DatriseAI-first ETL

Tempo to PostgreSQL Integration

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

How Datrise loads Tempo into PostgreSQL

Datrise syncs Tempo's worklogs, accounts, projects, users, teams, and billable hours 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

Tempo: Time tracking and worklogs layered on Jira.

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

How Tempo entities map to PostgreSQL

Tempo entityPostgreSQL objectNotes
worklogstempo_worklogsid PK · custom fields → jsonb columns
accountstempo_accountsid PK · linked to tempo_worklogs
projectstempo_projectsid PK · linked to tempo_worklogs
userstempo_usersid PK · linked to tempo_worklogs

FAQ

How does Datrise handle Tempo'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 Tempo 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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