Clockify to Birst Integration
AI-first ETL from Clockify into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clockify into Birst
Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags 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
Clockify: Time tracking across projects and teams.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How Clockify entities map to Birst
| Clockify entity | Birst object | Notes |
|---|---|---|
| time entries | clockify_time_entries | id PK · custom fields → flattened columns |
| projects | clockify_projects | id PK · linked to clockify_time_entries |
| tasks | clockify_tasks | id PK · linked to clockify_time_entries |
| users | clockify_users | id PK · linked to clockify_time_entries |
FAQ
How does Datrise handle Clockify'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 Clockify to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
More destinations for Clockify
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