I just published a hands-on walkthrough of solving a real SQL reporting challenge using Microsoft Fabric. Read the full article on Medium In this post, I cover: Designing a star schema with Sales, Product, and Date tables Writing SQL joins for clean reporting Replicating the same logic visually using Power Query in a Dataflow Loading the final result into a Fabric Warehouse for scalable reporting -- Create the Sales table
CREATE TABLE Sales (
DateKey INT,
ProductKey INT,
SalesAmount INT
);
-- Insert sample records into Sales
INSERT INTO Sales (DateKey, ProductKey, SalesAmount)
VALUES
(20231201, 101, 500),
(20231201, 102, 300),
(20231202, 101, 400);
-- Create the Product table
CREATE TABLE Product (
ProductKey INT,
ProductName VARCHAR(100)
);
-- Insert into Product
INSERT INTO Product (ProductKey, ProductName)
VALUES
(101, 'Phone'),
(102, 'Laptop');
-- Create the Date table
CREATE TABLE Date (
DateKey INT,
Day INT,
Month INT,
Year INT
);
-- Insert into Date
INSERT INTO Date (DateKey, Day, Month, Year)
VALUES
(20231201, 1, 12, 2023),
(20231202, 2, 12, 2023); Power Query Solution — No-Code Approach in Fabric Here’s how I replicated the same logic visually using Power Query in a Dataflow: 🪜 Steps Upload source Excel or CSV files to OneLake Open Power Query Editor inside Fabric Clean each table: Rename columns Adjust data types 4. Merge Sales → Product on ProductKey #MicrosoftFabric #SQL #PowerQuery #Dataflows #Warehouse #StarSchema #ReportingTips
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