Vitally to Google BigQuery Integration

How Datrise loads Vitally into Google BigQuery

Datrise syncs Vitally's accounts, users, health scores, tasks, playbooks, and product usage 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

Vitally: Customer success health and account analytics.

Google BigQuery: Serverless analytics warehouse on GCP.

How Vitally entities map to Google BigQuery

Vitally entity	Google BigQuery object	Notes
accounts	`vitally_accounts`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
users	`vitally_users`	id PK · linked to vitally_accounts
health scores	`vitally_health_scores`	id PK · linked to vitally_accounts
tasks	`vitally_tasks`	id PK · linked to vitally_accounts

FAQ

How does Datrise handle Vitally'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 Vitally 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.

Related pipelines

Early access

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