Clockify to Chartio Integration
AI-first ETL from Clockify into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clockify into Chartio
Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, 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. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
Clockify: Time tracking across projects and teams.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How Clockify entities map to Chartio
| Clockify entity | Chartio object | Notes |
|---|---|---|
| time entries | clockify_time_entries | id PK · custom fields → flattened columns for visual SQL |
| 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 Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the Clockify to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Clockify
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