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Introduction to Power BI Data Modeling
Build a model from raw data using Power Query, create dimensions, merge and load data, build relationships and a date table, choose schemas, handle bidirectional and 1:1 cases, and follow best practices.
In Introduction to Power BI Data Modeling, Greg Trzeciak teaches you how to build clean, reliable models that make reporting easier and DAX more predictable. You’ll learn the core concepts behind good modeling, fact and dimension tables, model types, relationships, and schemas, plus the practical decisions that prevent common reporting problems like confusing filters, inconsistent totals, and slow performance. This course is designed to give you a strong foundation that applies to nearly every Power BI project, from simple dashboards to enterprise semantic models.
You’ll follow a hands-on modeling workflow from raw data to a working model. Greg covers how Power Query supports data shaping, how to build and refine dimensions, how to merge data when needed, and how to load the results back into Power BI. Then you’ll focus on relationships and structure, creating a date table, choosing the right schema, understanding bidirectional filtering, and handling special cases like 1-to-1 relationships. By the end, you’ll have a repeatable modeling process and best practices you can apply to build fast, trustworthy Power BI solutions.
Course Outline ( Free Preview)
Module 01 - Introduction
Greg introduces what data modeling means in Power BI and why it directly impacts report accuracy, performance, and maintainability. You’ll preview the modeling workflow you’ll follow in this course, raw data to shaped tables to relationships, and understand what “good” modeling looks like before you start building.
Module 02 - Things to Consider
Before you create tables and relationships, you need to make a few key decisions. Greg covers the most important modeling considerations, business questions, grain, data quality, refresh requirements, and usability, so your model supports real reporting needs instead of just mirroring the source system.
Module 03 - Fact and Dimension Tables14 min.
Learn the foundation of strong models, facts and dimensions. Greg explains what belongs in each table, why the separation matters, and how it makes filtering and slicing more intuitive. You’ll also learn how this structure simplifies measures and reduces common modeling confusion.
Module 04 - Model Types7 min.
This module introduces common model patterns you’ll see in Power BI. Greg breaks down the basic types at a high level and explains how to recognize which one you’re building based on the shape of your data and reporting goals.
Module 05 - Model Types (Part 2)8 min.
Continue exploring model types with additional patterns and real-world tradeoffs. Greg discusses why certain patterns create problems in reporting and how choosing the right model type early can prevent performance issues and confusing filter behavior later.
Module 06 - Model Types (Part 3)10 min.
Finish the model types discussion with more advanced or edge-case scenarios. Greg helps you understand when models drift away from clean star schema patterns and how to keep structure and clarity even when your source data is messy or constrained.
Module 07 - Raw Data4 min.
Start working with the raw input and learn how to evaluate it before modeling. Greg shows what to look for in source data, duplicates, missing keys, inconsistent values, and how raw structure influences the transformations you’ll need in Power Query.
Module 08 - Power Query in Power BI3 min.
Learn how Power Query supports modeling by shaping your data before it enters the model. Greg walks through practical transformation habits, cleaning, renaming, removing, and standardizing, so your model begins with tables that are consistent and ready for relationships.
Module 09 - Creating Our First Data Model13 min.
Now you’ll begin assembling your first model. Greg shows how to move from shaped queries into a structured dataset and explains what to validate early so you don’t build relationships on unstable or inconsistent fields.
Module 10 - Building Dimensions9 min.
Now you’ll begin assembling your first model. Greg shows how to move from shaped queries into a structured dataset and explains what to validate early so you don’t build relationships on unstable or inconsistent fields.
Module 11 - Dimension Tables (Part 2)4 min.
Continue refining dimension tables with additional design patterns and cleanup. Greg focuses on improving clarity, consistency, and maintainability, including how to handle messy attributes and avoid dimensions that confuse users or break filtering.
Module 12 - Merging13 min.
Learn how to merge tables in Power Query when you need to enrich a dataset with additional fields. Greg explains merge types, matching keys, and validation tips so joins don’t silently drop rows or create duplicates that later distort measures.
Module 13 - Load Back Into Power BI14 min.
Move your transformed tables into the model and confirm they’re ready for relationship design. Greg covers load settings, what should and shouldn’t be loaded, and quick checks to ensure your tables are clean before you begin connecting them.
Module 14 - Building Relationships5 min.
Relationships are where models succeed or fail. Greg walks through creating relationships correctly, choosing the right keys, cardinality, and filter direction. You’ll learn how to avoid ambiguous relationships and ensure visuals filter the way users expect.
Module 15 - Creating a Date Table12 min.
A dedicated date table is essential for time-based reporting. Greg shows why date tables matter, what columns to include, and how they support time intelligence, consistent filtering, and cleaner reporting. You’ll build one that’s reusable across models.
Module 16 - Schemas6 min.
Learn how schema design affects usability and performance. Greg explains common schema patterns, why star schemas are favored, and how to recognize when your model is drifting into snowflake or overly complex territory that makes reporting harder.
Module 17 - Bidirectional Filtering6 min.
Bidirectional filtering can be helpful, but it can also create confusion and performance issues. Greg explains when to use it, when to avoid it, and how to test for unintended filter propagation so your model stays predictable and efficient.
Module 18 - 1 to 1 Relationships2 min.
Understand what 1-to-1 relationships mean and why they’re often a modeling warning sign. Greg explains when they’re valid, how they affect filtering, and how to rethink table structure when a 1-to-1 relationship suggests a table should be combined or redesigned.
Module 19 - Data Model Tips4 min.
Greg shares practical modeling tips that improve clarity, reliability, and performance. You’ll learn small choices that make a big difference, naming conventions, reducing columns, key design, and validation habits that prevent common report problems.
Module 20 - Best Practices9 min.
Wrap up with a clear best-practice checklist you can reuse on every model. Greg summarizes the most important modeling rules, how to keep models maintainable as they grow, and the next steps you should take to keep improving your Power BI modeling skills.
Gregory Trzeciak has his master’s degree in Education from the University of Florida. He has 9 years of teaching experience in high school, college level, and summer programs where he was recognized as a top educator and leader in interactive education. As a trainer at Pragmatic Works, his primary goal is to help individuals gain confidence in using Power BI and the Power Platform. While not in the office, he enjoys fantasy football, walking his dog, and running half-marathons!
