Case Study: Ticketmaster achieves reduced development friction and accelerated ML-powered fraud prevention with Confluent

Ticketmaster Leverages Confluent to Reduce Development Friction and Boost Machine Learning

Ticketmaster, which sells over 500 million tickets a year, faced heavy development friction from hundreds of interacting software systems while migrating to a microservices architecture. To centralize data and support new machine learning efforts against fraudulent resellers, Ticketmaster adopted Confluent (with Apache Kafka) to unify streams and give teams a single, reliable data source.

Confluent implemented the Confluent Platform and Kafka as a central inventory stream that abstracts systems and protects against single points of failure, enabling faster innovation and quicker rollouts of new technologies. The Confluent solution reduced development friction, improved forecasting, and let Ticketmaster’s data science teams deploy ML models that prioritize legitimate customers and rapidly adapt to changing fraud strategies.

Ticketmaster

Chris Smith

VP of Engineering Data Science