ecommerce-data-warehouse

Centralize Shopify and ad spend data for a data warehouse by using managed connectors like Fivetran or Airbyte to ingest raw records, then apply dbt transformations to build staging layers and star-schema analytics marts.

Model ecommerce data using dbt and a star schema by structuring your project into staging, intermediate, and marts layers, applying SCD Type 2 patterns for historical tracking, and configuring incremental models for large datasets.

You can use BigQuery, Snowflake, or Redshift for your ecommerce analytics warehouse. The setup includes deployment guidance, installing the appropriate dbt adapter, and configuring connectivity to BI tools like Looker Studio, Metabase, or Tableau.

Calculate ROAS and LTV by combining orders and ad spend data within your dbt marts layer. This integrates Shopify transactions with Meta and Google Ads spend to compute channel-level attribution and build cohort models for marketing optimization.

The dbt project layering includes data quality tests for incremental ETL strategies. It provides specific patterns for staging, intermediate models, and marts to ensure reliable cross-source attribution and executive dashboards without overloading operational systems.