DatriseAI-first ETL

Harvest Databricks SQL Warehouse

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

How Datrise loads Harvest into Databricks SQL Warehouse

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

Harvest: Time tracking and project profitability for services teams.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Harvest entities map to Databricks SQL Warehouse

Harvest entityDatabricks SQL Warehouse objectNotes
time entriesharvest_time_entriesid PK · custom fields → VARIANT or STRUCT 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 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 Harvest 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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