Share
Data God Guide to Learning DAX
Christopher Wagner • January 22, 2023
DAX is not just the name of my new puppy
DAX (Data Analysis Expressions) is a formula language used in Power BI to create custom calculations and aggregations unavailable in the standard user interface. It is similar to Excel formulas but is designed to work with relational data and perform dynamic aggregation.
Here is a guide to help you get started with learning DAX:
- Understand the basic structure of DAX formulas: DAX formulas begin with an equal sign (=) and are composed of functions, operators, and constants. The basic structure of a DAX formula is similar to that of a regular mathematical formula.
- Learn the essential DAX functions: Several are commonly used in Power BI, including SUM, COUNT, AVERAGE, MAX, and MIN. These functions can perform basic calculations and aggregations on your data.
- Understand the difference between calculated columns and calculated tables: In Power BI, you can create calculated columns and calculated tables. Calculated columns are added to an existing table, while calculated tables are made from existing tables. The critical difference between the two is that calculated columns are calculated at the row level, while calculated tables are calculated at the table level.
- Learn how to use filters in DAX formulas: DAX formulas can use filters to include or exclude specific rows of data from the calculation. This can be useful when you want to create measures based on particular data or conditions.
- Practice with sample data: The best way to learn DAX is to practice using it on a sample dataset. You can use the Power BI sample data on KratosBI.com.
- Understand the context of the calculation: DAX formulas work with the context of the calculation, which is the set of rows and columns on which the formula is currently working. Understanding the context of the measure is crucial for getting the correct results from your DAX formulas.
- Learn about time intelligence functions: Power BI has a set of time intelligence functions that make it easy to work with date and time data. These functions allow you to perform calculations such as year-to-date, quarter-to-date, and the same period as last year.
- Understand the difference between relationships and context: To get the most out of DAX formulas, you need to understand the difference between relationships and context. Relationships define how tables are related to each other, while context defines which table rows are used in a calculation.
- Learn about advanced DAX functions: Many advanced DAX functions can be used to perform more complex calculations. Some of these functions include RANKX, RANK.EQ, and TOPN.
- Keep learning and experimenting: DAX is a powerful language that can be used to perform a wide variety of calculations and aggregations. The more you know about DAX, the more you can do with it. Keep experimenting with different formulas and try new things to see what works best for your data and analysis.
Remember that practice is the key to mastering DAX, and keep experimenting with different formulas and try new things to see what works best for your data and analysis.
CHRIS WAGNER, MBA MVP
Analytics Architect, Mentor, Leader, and Visionary
Chris has been working in the Data and Analytics space for nearly 20 years. Chris has dedicated his professional career to making data and information accessible to the masses. A significant component in making data available is continually learning new things and teaching others from these experiences. To help people keep up with this ever-changing landscape, Chris frequently posts on LinkedIn and to this blog.
Compare Microsoft Fabric and Databricks, two leading data platforms. Highlights their features, strengths, and unique offerings across various domains like data engineering, data analytics, data science, DevOps, security, integration with other tools, cost management, and governance. Microsoft Fabric is noted for its low-code/no-code solutions and seamless integration with Microsoft tools, making it accessible for users with varying technical skills. Databricks is praised for its high-performance capabilities in big data processing and collaborative analytics, offering flexibility and control for experienced data teams.
In a dynamic data engineering scenario, Sam, a skilled professional, adeptly navigates urgent requests using Microsoft Fabric. Collaborating with Data Steward Lisa and leveraging OneLake, Sam streamlines data processes, creating a powerful collaboration between engineering and stewardship. With precision in Azure Data Factory and collaboration with a Data Scientist, Sam crafts a robust schema, leading to a visually appealing Power BI report.
NOTE: This is the first draft of this document that was assembled yesterday as a solo effort. If you would like to contribute or have any suggestions, check out my first public GIT repository - KratosDataGod/LakehouseToPowerBI: Architectural design for incorporating a Data Lakehouse architecture with an Enterprise Power BI Deployment (github.com) This article is NOT published, reviewed, or approved by ANYONE at Microsoft. This content is my own and is what I recommend for architecture and build patterns.