Real-time Insights: Transforming Event Ticketing with Google Cloud
Industry: Event Ticketing & Live Entertainment
About customer:
Client is a dynamic platform enabling efficient management and real-time monitoring of ticket sales, inventory, and profitability for large-scale events. With millions of tickets listed daily, the platform serves as a critical decision-making hub for pricing, availability, and audience engagement strategies.
The challenge:
As the volume of ticketing data scaled rapidly, client needed:
1. Real-time insights into ticket availability, pricing trends, and sales performance.
2. An intuitive, self-service BI dashboard to assist with day-to-day operations and strategic planning.
3. A scalable and performant data architecture capable of ingesting million records daily.
4. Drill-downs and filters to analyze data by pack count, event sections, date ranges, and ticket types.
5. A better-performing backend than Cloud SQL for Looker visualizations, which faced delays of over 30+ minutes per query.
The solution:
Evonence LLC led a complete end-to-end transformation by migrating high-volume data from Cloud SQL to BigQuery using Google Datastream for real-time streaming. Developed reusable BigQuery CTEs to handle complex aggregations, reducing dashboard latency. Built two comprehensive Looker dashboards - Single Day Dashboard for operational overview and Master Dashboard focused on Pack 2 Count trends. Implemented dynamic filters and enabled drill-downs into ticket block sell-through trends across 1, 7, and 30-day windows.
Leveraging Google’s product suite:
1. BigQuery: Core analytics engine for querying billions of rows across multiple ticketing dimensions.
2. Looker: Self-service BI platform for building dashboards, defining business metrics, and interactive exploration.
3. Google Datastream: Real-time change data capture pipeline from Cloud SQL → BigQuery.
4. Cloud SQL: Used as the original data store before migrating to BigQuery.
5. GCP IAM & GitHub: Secure credential management and version control of LookML projects.
Cloud scale and speed:
Dashboard load times reduced from mins to seconds. BigQuery handled million rows, streaming in million new rows per day. Partitioned tables with 31-day expiration reduced storage and boosted performance.
Google AI-enhanced predictions:
Although not developed yet the architecture design can support predicted sell out dates based on historical availability and sales velocity, enhanced event-level pricing optimization through ML-ready data modeling in BigQuery, drill-down analytics to uncover buying patterns and inventory decay curves.
Partner role in the project:
Defined architecture and led end-to-end development, designed and built LookML models, Explores, and visualizations also delivered documentation, user training, and performance testing. Collaborated with business and technical teams to iteratively refine filters and metrics.
The results:
Real-time visibility into ticket sales, availability and profit margins with filterable dashboards for strategic decisions. Cost-effective data exploration with partitioned datasets and performance uplift by migrating to BigQuery from Cloud SQL. Improved event management efficiency through drill-down time series analytics.
High availability:
1. Serverless BigQuery ensures uninterrupted querying and scaling.
2. Looker’s live connection to BigQuery keeps dashboards fresh without delay.
3. Datastream CDC pipeline guarantees near-real-time ingestion from Cloud SQL.
4. IAM-controlled access across Looker and BigQuery ensures secure availability.
AI-based predictions:
Future integration with Google Cloud AI tools (e.g., AutoML, VertexAI) for forecasting demand spikes, buyer segmentation & personalization.
