DatriseAI-first ETL

Everhour to ClickHouse Integration

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

How Datrise loads Everhour into ClickHouse

Datrise syncs Everhour's time entries, projects, tasks, users, clients, and budgets 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

Everhour: Time tracking and budgets for project teams.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Everhour entities map to ClickHouse

Everhour entityClickHouse objectNotes
time entrieseverhour_time_entriesid PK · custom fields → JSON or Map columns
projectseverhour_projectsid PK · linked to everhour_time_entries
taskseverhour_tasksid PK · linked to everhour_time_entries
userseverhour_usersid PK · linked to everhour_time_entries

FAQ

How does Datrise handle Everhour'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 Everhour 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.

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