Customer Case Study: Condé Nast - Databricks

Condé Nast

Customer Case Study

Condé Nast

Condé Nast is one of the world’s leading media companies, counting some of the most iconic magazine titles in its portfolio, including, The New Yorker, Wired, and Vogue. The company uses data to reach over 1 billion people in print, online, on video, and on social media.

Industry

Media and Entertainment

Vertical Use Case

  • Personalize content recommendations
  • Create new brand experiences

Technical Use Case

  • Ingest, ETL
  • Machine Learning

The Challenges

As a leading media publisher, Conde 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 Apache Spark on EMR to power their data platform proved to be challenging:

  • Infrastructure complexity: Building and managing Spark clusters required lots of setup and constant maintenance, pulling teams from higher value activities.
  • Breaking down walls: Needed to find a common platform for teams to build data pipelines and advance analytics to better foster collaboration.
  • Too much data: Datasets were outgrowing existing data lake solutions.

 

The Solution

Databricks provides Conde Nast with a fully managed cloud platform that simplifies operations, delivers superior performance, and enables data science innovation.

  • Interactive Workspace: Data scientists can collaborate, share, and track data and insights, fostering an environment of collaboration.
  • Delta Lake: As data sets grew in volume (over 1 trillion data points per month), Delta Lake can keep up and allow for more use cases, such as data rewrites and data merges.
  • Managed MLflow: With MLflow, Condé Nast can easily manage the entire machine learning lifecycle, from tracking experiments to monitoring production models.

The Results

  • Improved Customer Engagement: With an improved data pipeline, Condé Nast can make better, faster, and more accurate content recommendations, improving the user experience.
  • Unified Approach: Data engineering and data science teams are now solving problems together and collaborating to build new content products and experiences.
  • Built for Scale: Datasets can no longer outgrow Condé Nast’s capacity to process and glean insights.
  • More Models in Production: With MLflow, Condé Nast’s data science teams can innovate their products faster. They have deployed over 1,200 models  in production.

 

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