Toggl to DuckDB Integration
AI-first ETL from Toggl into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Toggl into DuckDB
Datrise syncs Toggl's time entries, projects, clients, workspaces, tags, and users 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
Toggl: Time tracking with projects, clients, and reports.
DuckDB: In-process analytics database for fast local OLAP.
How Toggl entities map to DuckDB
| Toggl entity | DuckDB object | Notes |
|---|---|---|
| time entries | toggl_time_entries | id PK · custom fields → JSON or STRUCT columns |
| projects | toggl_projects | id PK · linked to toggl_time_entries |
| clients | toggl_clients | id PK · linked to toggl_time_entries |
| workspaces | toggl_workspaces | id PK · linked to toggl_time_entries |
FAQ
How does Datrise handle Toggl'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 Toggl 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
More destinations for Toggl
Early access
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