Copper → Microsoft SQL Server
AI-first ETL from Copper into Microsoft SQL Server. Governed entities, incremental sync, typed landing tables.
How Datrise loads Copper into Microsoft SQL Server
Datrise syncs Copper's Google Workspace CRM entities, opportunities, and relationship timelines into Microsoft SQL Server as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.
Sync is incremental: Datrise uses a watermark on updated-at, applied with a MERGE statement, so re-runs update only what changed. Optional partitioned tables on a date partition function. SQL Server defaults to a case-insensitive collation, so Datrise preserves original casing in a metadata column to avoid silent key collisions.
Ideal for Microsoft-stack analytics and Power BI Import models.
Endpoints
Copper: Google Workspace-native CRM.
Microsoft SQL Server: Microsoft relational DB with enterprise features.
How Copper entities map to Microsoft SQL Server
| Copper entity | Microsoft SQL Server object | Notes |
|---|---|---|
| Google Workspace CRM entities | copper_google_workspace_crm_entities | id PK · custom fields → NVARCHAR(MAX) JSON columns |
| opportunities | copper_opportunities | id PK · linked to copper_google_workspace_crm_entities |
| relationship timelines | copper_relationship_timelines | datetime2 events |
FAQ
How does Datrise handle Copper's custom fields in Microsoft SQL Server?
Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Microsoft SQL Server types.
How does the Copper to Microsoft SQL Server sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with a MERGE statement.
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Early access
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