Tempo to DuckDB Integration
AI-first ETL from Tempo into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Tempo into DuckDB
Datrise syncs Tempo's worklogs, accounts, projects, users, teams, and billable hours 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
Tempo: Time tracking and worklogs layered on Jira.
DuckDB: In-process analytics database for fast local OLAP.
How Tempo entities map to DuckDB
| Tempo entity | DuckDB object | Notes |
|---|---|---|
| worklogs | tempo_worklogs | id PK · custom fields → JSON or STRUCT columns |
| accounts | tempo_accounts | id PK · linked to tempo_worklogs |
| projects | tempo_projects | id PK · linked to tempo_worklogs |
| users | tempo_users | id PK · linked to tempo_worklogs |
FAQ
How does Datrise handle Tempo'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 Tempo 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
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Early access
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