Jobber to Azure Synapse Integration
AI-first ETL from Jobber into Azure Synapse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Jobber into Azure Synapse
Datrise syncs Jobber's jobs, clients, quotes, invoices, visits, and expenses into Azure Synapse 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 COPY into staging, then a MERGE on stable id, so re-runs update only what changed. Hash distribution on the join id with date partitioning on facts. Synapse dedicated pools reward good hash-distribution choices, so Datrise distributes on entity ids to avoid data-movement-heavy joins.
Ideal for Azure analytics estates feeding Power BI.
Endpoints
Jobber: Field service quoting, scheduling, and job tracking.
Azure Synapse: Microsoft analytics workspace with SQL pools.
How Jobber entities map to Azure Synapse
| Jobber entity | Azure Synapse object | Notes |
|---|---|---|
| jobs | jobber_jobs | id PK · custom fields → NVARCHAR(MAX) JSON 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 Azure Synapse?
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 Azure Synapse types.
How does the Jobber to Azure Synapse sync stay up to date?
It runs incrementally — Datrise uses COPY into staging, then a MERGE on stable id.
Related pipelines
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