DatriseAI-first ETL

Shopify ClickHouse

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

How Datrise loads Shopify into ClickHouse

Datrise syncs Shopify's orders, products, customers, inventory levels, and fulfillment events into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.

Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.

Ideal for high-volume event analytics that need sub-second aggregation.

Endpoints

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

ClickHouse: Columnar OLAP engine for fast aggregations.

How Shopify entities map to ClickHouse

Shopify entityClickHouse objectNotes
ordersshopify_ordersid PK · custom fields → JSON or Map 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 ClickHouse?

Flexible values are stored as JSON or Map columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ClickHouse types.

How does the Shopify to ClickHouse sync stay up to date?

It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.

Related pipelines

Early access

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