As cloud data volumes grow, so do storage costs. Many organizations continue to store large datasets in expensive systems—such as Cosmos DB, Azure SQL, or managed NoSQL platforms—despite using that data primarily for analytics, not transactional workloads.

Enterprises running on traditional SQL platforms—such as SQL Server, Oracle, Teradata, or Netezza—often find themselves at a crossroads: increasing costs, limited scalability, and a lack of agility for modern analytics and AI. At the same time, moving critical data and logic to a platform like Databricks requires more than just “lift and shift”—it demands careful planning, tooling, and validation.

Power BI is a powerful tool for interactive dashboards and analytics—but as datasets grow and logic becomes complex, report performance and maintainability often suffer. Business logic written in DAX becomes harder to govern, and refreshes slow down under the weight of large semantic models or duplicated transformations.