Clockify to Yellowfin Integration
AI-first ETL from Clockify into Yellowfin. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clockify into Yellowfin
Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags into Yellowfin as warehouse tables Yellowfin builds views on. 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 connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.
Ideal for dashboards with automated data storytelling.
Endpoints
Clockify: Time tracking across projects and teams.
Yellowfin: BI suite with dashboards, automated insights, and data storytelling.
How Clockify entities map to Yellowfin
| Clockify entity | Yellowfin 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 Yellowfin?
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 Yellowfin types.
How does the Clockify to Yellowfin sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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