DatriseAI-first ETL

Harvest Amazon Redshift

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

How Datrise loads Harvest into Amazon Redshift

Datrise syncs Harvest's time entries, projects, clients, invoices, and utilization into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Harvest: Time tracking and project profitability for services teams.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Harvest entities map to Amazon Redshift

Harvest entityAmazon Redshift objectNotes
time entriesharvest_time_entriesid PK · custom fields → SUPER 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 Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the Harvest to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

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