Clockify to Tableau Integration
AI-first ETL from Clockify into Tableau. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clockify into Tableau
Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.
Ideal for visual analytics and dashboards in Tableau.
Endpoints
Clockify: Time tracking across projects and teams.
Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.
How Clockify entities map to Tableau
| Clockify entity | Tableau object | Notes |
|---|---|---|
| time entries | clockify_time_entries | id PK · custom fields → flattened columns for Tableau fields |
| 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 Tableau?
Flexible values are stored as flattened columns for Tableau fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Tableau types.
How does the Clockify to Tableau sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.
Related pipelines
More destinations for Clockify
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