SpotDraft to Data Warehouse

Warehouse integrations should be designed as repeatable exports, not one-time giant pulls.

This guide is for analytical completeness and reporting use cases, not for real-time workflow automation.

Recommended extraction strategy

1. choose an updated timestamp checkpoint
2. request paged results in a stable order
3. persist the last successful checkpoint only after processing completes
4. run a periodic reconciliation window with slight overlap

scheduled warehouse sync
  -> paged SpotDraft API extraction
  -> normalize contracts, parties, metadata, lifecycle timestamps
  -> load analytics tables

Warehouse syncs are one of the few places where scheduled polling is expected, because the goal is analytical completeness rather than low-latency workflow automation. That is different from lifecycle integrations, where webhooks should usually be the primary signal.

Backfills

For historical loads:

• split the export into manageable windows
• throttle requests intentionally
• monitor queue size and response times
• persist partial progress so you can resume cleanly

Source data to plan for

Warehouse exports usually need a normalized model for:

• contract ids and lifecycle timestamps
• contract type or workflow identifiers
• counterparties and signatories where exposed
• metadata or external metadata used for joins
• execution or repository milestones

Related guides

• Use Event-Driven Integrations if the warehouse also needs near-real-time downstream triggers.
• Review SpotDraft integrations to separate analytical exports from native operational integrations.