Leading media and entertainment company accelerates analytics with Databricks data intelligence platform

A leading media and entertainment company operates one of the largest portfolios of film, television, and streaming properties. Its advertising and analytics teams support hundreds of global brands through advanced audience insights, sales strategy, and cross-platform campaign measurement.

The company’s internal analytics platform serves as a central hub for performance reporting—providing sales, marketing, and executive teams with timely insights on content and advertising performance. As data volumes expanded and reporting demands grew, the legacy environment struggled to keep pace, slowing decision cycles and limiting visibility across the organization.

The company’s analytics platform was originally built on Airflow and legacy data systems that could no longer handle its rapidly increasing data scale. Pipelines were fragile, slow, and difficult to maintain—often taking several hours to complete data updates.

Multiple disconnected workflows created operational inefficiencies, and engineers spent excessive time debugging and maintaining pipelines rather than delivering insights. These limitations delayed campaign and content reporting, reduced confidence in data quality, and made it harder to deliver accurate, on-demand insights for internal stakeholders and advertising partners.

The organization sought to modernize its analytics environment with a unified, cloud-based data platform capable of supporting near-real-time processing, improved governance, and simplified maintenance.

The company partnered with Lumenalta to migrate its analytics and reporting ecosystem to the Databricks Data Intelligence Platform, consolidating data pipelines, analytics, and governance under a single, scalable environment. 

Using Databricks Spark for distributed data processing, Unity Catalog for centralized governance, and Databricks’ unified UI for development and monitoring, the new architecture simplifies pipeline creation and management. Engineering teams can now write, schedule, and track jobs from a single workspace—reducing fragmentation and improving observability.

Enhanced reliability: Consolidated multiple fragmented systems into a single Lakehouse-based environment, improving overall data trust and maintainability.

The Databricks-based analytics platform delivers significant performance and productivity improvements across the organization. Pipeline runtime has decreased from over three hours to approximately 30 minutes, while pipeline errors are expected to drop by 70 percent. Engineering time spent on maintenance will be cut by at least half, freeing teams to focus on innovation and insights.

With these improvements, the company now provides faster, more trustworthy data to its internal and client-facing teams—enabling accurate, real-time reporting and decision-making. The platform’s scalability supports continued growth in data volume and complexity, ensuring the organization can make smarter, data-driven decisions and deliver better outcomes across its media and streaming portfolio.