ShopifyQL Notebooks: Simplifying Querying with Commerce Data Models

Summary

ShopifyQL Notebooks gives merchants a self-serve way to investigate changing business metrics, understand their causes, and share findings as data stories. It combines the ShopifyQL query language with commerce-specific data models designed for users without SQL expertise, covering domains such as products, orders, and customers. The modeling process starts with business questions, translates them into columns and an appropriate grain, validates the design with mock data and queries, and evaluates upstream data, freshness, and performance. To support large merchants, the implementation uses incremental models and roll-ups, which can reduce build and query times while adding maintenance, pipeline, and monitoring complexity.

Takeaways

1. 1

   Fully additive metrics can be aggregated across all available dimensions; semi-additive metrics such as customers who placed an order can double-count entities, while non-additive metrics such as discount percentages should not be summed.

2. 2

   Incremental data models process only newly available differences, improving build times but making schema changes, pipeline dependencies, and monitoring more challenging than full-drop models.

3. 3

   Roll-ups reduce query work by storing older data at the finest required grain, such as daily sales by product title, so the engine scans fewer rows.