DatriseAI-first ETL

Timely to Redash Integration

AI-first ETL from Timely into Redash. Governed entities, incremental sync, typed landing tables.

How Datrise loads Timely into Redash

Datrise syncs Timely's time entries, projects, clients, users, tags, and billable hours into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, 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 for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.

Ideal for lightweight, query-driven dashboards.

Endpoints

Timely: Automatic time tracking for teams and projects.

Redash: Open-source SQL client for queries, visualizations, and dashboards.

How Timely entities map to Redash

Timely entityRedash objectNotes
time entriestimely_time_entriesid PK · custom fields → flattened columns for query results
projectstimely_projectsid PK · linked to timely_time_entries
clientstimely_clientsid PK · linked to timely_time_entries
userstimely_usersid PK · linked to timely_time_entries

FAQ

How does Datrise handle Timely's custom fields in Redash?

Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.

How does the Timely to Redash sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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