Everhour to Google BigQuery Integration
AI-first ETL from Everhour into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Everhour into Google BigQuery
Datrise syncs Everhour's time entries, projects, tasks, users, clients, and budgets into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.
Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.
Ideal for Google-stack analytics and ML on serverless infrastructure.
Endpoints
Everhour: Time tracking and budgets for project teams.
Google BigQuery: Serverless analytics warehouse on GCP.
How Everhour entities map to Google BigQuery
| Everhour entity | Google BigQuery object | Notes |
|---|---|---|
| time entries | everhour_time_entries | id PK · custom fields → JSON or nested/repeated (STRUCT) 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 Google BigQuery?
Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.
How does the Everhour to Google BigQuery sync stay up to date?
It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.
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Early access
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