DatriseAI-first ETL

Harvest Looker Studio

AI-first ETL from Harvest into Looker Studio. Governed entities, incremental sync, typed landing tables.

How Datrise loads Harvest into Looker Studio

Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Harvest: Time tracking and project profitability for services teams.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Harvest entities map to Looker Studio

Harvest entityLooker Studio objectNotes
time entriesharvest_time_entriesid PK · custom fields → flattened columns for chart fields
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 Looker Studio?

Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.

How does the Harvest to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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