Case Study: Ticketmaster achieves real-time, seat-level reporting and data modernization with Elastic

Ticketmaster - Customer Case Study

Ticketmaster, a 40‑year ticketing company, faced a classic legacy-data challenge: event metadata and sales were trapped in relational systems and nightly ETL pipelines that produced brittle, slow reporting (24–48 hour delays) and risked impacting operational databases. With massive fan demand (e.g., 10M unique visitors in a minute for Hamilton) and a need for seat‑level, real‑time views for clients and fans, the company needed a way to modernize incrementally without breaking existing systems.

The team implemented a piecewise modernization: tapping transaction logs into Kafka, using Spring Boot for “smart collapse” and indexing improvements, and indexing de‑normalized, idempotent seat‑level data into Elasticsearch. The result was real‑time, comparable event reporting that doesn’t touch the operational store, supports ~15k kiloseats/sec per host, and delivered major infrastructure gains (double performance, ~6× storage efficiency, roughly half the cost), alongside practical learnings about plugins and compression for large-node data.

Ticketmaster

John Carnahan

Executive Vice President