Condé Nast, the publisher of iconic magazines such as Vogue, the New Yorker and Wired, uses data to reach over 1 billion people in print, online, on video, and on social media. With tremendous amounts of data to leverage, they struggled to manage infrastructure and enable data science productivity. With Databricks, cluster automation has eliminated unnecessary DevOps effort, Delta Lake has enabled them to build data pipelines that scale to 1 trillion data points per month, and data science innovation has been unlocked with a collaborative environment with MLflow to manage the entire ML lifecycle. This has allowed them to deliver personalized content across their brands to engage and retain customers.
As a leading media publisher, Condé Nast manages over 20 brands in their portfolio. On a monthly basis, their web properties garner 100+ million visits and 800+ million page views producing a tremendous amount of data. The data team is focused on improving user engagement by using machine learning to provide personalized content recommendations and targeted ads. However, running vanilla Spark to power their data platform proved to be challenging:
Databricks provides Condé Nast with a fully managed cloud platform that simplifies operations, delivers superior performance, and enables data science innovation.
With Databricks as the foundation for their data analytics and machine learning efforts, Condé Nast’s newfound insights into their customers has transformed the way they drive engagement across their 20+ brands.
Databricks has been an incredibly powerful end-to-end solution for us. It’s allowed a variety of different team members from different backgrounds to quickly get in and utilize large volumes of data to make actionable business decisions.”
– Paul Fryzel, Principal Engineer of AI Infrastructure, Condé Nast
Technical Talk at Spark + AI Summit EU 2019