Generating a large, customized dataset for practice in Power BI, SQL, or Python doesn’t have to involve writing complex scripts or hunting for the perfect file online. While many analysts rely on intricate SQL queries or long Python blocks to randomize data, Microsoft Excel offers a far more intuitive and flexible alternative. By using simple sequential numbering for Transaction IDs and basic addition for dates, you can quickly build the foundation of a dataset. To make the data realistic, you can add decimal increments to dates to simulate multiple transactions per day, or use the RANDBETWEEN function to vary the volume of daily sales, ensuring the dataset doesn’t look artificially uniform.
To populate more complex fields like regions, cities, or product categories, you can leverage AI tools like Google Gemini to generate structured tables which are then imported into Excel. Using the XLOOKUP function in combination with RANDBETWEEN, you can dynamically map these external lists to your main table, creating a rich web of related data including prices, categories, and subcategories. Once the categorical data is set, calculating numeric fields like quantity, discount, and total sales becomes a simple matter of dragging formulas down across thousands of rows. This method provides total control over the data distribution and allows you to generate millions of records tailored specifically to your project’s needs.
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