Everhour to GoodData Integration
AI-first ETL from Everhour into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads Everhour into GoodData
Datrise syncs Everhour's time entries, projects, tasks, users, clients, and budgets into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.
Ideal for embedded, multi-tenant analytics.
Endpoints
Everhour: Time tracking and budgets for project teams.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How Everhour entities map to GoodData
| Everhour entity | GoodData object | Notes |
|---|---|---|
| time entries | everhour_time_entries | id PK · custom fields → flattened 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 GoodData?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native GoodData types.
How does the Everhour to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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