Databricks
Case Studies
Global Media Giant Condé Nast Enhances Data Architecture for Scalability and Efficiency
Overview
Global Media Giant Condé Nast Enhances Data Architecture for Scalability and EfficiencyDatabricks |
Analytics & Modeling - Machine Learning Application Infrastructure & Middleware - Data Exchange & Integration | |
Buildings Cement | |
Sales & Marketing Warehouse & Inventory Management | |
Picking, Sorting & Positioning Predictive Maintenance | |
Data Science Services System Integration | |
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
30% increase in self-service among data warehousing engineers | |
85 dbt models built on its data platform, Evergreen, across various domains | |
16 hours saved per data integration sprint project | |