Impact to Google BigQuery Integration

How Datrise loads Impact into Google BigQuery

Datrise syncs Impact's partners, actions, clicks, conversions, payouts, and campaigns 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

Impact: Partnership and affiliate performance tracking.

Google BigQuery: Serverless analytics warehouse on GCP.

How Impact entities map to Google BigQuery

Impact entity	Google BigQuery object	Notes
partners	`impact_partners`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
actions	`impact_actions`	id PK · linked to impact_partners
clicks	`impact_clicks`	id PK · linked to impact_partners
conversions	`impact_conversions`	id PK · linked to impact_partners

FAQ

How does Datrise handle Impact'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 Impact 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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