moves data from raw sources (like CSVs or ERP systems) into your warehouse. Extract : Pulling raw data into the Bronze Layer .
-- 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 Building a Data Warehouse with Examples in SQL ...
: Cleaning data in the Silver Layer , such as standardizing "Yes/No" strings to booleans. Load : Inserting into the final Gold Layer tables. moves data from raw sources (like CSVs or
: 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. Copied to clipboard : Cleaning data in the
A data warehouse typically uses a , consisting of a central Fact Table (quantitative data like sales) surrounded by Dimension Tables (descriptive data like products or dates).
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)