Databricks > Case Studies > Global Media Giant Condé Nast Enhances Data Architecture for Scalability and Efficiency

Global Media Giant Condé Nast Enhances Data Architecture for Scalability and Efficiency

Technology Category

Analytics & Modeling - Machine Learning
Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Buildings
Cement

Applicable Functions

Sales & Marketing
Warehouse & Inventory Management

Use Cases

Picking, Sorting & Positioning
Predictive Maintenance

Services

Data Science Services
System Integration

About The Customer

Condé Nast is a global media company that cultivates 37 of the world’s most influential and iconic brands, including the New Yorker, Vogue, GQ, and Wired. The company strives to create the highest quality content across its digital, social, video, and print channels. It has 1 billion fans, 435 million social followers, and 75 million monthly print readers. This activity has generated 3.6 petabytes of data. The company recently aimed to intensify its focus on digital channels and extend its global reach.

The Challenge

Condé Nast, a global media company that cultivates 37 of the world’s most influential and iconic brands, was planning its global expansion. However, the company realized that its data architecture was too complex to provide the scalability it needed. The company had stored its data in siloed systems, with five different data sources integrated with its then query engine, Presto. Data engineers ran ETL jobs and processes on Databricks Lakehouse and stored the data in Amazon S3. They also created tables in Databricks and pointed them to the storage layer in AWS S3. The data warehousing team used Informatica to build data models, stored the results in S3, and worked with data engineers to point that data set back toward Presto so that teams could access it in data queries. This complex and siloed data architecture was hindering the company's growth and expansion plans.

The Solution

To simplify its data architecture and increase collaboration among its data teams, Condé Nast implemented dbt Cloud alongside Databricks Lakehouse. This gave all data teams access to the same data sets. The data science and data warehouse teams could now work more productively without relying solely on data engineers for simple tasks. The company also built a platform called Evergreen on Databricks Lakehouse and Amazon Web Services, which allowed teams across the company’s three geographic regions to access data. Condé Nast also used Databricks to build reusable data ingestion frameworks for the company’s four main data sources. This seamless integration with dbt Cloud enabled data warehouse engineers to build data models quickly for analytics, machine learning applications, and reporting. Data scientists could pull data transformed with dbt to build better machine learning use cases for personalizing Condé Nast’s products in advertising, consumer experiences, and content recommendations.

Operational Impact

The implementation of dbt Cloud alongside Databricks Lakehouse has significantly improved the productivity of Condé Nast's data science and data warehouse teams. They no longer have to rely solely on data engineers for simple tasks, which has increased efficiency and collaboration. The company has also seen an increase in self-service among data warehousing engineers. The new platform, Evergreen, has enabled teams across the company’s three geographic regions to access the same Silver (analytics-ready) and Gold (business-ready) data sets. This has improved data integrity and traceability. Furthermore, the company has been able to build reusable, centralized macros across its dbt instance, saving even more time and improving the quality of its data sets.

Quantitative Benefit