Customer Case Study: Showtime - Databricks


Customer Case Study


SHOWTIME® is a premium television network and streaming service featuring award-winning Original Series and Original Limited Series like Shameless, Homeland, Billions, The Chi, Ray Donovan, SMILF, The Affair, Patrick Melrose, Our Cartoon President, Twin Peaks and more.

Vertical Use Case

  • Subscriber Churn Analysis
  • Smarter Marketing Decisions
  • Revenue Forecasting

Technical Use Case

  • Data ingest, ETL
  • Machine learning

The Challenges

The Data Strategy team at Showtime is focused on democratizing data and analytics across the organization. They collect huge volumes of subscriber data (e.g. shows watched, time of day, devices used, subscription history, etc) and use machine learning to predict subscriber behavior and improve scheduling and programming.  Unfortunately, legacy technology architectures were pulling teams away from high-value data science activities.

  • Infrastructure complexity: Finding the infrastructure that allowed for flexibility but didn’t require constant maintenance.
  • Inefficient Machine Learning Pipelines: The process to develop, train, and deploy machine learning models was highly manual and error-prone, leading to slower time-to-market of new models.


The Solution

The Databricks Unified Analytics Platform provides Showtime with a fully managed service that has greatly simplified data engineering and improved the productivity of their data science teams.

  • Automated Infrastructure: Fully managed, serverless cloud infrastructure for speed, cost control and elasticity.
  • Interactive Workspace: Make collaboration easy and seamless across teams and multiple programming languages to accelerate data science productivity.
  • Simplified ML Lifecycle: MLflow allows them to streamline the entire ML lifecycle.


The Results

Databricks has helped Showtime democratize data and machine learning across the organization, creating a more data-driven culture.

  • 6x Faster Pipelines: Data pipelines that took over 24 hours are now run in less than 4 hours enabling teams to make decisions faster.
  • Removing Infrastructure Complexity: Fully managed platform in the cloud with automated cluster management allows the data science team to focus on machine learning rather than hardware configurations, provisioning clusters, debugging, etc.
  • Innovating the Subscriber Experience: Improved data science collaboration and productivity has reduced time-to-market for new models and features. Teams can experiment faster leading to a better, more personalized experience for subscribers.


Being on the Databricks platform has allowed a team of exclusively data scientists to make huge strides in setting aside all those configuration headaches that we were faced with. It's dramatically improved our productivity.

Josh McNutt
Senior Vice President of Data Strategy and Consumer Analytics