DatriseAI-first ETL

Copper Databricks SQL Warehouse

AI-first ETL from Copper into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Copper into Databricks SQL Warehouse

Datrise syncs Copper's Google Workspace CRM entities, opportunities, and relationship timelines into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Copper: Google Workspace-native CRM.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Copper entities map to Databricks SQL Warehouse

Copper entityDatabricks SQL Warehouse objectNotes
Google Workspace CRM entitiescopper_google_workspace_crm_entitiesid PK · custom fields → VARIANT or STRUCT columns
opportunitiescopper_opportunitiesid PK · linked to copper_google_workspace_crm_entities
relationship timelinescopper_relationship_timelinesTIMESTAMP events

FAQ

How does Datrise handle Copper's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Copper to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

Early access

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