Databricks (Data Warehouse) and Snowplow (Analytics) are commonly run side-by-side. When teams operate both, the question that follows is always how to keep the two in sync without re-entering data twice.
What people typically integrate
- Keep records in sync between the two systems so teams stop re-entering data
- Trigger downstream actions in one tool from events in the other
- Surface relevant context from one system inside the other so users don't switch tabs
- Aggregate data into shared reporting without manual exports
How the integration works
There are usually a few paths to choose from, depending on stack and budget:
- rest-api" class="glossary-link text-db-cyan hover:text-db-cyan-dark underline decoration-dotted underline-offset-2" title="Representational State Transfer - an architectural style for building web services....">REST API + scheduled sync (build once, run on cron)
- Webhook-driven sync (real-time, event-based)
- Zapier / Make / n8n (low-code, fast to ship, ongoing per-task fees)
- Custom Python / ORM, admin interface, authentication, and 'batter...">Django adapter (full control, no per-task fees, requires engineering)
What flows between the systems
Common records that move back and forth:
- primary records (customers / orders / contacts)
- events and state changes
- metadata and tags
- user actions and timestamps
Complexity
This integration is typically medium. Both tools have a REST API, so a custom integration is straightforward to build but does require some engineering. Off-the-shelf connectors may also exist.
Need help building this?
Purcell Analytics builds custom integrations between Databricks and Snowplow for businesses that want this running reliably without DIY headaches. Schedule a consultation to talk through your specific use case.