Everhour to Birst Integration
AI-first ETL from Everhour into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads Everhour into Birst
Datrise syncs Everhour's time entries, projects, tasks, users, clients, and budgets into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
Everhour: Time tracking and budgets for project teams.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How Everhour entities map to Birst
| Everhour entity | Birst object | Notes |
|---|---|---|
| time entries | everhour_time_entries | id PK · custom fields → flattened columns |
| projects | everhour_projects | id PK · linked to everhour_time_entries |
| tasks | everhour_tasks | id PK · linked to everhour_time_entries |
| users | everhour_users | id PK · linked to everhour_time_entries |
FAQ
How does Datrise handle Everhour's custom fields in Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the Everhour to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
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