DatriseAI-first ETL

Harvest Microsoft SQL Server

AI-first ETL from Harvest into Microsoft SQL Server. Governed entities, incremental sync, typed landing tables.

How Datrise loads Harvest into Microsoft SQL Server

Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization 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

Harvest: Time tracking and project profitability for services teams.

Microsoft SQL Server: Microsoft relational DB with enterprise features.

How Harvest entities map to Microsoft SQL Server

Harvest entityMicrosoft SQL Server objectNotes
time entriesharvest_time_entriesid PK · custom fields → NVARCHAR(MAX) JSON columns
projectsharvest_projectsid PK · linked to harvest_time_entries
clientsharvest_clientsid PK · linked to harvest_time_entries
invoicesharvest_invoicesid PK · linked to harvest_time_entries

FAQ

How does Datrise handle Harvest'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 Harvest to Microsoft SQL Server sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with a MERGE statement.

Related pipelines

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