Case Study: Weverse Company achieves 80% faster time-to-market for new fan experiences with Databricks Lakehouse Platform

Weverse uses fan-driven insights to fuel engagement strategies with Databricks

Weverse Company is a global fandom platform launched in 2019 that connects artists and fans, growing rapidly to over 6.4 million average monthly active users and seeing strong year-over-year customer growth. That surge exposed fragmented, siloed data across multiple AWS accounts, redundant ETL processes and poor ingestion performance, leaving teams unable to reliably access fan insights or scale analytics to inform product and marketing decisions.

By adopting the Databricks Lakehouse Platform on AWS—using Delta Lake, Delta Live Tables, autoscaling compute and native Tableau integration—Weverse unified its data into a single, reliable source of truth and simplified pipelines for analysts, engineers and data scientists. The platform cut pre-work to access insights by 80–90%, sped time-to-market for new experiences, enabled Databricks-powered dashboards for actionable fan analytics, and positioned Weverse to launch new data-driven services and improve fan engagement.

Weverse Company