The useful question is what changes for users, developers or buyers, and whether the announcement stays industry context or becomes something people can actually use.

NBCUniversal, a global media and entertainment company, sought to modernize its data infrastructure to handle growing analytics demands and rising compute costs. The existing slot-based reservation model led to resource contention and inefficiencies, prompting the company to explore alternatives. NBCUniversal partnered with Databricks and EXL to migrate to the Databricks Data Intelligence Platform, aiming to improve flexibility, performance, and operational efficiency across its data pipelines and machine learning workloads.

The migration involved transforming data structures, optimizing code, and redesigning compute strategies to align with Databricks' architecture. EXL and Databricks developed a phased approach, beginning with a discovery phase to inventory workloads and dependencies. This assessment identified priority artifacts for migration and established success criteria, ensuring minimal operational disruption during the transition to the new platform.

A pilot phase validated the migration approach using a subset of NBCUniversal's workloads. EXL created custom automation tools, including a SQL translation engine to convert SQL syntax into optimized Spark SQL, an orchestration migrator to regenerate DAGs as Lakeflow Jobs, and a data transfer orchestrator to move data to Delta Lake efficiently.

The migration unified NBCUniversal's data engineering, machine learning, and analytics on a single platform, replacing multiple specialized tools. Databricks' lakehouse architecture, supported by DeltaLake and Apache Iceberg, enabled handling of structured and unstructured data. Granular compute control and Unity Catalog for governance further enhanced scalability, security, and performance across NBCUniversal's data ecosystem.