DatriseAI-first ETL

Harvest Google BigQuery

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

How Datrise loads Harvest into Google BigQuery

Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Harvest: Time tracking and project profitability for services teams.

Google BigQuery: Serverless analytics warehouse on GCP.

How Harvest entities map to Google BigQuery

Harvest entityGoogle BigQuery objectNotes
time entriesharvest_time_entriesid PK · custom fields → JSON or nested/repeated (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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Harvest to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

Connect Harvest to Google BigQuery the easy way

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