DatriseAI-first ETL

Clockify to PostgreSQL Integration

AI-first ETL from Clockify into PostgreSQL. Governed entities, incremental sync, typed landing tables.

How Datrise loads Clockify into PostgreSQL

Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

Clockify: Time tracking across projects and teams.

PostgreSQL: Open-source relational database with strong SQL and extensions.

How Clockify entities map to PostgreSQL

Clockify entityPostgreSQL objectNotes
time entriesclockify_time_entriesid PK · custom fields → jsonb columns
projectsclockify_projectsid PK · linked to clockify_time_entries
tasksclockify_tasksid PK · linked to clockify_time_entries
usersclockify_usersid PK · linked to clockify_time_entries

FAQ

How does Datrise handle Clockify's custom fields in PostgreSQL?

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

How does the Clockify to PostgreSQL sync stay up to date?

It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

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