Jobber to Databricks SQL Warehouse Integration

How Datrise loads Jobber into Databricks SQL Warehouse

Datrise syncs Jobber's jobs, clients, quotes, invoices, visits, and expenses 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

Jobber: Field service quoting, scheduling, and job tracking.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Jobber entities map to Databricks SQL Warehouse

Jobber entity	Databricks SQL Warehouse object	Notes
jobs	`jobber_jobs`	id PK · custom fields → VARIANT or STRUCT columns
clients	`jobber_clients`	id PK · linked to jobber_jobs
quotes	`jobber_quotes`	id PK · linked to jobber_jobs
invoices	`jobber_invoices`	id PK · linked to jobber_jobs

FAQ

How does Datrise handle Jobber'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 Jobber 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

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