DatriseAI-first ETL

My Hours to CSV Files Integration

AI-first ETL from My Hours into CSV Files. Governed entities, incremental sync, typed landing tables.

How Datrise loads My Hours into CSV Files

Datrise syncs My Hours's time logs, projects, tasks, clients, team members, and billable rates into CSV Files as one CSV per source entity. Flexible or custom fields land in JSON-encoded strings for nested fields, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes a fresh, fully-typed CSV per entity each run, so re-runs update only what changed. Optional date-suffixed files for change tracking. CSV has no types, so Datrise emits a companion schema and quotes/escapes consistently so downstream loaders don't misparse commas and newlines.

Ideal for portable hand-off into any tool that ingests delimited files.

Endpoints

My Hours: Time tracking and billable hours for projects.

CSV Files: Flat-file destination for exports and lightweight data sharing.

How My Hours entities map to CSV Files

My Hours entityCSV Files objectNotes
time logsmy_hours_time_logsid PK · custom fields → JSON-encoded strings for nested fields
projectsmy_hours_projectsid PK · linked to my_hours_time_logs
tasksmy_hours_tasksid PK · linked to my_hours_time_logs
clientsmy_hours_clientsid PK · linked to my_hours_time_logs

FAQ

How does Datrise handle My Hours's custom fields in CSV Files?

Flexible values are stored as JSON-encoded strings for nested fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native CSV Files types.

How does the My Hours to CSV Files sync stay up to date?

It runs incrementally — Datrise uses writes a fresh, fully-typed CSV per entity each run.

Related pipelines

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