DatriseAI-first ETL

Linnworks to Google BigQuery Integration

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

How Datrise loads Linnworks into Google BigQuery

Datrise syncs Linnworks's orders, stock items, channels, shipments, suppliers, and inventory levels 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

Linnworks: Multichannel order and inventory management.

Google BigQuery: Serverless analytics warehouse on GCP.

How Linnworks entities map to Google BigQuery

Linnworks entityGoogle BigQuery objectNotes
orderslinnworks_ordersid PK · custom fields → JSON or nested/repeated (STRUCT) columns
stock itemslinnworks_stock_itemsid PK · linked to linnworks_orders
channelslinnworks_channelsid PK · linked to linnworks_orders
shipmentslinnworks_shipmentsid PK · linked to linnworks_orders

FAQ

How does Datrise handle Linnworks'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 Linnworks 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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