DatriseAI-first ETL

Nimble Databricks SQL Warehouse

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

How Datrise loads Nimble into Databricks SQL Warehouse

Datrise syncs Nimble's relationship records, deals, tasks, and activity intelligence 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

Nimble: Relationship-focused CRM for SMB sales teams.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Nimble entities map to Databricks SQL Warehouse

Nimble entityDatabricks SQL Warehouse objectNotes
relationship recordsnimble_relationship_recordsid PK · custom fields → VARIANT or STRUCT columns
dealsnimble_dealsid PK · linked to nimble_relationship_records
tasksnimble_tasksid PK · linked to nimble_relationship_records
activity intelligencenimble_activity_intelligenceTIMESTAMP events

FAQ

How does Datrise handle Nimble'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 Nimble 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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