DEV Community: Francis Jaleny

A Guide to Data Modeling in Power BI

Francis Jaleny — Mon, 02 Feb 2026 20:34:24 +0000

Data visualization is the pretty part of Power BI, but Data Modeling is the engine under the hood. If your model is messy, your reports will be slow, your DAX will be overly complex, and your numbers might even be wrong.
In this article, we’ll break down the core concepts of schemas and modeling that separate the amateurs from the pros.
from the pros.
The Building Blocks: Fact vs. Dimension Tables
Before you can build a schema, you need to categorize your data into two buckets:
Fact Tables the What Happened
These contain quantitative information the observations or events you want to measure.
Examples: Sales transactions, temperature readings, logins.
Characteristics: Usually very long millions of rows and contains foreign keys to link to dimensions.
Dimension Tables the Who, Where, When
These provide the context for your facts.
Examples: Product details, Date tables, Employee info, Customer locations.
Characteristics: Usually, wide many columns describing an attribute and contains unique keys.
Pro Tip: If you find yourself putting descriptive text like Customer Name inside a sales table, you're likely missing a dimension table!
Choosing Your Architecture: Star vs. Snowflake
How you arrange these tables determines your Schema.
The Star Schema the Gold Standard
In a Star Schema, every Fact table is surrounded by its Dimension tables. Each dimension is directly related to the fact table.
Why it wins: It’s the simplest for Power BI to process. It results in faster performance and simpler DAX calculations. The Snowflake Schema
A Snowflake schema is essentially a Star schema where dimensions are further normalized into sub-dimensions. For example, a Product table might link to a Category table, which links to a department table.
When to use: Use it sparingly. While it saves storage space, it makes your model more complex and can slightly degrade performance because Power BI has to jump through more relationships to filter data.
Relationships: The Glue of Your Model
In Power BI, relationships define how data flows. There are three things to watch:

Cardinality: Most relationships should be One-to-Many. One customer has many orders. Avoid Many-to-Many relationships whenever possible as they lead to ambiguity.
Cross-filter Direction: Keep this to Single by default. Setting it to Both can create circular dependencies and slow down your report significantly.
The Date Table: Never rely on Power BI's Auto Date/Time. Always create a dedicated Calendar Dimension. It ensures your Time Intelligence functions like Year-over-Year growth work correctly.
Why Good Modeling is Critical
You might be tempted to just throw one giant flat table like an Excel sheet into Power BI. Don't. Here is why modeling matters:
Performance: Power BI’s engine is optimized for Star Schemas. It compresses data much more efficiently when it's structured this way.
Accuracy: Poor modeling leads to double counting or incorrect aggregations when you start filtering across different categories.
Usability: A well-modeled tool is intuitive for the end-user. When dimensions are clearly separated, users can easily drag and drop fields to find insights without needing a degree in data science.

My Excel & Power Query Journey

Francis Jaleny — Sun, 25 Jan 2026 18:54:09 +0000

**Excel and Power Query.

The Challenge**
The dataset had over 100+ rows of orders across regions, with issues like:
• Negative unit prices
• Required dates earlier than order dates
• Inconsistent discount percentages
My goal was to clean, classify, and model scenarios to make the data useful for decision-making.
Data Cleaning
• Fixed anomalies in Unit Price and Required Date.
• Standardized discount percentages.
Scenario Modeling
• Built columns for Adjusted Discount, Inflated Unit Cost, Uplifted Quantity.
• Compared baseline vs scenario totals.
Price Band Classification
• Used and to group orders.
• Validated thresholds with documentation for reproducibility.
Key Insights
• Scenario modeling showed how small changes in discount rates can significantly impact profit.
• Quartile-based classification revealed which products consistently fall into premium vs budget bands.
• Cleaned data is now ready for dashboard visualization in Excel or Power BI.
Final Thoughts
This project reinforced the importance of methodical, stepwise analysis. Cleaning data isn’t glamorous, but it’s the foundation of reliable insights.
If you’re learning Excel or Power Query, I highly recommend practicing with real-world messy datasets it’s the best way to build confidence.

Git & GitHub: A Beginner's Guide to Version Control

Francis Jaleny — Sun, 18 Jan 2026 07:45:22 +0000

What is Git and why does it matter?

Git is a "Version Control System." Think of it as a time machine for your code. If you make a mistake, you can go back to a version that worked perfectly.

How to track changes

Initialize your project: git init
Stage your files: git add .
Save your progress: git commit -m "Describe your change"

Pushing to GitHub (The Cloud)

Once you've saved locally, send it to the world:
git push origin main

Pulling from GitHub

If you're working on a team and someone else updated the code:
git pull origin main