DatriseAI-first ETL

Shopify Google BigQuery

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

How Datrise loads Shopify into Google BigQuery

Datrise syncs Shopify's orders, products, customers, inventory levels, and fulfillment events 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

Shopify: E-commerce platform for orders, catalog, and customer data.

Google BigQuery: Serverless analytics warehouse on GCP.

How Shopify entities map to Google BigQuery

Shopify entityGoogle BigQuery objectNotes
ordersshopify_ordersid PK · custom fields → JSON or nested/repeated (STRUCT) columns
productsshopify_productsid PK · linked to shopify_orders
customersshopify_customersid PK · linked to shopify_orders
inventory levelsshopify_inventory_levelsid PK · linked to shopify_orders

FAQ

How does Datrise handle Shopify'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 Shopify 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

Connect Shopify to Google BigQuery the easy way

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