Clockify to DuckDB Integration

How Datrise loads Clockify into DuckDB

Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Clockify: Time tracking across projects and teams.

DuckDB: In-process analytics database for fast local OLAP.

How Clockify entities map to DuckDB

Clockify entity	DuckDB object	Notes
time entries	`clockify_time_entries`	id PK · custom fields → JSON or STRUCT columns
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 DuckDB?

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

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

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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