What are the best DAX formulas for Power BI?

The best DAX formulas are readable, include comments and have high performance. Once performance start to become an issue, take a good look at the number of iterations in your code. Are you able to store some logic in a table, and reference it instead of computing it dynamically? May be a way to go. Also put filters in your calculate statement on columns, not on tables. The most important DAX functions: - MAX - SUMX - FILTER - VALUES - SWITCH - RELATED - CALCULATE - SUMMARIZE - HASONEVALUE - ADDCOLUMNS - SELECTEDVALUE

When you get data into PowerBI and load the data. You may have the need to create some some data summaries (metrics) - Measures, or extend your data tables with new columns (calculated columns), or even create new data tables (calculated tables). That's where DAX comes in. A formula language with a lot functions similar to those obtainable in Excel.

To learn Dax we need to learn a few things first then DAX will be very easy. 1. difference between calculated column, calculated table, and measures 2. difference between row context and filter context 3. context transition 4. variables 5. time intelligence functions

DAX is a valuable tool for data professionals, analysts, and business users who need to work with and analyze data in a more structured and powerful way. It provides a robust framework for building data models, creating calculations, and generating insights, all of which are crucial for data-driven decision-making in today's business environment.

We can’t define what are the best DAX formulas for Power BI, importance of DAX formula can vary depending on your specific data and analysis needs. DAX is a formula language used in Power BI to create custom calculations and aggregations for your data. There are some most commonly used DAX formulas and functions in Power BI: SUMX: Calculates the sum of values AVERAGEX: Calculates the average of values COUNTROWS: Counts the number of rows in a table expression FILTER: Allows you to filter a table based on specified conditions CALCULATE: Modifies the filter context of a calculation. ALL: Removes filters from a table or a column. RELATED: These functions are used to navigate relationships between tables and retrieve data. RANKX Assigns a rank

By using third party tools like DAX studio or Tabular Editor (2 or 3) you can write some DAX and save it to your model in addition to the standard formula bar. With Tabular Editor you can also create calculation groups (which is only possible from the October 2023 PowerBI Desktop uodate), and create Object level security as well within your model. Another good practice when writing DAX is to comment your code- this will help for future troubleshooting or optimisation of measures. (Commenting is a generally accepted good practice in all programming languages as well). Another good practice when writing DAX is the use of variables where possible, and the so-called "measure branching". ALWAYS define explicit measures via DAX as well.

While you can test DAX formulas inside DAX Studio (an external tool), you can't save those formulas into your Power BI Model. Tabular Editor (also an external tool) on the hand, allows a lot of Data Model feature modifications including saving DAX written in TE into Power BI. This also comes with a lot of abilities to improve productivity while writing DAX. Such as, using scripting DAX.

To write DAX formulas in Power BI, open Power BI Desktop, select a visual, and use the DAX Formula bar. After we give the right name for project we can start by typing a DAX formula with functions and column references. Create custom measures in the "Model" view. We need to test our formulas and integrate them into visualizations before saving and publishing our report.

Few points may help: * Use "var ... return" if necessary to repeat any part of a formula. * Architect measures beyond their DAX code - what I mean by that is look how it would leverage filtering while building visuals. * Learn CALCULATE() and filters - learn well... * Minimum filtering in formulas, maximum leverage of filtering by data structure and table relations. * Avoid using DAX to pre-process data, if may be done with Power Query M formulas. * Remember that BLANK() (null) is different than "0" (zero)! Zero is an information and null means lack of information. That may severely affect calculation, for instance of averages.

Writing DAX formulas in Power BI is a process that requires a clear understanding of the DAX language and how it interacts with data. Tips to create: 1- Understand Your Data Model 2- Open DAX Editor: In Power BI Desktop, right-click on the table to create a new measure or column, opening the formula bar. 3- Start with Simple Formulas: Begin with basic DAX syntax, like: Total Sales = SUM(Sales[SalesAmount]) This formula calculates the total sales by summing the 'SalesAmount' column in the 'Sales' table. Learn Contexts: Grasp the concept of row and filter contexts that influence calculations. Row context pertains to individual rows in a table, while filter context refers to the set of filters applied to calculations. .

By knowing a key DAX functions, you can accomplish the majority of your requirements. Using time-intelligence functions (DATEADD will be your best friend here) will allow you to do some serious time series analysis. Also know the difference between "normal" vs Iterator functions (usually ending in "X") i.e.: SUM vs SUMX. Also make use of websites such as: ?Dax.guide ?Dax.do ?Dax.patterns to learn about DAX functions and testing it out.

SUMMARIZECOLUMNS() function is your DAX bff. Why? You can join facts and dimensions in one function, and it's mostly used inside iterators like SUMX. There's no other DAX function with this kind of flexibility. How? It creates a new invisible table (called virtual table) that you can use inside a DAX measure or calculated column to aggregate. As opposed to VALUES() which only groups by one column, use SUMMARIZECOLUMNS() to group by many columns. Example: You want to calculate the weekly average number of orders by Type, Region, and Category. AVERAGEX( SUMMARIZECOLUMNS( WeekStart, Type, Region, Category ), [NumberOfOrders] )

Calculate: The 'Calculate' function is one of the most powerful as it allows the transition between the row and filter contexts.

The CALCULATE function is one you are likely to use the most. It's the function that provides you with the flexibility to manipulate the "chunk" of data (Filters) required for any calculation across the related tables in the data model.

Commonly used DAX functions in Power BI include SUM for basic summation, DISTINCTCOUNT for unique counts, AVERAGE for calculating averages, IF for conditional logic, COUNTROWS for row counts, and CALCULATE for modifying filter context. The choice of function depends on the specific insights you need from your data. Understanding your data model and the questions your Power BI reports aim to answer is crucial. Each function serves distinct purposes, allowing you to craft measures and calculated columns tailored to your analysis and reporting requirements. It's essential to select the most appropriate DAX functions to achieve your desired outcomes effectively.

The best practices for DAX are Use Measures Sparingly: Prefer measures over calculated columns for efficiency and flexibility. Optimize Performance: Keep DAX calculations simple and avoid unnecessary complexity to ensure fast query performance. Avoid Circular Dependencies: Be cautious to prevent circular references in your data model. Document Your Formulas: Provide clear comments to explain your DAX calculations for yourself and others. Test and Validate: Test your DAX formulas rigorously with sample data and real-world scenarios to ensure accuracy. Leverage Variables: Use variables to break down complex formulas into more manageable parts. Monitor Resource Usage: Keep an eye on memory and CPU usage to optimize your Power BI reports.

One of the best practises that I came across recently, Is to have a separate measures table so that all of them are organised and maintained in one place and can be retrieved easily, without confusing with the existing columns. Measures are dynamic calculations and do not belong to any of the existing tables. Hence they all can be organised together and need not be part of the related table. To do this, you can 1. create a new table with a dummy column by ( enter data ) option 2. Select the measure, and in measure tools tab , under home table , change the location to the newly created table 3. Once measure is moved, dummy column can be deleted. 4. Follow the same for the rest of the measures!

My top 5 best practices for writing DAX formulas would be: 1. Keep formulas simple and modular for readability. 2. Use meaningful variable and measure names. 3. Use comments to explain complex logic. 4. Optimize performance by avoiding unnecessary calculations. 5. Test and validate formulas thoroughly to ensure accuracy.

* Use "var ... return" if necessary to repeat any part of a formula. * Architect measures beyond their DAX code - what I mean by that is look how it would leverage filtering while building visuals. * Learn CALCULATE() and filters - learn well... * Minimum filtering in formulas, maximum leverage of filtering by data structure and table relations. * Avoid using DAX to pre-process data, if may be done with Power Query M. Do all possible there, tailor your data structure well (tables, relations). * Remember that BLANK() (null) is different than "0" (zero)! Both as data in a table and result of a measure calculation. Zero is an information and null means lack of information. That may severely affect calculation, for instance of averages.

Some Best Practices of writing DAX formulas in Power BI are: 1. Understanding your data is essential for providing guidance in writing effective DAX formulas. 2. Begin with the simple DAX formulas and gradually increase complexity to avoid errors and confusion. 3.Use meaningful names to measures for easy readability and understanding. 4. Clearly define the relationships between tables, as this is essential for your accurate data modeling and reporting.

One resource you will find useful is daxpatterns.com. This site has many of the different patterns of DAX formulas you may need.

Here are some practical examples: -Calculating Total Sales: Total Sales = SUM(Sales[SalesAmount]) This formula sums up the 'SalesAmount' from the 'Sales' table. -Calculating Average Sales: Average Sales = AVERAGE(Sales[SalesAmount]) This computes the average 'SalesAmount' from the 'Sales' table. -Year-to-Date Sales: YTD Sales = TOTALYTD(SUM(Sales[SalesAmount]), 'Date'[Date]) This calculates the total sales up to the current date in a year. -Conditional Count: High Value Orders = COUNTX( FILTER(Sales, Sales[SalesAmount] > 10000), Sales[OrderNumber] ) This counts the orders where the 'SalesAmount' was over 10,000.

I wouldn't bother trying to memorize specific functions. You'll eventually memorize the ones you use all the time. In my opinion the single best resource for DAX is daxpatterns.com. It covers virtually every business scenario with increasingly more complex implementations of the pattern depending on your needs. If your data model is built correctly, the vast majority of the time, you can copy the code and just change the table and column names to match your data model. Best of all, the explanations are top notch.

Some Best practices around writing DAX: 1. Understand the Data Model 2. Use Descriptive Names 3. Comment Your Code 4. Avoid Ambiguity in Naming 5. Break Down Complex Calculations 6. Use Variables Wisely 7. Leverage Measures for Reusability 8. Mind Data Type Compatibility 9. Handle Errors and Edge Cases 10. Optimize Performance 11. Test and Validate Formulas 12. Stay Updated with DAX Functions 13. Document Calculations and Measures

Writing DAX (Data Analysis Expressions) formulas in Power BI is essential for unleashing the platform's full analytical potential. DAX enables the creation of custom calculations, measures, and calculated columns, making it a cornerstone of data modeling and reporting. By crafting DAX formulas, analysts can aggregate, filter, and manipulate data in ways that standard visuals cannot achieve.

1. Types First: Before diving into DAX functions, acquaint yourself with the types. Knowing the DAX function landscape helps you navigate and select the right tool for the job. 2. Mental Blueprint: Complexity in DAX? Visualize your approach first. Design the logic in your mind’s eye – it’s like a roadmap to smoother DAX scripting. 3. Simplify, Simplify: Streamline your code. Remove or comment out redundant variables to keep the computational complexity in check. A clean workspace is a productive workspace. 4. Utilize and Optimize: Don’t just use DAX functions, make them your tools. Utilize them with precision, and go a step further – optimize their performance for efficient data modeling. Your data will thank you!

As I am not a BI expert, I asked ChatGPT to design a training plan (with a max of 4h/weekly): Weeks 1-2: Getting Started Week 1 (2h): Day 1: Introduction to DAX and data modeling (1h) Day 2: Basics of measures and calculated columns (1h) Week 2 (2h): Day 1: Common DAX functions (1h) Day 2: Practice creating basic measures and calculated columns (1h) Weeks 3-4: Building Skills Week 3 (4h): Day 1: Intermediate DAX functions (1h) Day 2: Working with tables and relationships (1h) Day 3: Practice building more complex DAX calculations (1h) Day 4: Troubleshooting and debugging in DAX (1h) Week 4 (4h) Day 1: Time intelligence in DAX (1 hour) Day 2: Advanced DAX functions (1 hour) ... Weeks 5-6: Practical Application ...