Data operations integrations and automations
Getting data out of the systems that hold it and back into the ones that need it, with history kept and row counts reconciled.
The warehouse is only as trustworthy as the least careful thing writing to it. These guides are about getting data out of operational systems without losing deletes or ordering, monitoring it for the failures that do not raise an error, and pushing it back out to the systems where people work. The theme is that a pipeline reporting success and a pipeline being correct are different claims.
Built with Tray Headless
How to build a CRM to warehouse sync
Capture history instead of current state, handle deletes and field changes, land raw then model, and prove the row counts.
Integration
How to build reverse ETL from the warehouse
Push modelled data back into the tools people work in, syncing deltas, respecting field ownership, and never overwriting a human.
Integration
How to build a customer 360 and master data sync
Pick a survivorship order per attribute, resolve identity on more than email, and publish a golden record every system can point at.
Integration
How to build data quality monitoring
Test what breaks decisions, alert the owner rather than a channel, and say what depends on a failure.
Automation
How to build schema change management
Detect a source change before it breaks a model, resolve what depends on it, and tell the owner in time to act.
Automation
Guides for other teams
- Revenue operations (17)
- Finance (8)
- Customer success (5)
- People operations (6)
- IT and security (7)
- Marketing (3)
- Platform engineering (4)
- AI operations (8)
- Legal and compliance (5)