DatriseAI-first ETL

Aftership to Google BigQuery Integration

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

How Datrise loads Aftership into Google BigQuery

Datrise syncs Aftership's shipments, trackings, checkpoints, couriers, delivery statuses, and notifications 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

Aftership: Shipment tracking and delivery notifications.

Google BigQuery: Serverless analytics warehouse on GCP.

How Aftership entities map to Google BigQuery

Aftership entityGoogle BigQuery objectNotes
shipmentsaftership_shipmentsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
trackingsaftership_trackingsid PK · linked to aftership_shipments
checkpointsaftership_checkpointsid PK · linked to aftership_shipments
couriersaftership_couriersid PK · linked to aftership_shipments

FAQ

How does Datrise handle Aftership'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 Aftership 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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