DatriseAI-first ETL

My Hours to ClickHouse Integration

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

How Datrise loads My Hours into ClickHouse

Datrise syncs My Hours's time logs, projects, tasks, clients, team members, and billable rates into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.

Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.

Ideal for high-volume event analytics that need sub-second aggregation.

Endpoints

My Hours: Time tracking and billable hours for projects.

ClickHouse: Columnar OLAP engine for fast aggregations.

How My Hours entities map to ClickHouse

My Hours entityClickHouse objectNotes
time logsmy_hours_time_logsid PK · custom fields → JSON or Map columns
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 ClickHouse?

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

How does the My Hours to ClickHouse sync stay up to date?

It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.

Related pipelines

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