Everhour to Amazon Redshift Integration
AI-first ETL from Everhour into Amazon Redshift. Governed entities, incremental sync, typed landing tables.
How Datrise loads Everhour into Amazon Redshift
Datrise syncs Everhour's time entries, projects, tasks, users, clients, and budgets into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.
Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.
Ideal for AWS-native warehouses already using the Redshift ecosystem.
Endpoints
Everhour: Time tracking and budgets for project teams.
Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.
How Everhour entities map to Amazon Redshift
| Everhour entity | Amazon Redshift object | Notes |
|---|---|---|
| time entries | everhour_time_entries | id PK · custom fields → SUPER columns |
| projects | everhour_projects | id PK · linked to everhour_time_entries |
| tasks | everhour_tasks | id PK · linked to everhour_time_entries |
| users | everhour_users | id PK · linked to everhour_time_entries |
FAQ
How does Datrise handle Everhour's custom fields in Amazon Redshift?
Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.
How does the Everhour to Amazon Redshift sync stay up to date?
It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.
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Early access
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