DatriseAI-first ETL

Shopify Amazon DynamoDB

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

How Datrise loads Shopify into Amazon DynamoDB

Datrise syncs Shopify's orders, products, customers, inventory levels, and fulfillment events into Amazon DynamoDB as an item per source record in a table per entity. Flexible or custom fields land in nested map/list attributes, and timestamps such as created, updated, and status changes are typed as ISO-8601 string or epoch number attributes.

Sync is incremental: Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id, so re-runs update only what changed. Partition-key design on the entity id to spread throughput evenly. DynamoDB rewards access-pattern-first key design, so Datrise sets partition/sort keys from your entity ids rather than scan-heavy defaults.

Ideal for serverless apps needing single-digit-millisecond key lookups on CRM data.

Endpoints

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

Amazon DynamoDB: Serverless key-value and document store on AWS.

How Shopify entities map to Amazon DynamoDB

Shopify entityAmazon DynamoDB objectNotes
ordersshopify_ordersid PK · custom fields → nested map/list attributes
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 Amazon DynamoDB?

Flexible values are stored as nested map/list attributes, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon DynamoDB types.

How does the Shopify to Amazon DynamoDB sync stay up to date?

It runs incrementally — Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id.

Related pipelines

Early access

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