Case Study: S&P Global Cuts Costs and Speeds Queries with Dremio

How S&P Global is Building an Azure Data Lakehouse with Dremio

S&P Global, a market leader in analytics and financial information services, needed a better way to support its IT BI team’s internal reporting for service management data, cloud financials, and asset inventory. With slow query performance, data silos, and rising costs from complex data manipulations and Azure Cosmos DB-based table functions, the company struggled to deliver timely and accurate insights.

To address these issues, S&P Global implemented Dremio with Azure Data Lake Storage and Apache Iceberg to query data directly without duplication, improve governance, and enable self-service analytics. Dremio reduced query times by 70% and cut internal operational costs by 50% by eliminating Azure Cosmos DB, helping S&P Global streamline its data operations and improve decision-making.

S&P Global

Tian de Klerk

Director, Data Engineering