Clockify to Looker Studio Integration
AI-first ETL from Clockify into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clockify into Looker Studio
Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Clockify: Time tracking across projects and teams.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Clockify entities map to Looker Studio
| Clockify entity | Looker Studio object | Notes |
|---|---|---|
| time entries | clockify_time_entries | id PK · custom fields → flattened columns for chart 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 Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Clockify to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
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