Clockify to Domo Integration
AI-first ETL from Clockify into Domo. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clockify into Domo
Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags into Domo as datasets in Domo's cloud store via connector. Flexible or custom fields land in flattened columns for Magic ETL, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses partitioned dataset updates rather than full replaces, so re-runs update only what changed. Domo dataset partitions keyed on load date. Domo stores its own copy of data, so Datrise sends incremental partitions to avoid re-uploading whole datasets.
Ideal for all-in-one cloud BI with built-in ETL.
Endpoints
Clockify: Time tracking across projects and teams.
Domo: Cloud BI platform combining data integration and executive dashboards.
How Clockify entities map to Domo
| Clockify entity | Domo object | Notes |
|---|---|---|
| time entries | clockify_time_entries | id PK · custom fields → flattened columns for Magic ETL |
| 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 Domo?
Flexible values are stored as flattened columns for Magic ETL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Domo types.
How does the Clockify to Domo sync stay up to date?
It runs incrementally — Datrise uses partitioned dataset updates rather than full replaces.
Related pipelines
More destinations for Clockify
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