work from the same trusted datasets.

Databricks helped Condé Nast build reusable ingestion frameworks and a shared lakehouse platform, enabling teams across three regions to access the same Silver and Gold data sets. The result was faster model development for analytics and machine learning, greater self-service, and less reliance on data engineers; self-service among data warehousing engineers increased by 30%, while productivity improved and infrastructure costs became easier to monitor and control.

Condé Nast

Nana Essuman

Senior Director of Data Engineering & Data Warehouse