#SQL, #PowerQuery, or #MicrosoftFabric

[Olufemi7]

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

1. Upload source Excel or CSV files to OneLake
2. Open Power Query Editor inside Fabric
3. Clean each table:

• Rename columns
• Adjust data types

4. Merge Sales → Product on ProductKey 

#MicrosoftFabric #SQL #PowerQuery #Dataflows #Warehouse #StarSchema #ReportingTips