DatriseAI-first ETL

Harvest Forecast to Redash Integration

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

How Datrise loads Harvest Forecast into Redash

Datrise syncs Harvest Forecast's assignments, people, projects, clients, milestones, and scheduled hours into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.

Ideal for lightweight, query-driven dashboards.

Endpoints

Harvest Forecast: Resource scheduling and project capacity planning.

Redash: Open-source SQL client for queries, visualizations, and dashboards.

How Harvest Forecast entities map to Redash

Harvest Forecast entityRedash objectNotes
assignmentsharvest_forecast_assignmentsid PK · custom fields → flattened columns for query results
peopleharvest_forecast_peopleid PK · linked to harvest_forecast_assignments
projectsharvest_forecast_projectsid PK · linked to harvest_forecast_assignments
clientsharvest_forecast_clientsid PK · linked to harvest_forecast_assignments

FAQ

How does Datrise handle Harvest Forecast's custom fields in Redash?

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

How does the Harvest Forecast to Redash sync stay up to date?

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

Related pipelines

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