Timely to Mode Integration
AI-first ETL from Timely into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Timely into Mode
Datrise syncs Timely's time entries, projects, clients, users, tags, and billable hours into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Timely: Automatic time tracking for teams and projects.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Timely entities map to Mode
| Timely entity | Mode object | Notes |
|---|---|---|
| time entries | timely_time_entries | id PK · custom fields → flattened columns for SQL and notebooks |
| projects | timely_projects | id PK · linked to timely_time_entries |
| clients | timely_clients | id PK · linked to timely_time_entries |
| users | timely_users | id PK · linked to timely_time_entries |
FAQ
How does Datrise handle Timely's custom fields in Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Timely to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Timely
Early access
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