DatriseAI-first ETL

Amazon Seller Partner Google BigQuery

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

How Datrise loads Amazon Seller Partner into Google BigQuery

Datrise syncs Amazon Seller Partner's records, events, and configuration objects 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

Amazon Seller Partner: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Amazon Seller Partner entities map to Google BigQuery

Amazon Seller Partner entityGoogle BigQuery objectNotes
recordsamazon_seller_partner_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsamazon_seller_partner_eventsTIMESTAMP events
configuration objectsamazon_seller_partner_configuration_objectsid PK · linked to amazon_seller_partner_records

FAQ

How does Datrise handle Amazon Seller Partner'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 Amazon Seller Partner 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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