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NBCUniversal’s Seamless Migration: Unlocking Scalable Analytics with Databricks

What happened
Based on Databricks Newsroom · Jul 29, 2026

NBCUniversal migrated its data infrastructure to Databricks' platform to modernize analytics and machine learning workloads, reducing compute costs and improving scalability for content launches and live events.

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NBCUniversal’s Seamless Migration: Unlocking Scalable Analytics with Databricks
Databricks Newsroom — Databricks
Key points
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As one of the world's leading media and entertainment companies, NBCUniversal relies on data to deliver unparalleled entertainment experiences across its global broadcast, cable, and streaming networks.
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With content delivery demanding real-time analytics, complex data pipelines, and advanced machine learning, the company's data infrastructure is a critical business asset that supports decision making across the portfolio.
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NBCUniversal has been at the forefront of innovation for 100 years and recognized a need to modernize its data infrastructure to support its growing content ecosystem while maintaining operating costs.
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As data volumes grew and analytics demands evolved, the company sought a more scalable and integrated solution.
Key numbers
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NBCUniversal migrated its data infrastructure to Databricks with EXL's support, achieving a 30% reduction in compute costs and a 25% reduction in delayed jobs, while enabling parallel execution and ML innovation for its media...

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.

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