DatriseAI-first ETL

Trustpilot to Google BigQuery Integration

AI-first ETL from Trustpilot into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Trustpilot into Google BigQuery

Datrise syncs Trustpilot's reviews, business units, consumers, star ratings, replies, and invitations 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

Trustpilot: Consumer reviews and business reputation data.

Google BigQuery: Serverless analytics warehouse on GCP.

How Trustpilot entities map to Google BigQuery

Trustpilot entityGoogle BigQuery objectNotes
reviewstrustpilot_reviewsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
business unitstrustpilot_business_unitsid PK · linked to trustpilot_reviews
consumerstrustpilot_consumersid PK · linked to trustpilot_reviews
star ratingstrustpilot_star_ratingsid PK · linked to trustpilot_reviews

FAQ

How does Datrise handle Trustpilot'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 Trustpilot 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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