To build a data warehouse, you first need to identify your business objectives, such as revenue forecasting or customer segmentation, to guide your design. A common approach is the , which organizes data into three layers: Bronze (raw), Silver (cleaned), and Gold (analytical/star schema). The Story: Building the "North Star" Sales Warehouse 1. Designing the Blueprint (Data Modeling)
: dim_product , dim_customer , and dim_date provide context. 2. Laying the Foundation (SQL Table Creation) You start by defining these structures in your database. Building a Data Warehouse with Examples in SQL ...
-- Finding total sales by product category SELECT p.category, SUM(s.sale_amount) AS total_revenue FROM fact_sales s JOIN dim_product p ON s.product_key = p.product_key GROUP BY p.category; Use code with caution. Copied to clipboard To build a data warehouse, you first need
: Stores metrics like price, quantity, and foreign keys. Designing the Blueprint (Data Modeling) : dim_product ,
To build a data warehouse, you first need to identify your business objectives, such as revenue forecasting or customer segmentation, to guide your design. A common approach is the , which organizes data into three layers: Bronze (raw), Silver (cleaned), and Gold (analytical/star schema). The Story: Building the "North Star" Sales Warehouse 1. Designing the Blueprint (Data Modeling)
: dim_product , dim_customer , and dim_date provide context. 2. Laying the Foundation (SQL Table Creation) You start by defining these structures in your database.
-- Finding total sales by product category SELECT p.category, SUM(s.sale_amount) AS total_revenue FROM fact_sales s JOIN dim_product p ON s.product_key = p.product_key GROUP BY p.category; Use code with caution. Copied to clipboard
: Stores metrics like price, quantity, and foreign keys.