Hi everyone,

I'm working on a project where we collect visitor activity from multiple touchpoints such as ticketing, membership sign-ups, event participation, mobile app interactions, and digital membership card usage.

The goal is to create a unified visitor profile that can answer questions like:

1. Which members visit most frequently?
2. What exhibits or events drive the highest engagement?
3. Which memberships are likely to be renewed?
4. How can we personalize offers based on visitor behavior?

I'm considering using a lakehouse approach where raw visitor events are ingested into Delta tables, transformed into curated datasets, and then used for analytics and reporting.

One challenge is handling millions of visitor events while keeping member profiles updated in near real time. Digital membership card generate valuable check-in and engagement data, so I'd like to make that information available for dashboards, recommendation models, and renewal campaigns.

Has anyone built a similar visitor analytics solution on Databricks?

I'd be interested in learning about:

1. Recommended data models for visitor and membership data
2. Streaming vs. batch ingestion for check-in events
3. Delta Live Tables or other pipeline approaches
4. Best practices for maintaining a 360° visitor profile
5. Performance optimization for large-scale visitor analytics