DAX Decoded: Essential Fundamentals in Power BI

Distinguish between measures and calculated columns in Power BI using DAX, showing when to use each, including revenue calculations with related, sumX, and model-optimized dynamic results.

Create and organize DAX measures in a key measures group to master simple aggregations in Power BI, using context and filters to drive average, min, max, and count rows.

Explore DAX formula syntax, comments, and variables to write cleaner, more readable measures in Power BI, including variable branching and using calculate and filter for clearer logic.

Learn the key difference between Excel formulas and Power BI calculations: Excel references cells, while Power BI uses columns and tables evaluated by the model context and filters.

Understand how harmonizing the data model with DAX drives deep insights in Power BI by layering filters, interactions, and lookup tables to reveal dynamic metrics.

Explore evaluation context in Power BI, where each result is calculated with its own environment. Understand how slicers, visuals, and dimensions shape the context before DAX computes results.

Unlock filter context in DAX by showing how evaluation context and applied filters shape a simple sum, with the aggregation and iterating engines processing remaining rows.

Explore row context as the iterating calculation engine in DAX, using iterating functions like sumx to evaluate expressions at every row, and compare it with traditional filter context.

Explore how context shapes DAX calculations in Power BI, using calculate and filter to create running totals within dynamic contexts and understand the impact of evaluation context.

Master how the calculate function changes the context of a measure and enables time intelligence and location filters, illustrated by same period last year and North Carolina examples.

Understand how simple filters work in DAX through calculate and relationships. Learn why applying a dedicated filter function stabilizes results across contexts in Power BI.

Explore table functions in DAX, including filter, values, and all. Learn how to visualize and test their behavior by creating new tables in Power BI.

Explore time intelligence functions in DAX, including date add and same period last year, with month, quarter, and year to date calculations for cumulative insights.

Combine multiple patterns of DAX formula using measure branching and meta branching to build cumulative sales and compare versus last year, unveiling trends in Power BI visuals.

Explore quick measures in Power BI to rapidly create DAX calculations, weigh pros and cons, and reinforce mastering DAX fundamentals for accurate, flexible formulas.

Explore an advanced DAX example in Power BI, using measure branching to compute profits and create top 50, 50–200, and rest customer groups with time-based percentages.

Learn to compute percent of total in Power BI by creating a total sales measure, using calculate with all to remove product filters, and dividing by the total.

Learn how percent of total in DAX changes with context, and use slicers to reveal how product and customer filters affect overall and per-context totals.

Learn to build a simple moving average in DAX by creating a total sales measure, using an iterator and dates in period to compute 14- and 60-day trends.

Reuse the moving average pattern by swapping in a core measure, like total transactions from the sales data table, to track daily transactions with a quick copy-and-paste approach.

Learn to compute cumulative sales in Power BI with DAX by using calculate and filter all dates, iterating the date table, and aligning with max date for time-based analysis.

Learn how to calculate dynamic cumulative sales in Power BI by using allselected to respect the initial date range and slicers, replacing all for accurate time based totals.

Reuse the cumulative totals pattern to quickly compute and compare measures across timeframes by copying and substituting the core measure, including same period last year.

Apply time intelligence patterns to calculate month-to-date, quarter-to-date, and year-to-date sales, extend calculations with same period last year, and compare quarterly performance.

Exploit time intelligence functions and measure branching to compare year-over-year and quarterly performance, add context with products and regions, and visualize insights with column and stacked bar charts.

Explore calculating percentage change from the same period last year in Power BI using DAX, applying total sales, year-on-year analysis, and context changes to monthly, quarterly, or yearly views.

Learn how to use the dateadd time intelligence function to jump to parallel periods, compare sales across months and years, and build cumulative totals and moving averages.

Create a price range table with no relationships to other tables and a calculated column to group products by price, enabling dynamic analysis across price tiers.

Group customers by creating a lookup table dimension. Use DAX with context transition and calculate to classify top and bottom clients based on sales.

Learn dynamic grouping in DAX by using a measure with calculate to map unit prices to pricing groups via a support table, enabling flexible sales analysis without calculated columns.

Harness dynamic groupings in power bi using dax to rank customers by total sales, creating top five, 5 to 20, and rest cohorts and analyzing trends over time.

Learn the events in progress pattern in Power BI, using inactive date relationships and CALCULATE to count orders or sum revenue between order date and ship date.

Learn budgeting models by linking city budgets to a regional budget in Power BI, then allocate annual budgets across daily dates using a simple days-in-context algorithm.

Expand budgeting models in Power BI by building cumulative sales versus budgets visuals and city-level analysis, using a unique cities table and one-to-many relationships to compare performance.

Explore forecasting in Power BI with DAX by tracking profits, comparing to last year, and modeling cumulative forecasts with reusable patterns and scenario analysis.

Extend your dax forecasting skills by adding multiple scenarios, creating dynamic measures, and branching calculations to compare cumulative profits against last year and forecast scenarios.

Explore parameter tables in Power BI to drive dynamic analysis, using a standalone discount parameter table, the values function, and measures to compute total and cumulative sales with discounts.

Extend dax analyses with parameter tables to run scenarios and apply discounts. Create measures and charts to track cumulative sales over time for discounted products.

Master handling parameter tables in DAX by returning zero when nothing is selected, using hasonevalue to feed the correct value across measures and support scenario analysis.

Explore advanced scenario analysis in Power BI by modeling price changes and their impact on revenue, using measures to compare actuals vs scenarios and isolate product effects.

Layer multiple variables in measures to enable multi-layered scenario analysis in Power BI, using supporting tables for price, demand, and cost changes and iterators to compute scenario profits.

Explore what-if scenario analysis in Power BI by modeling profit margins on a dynamic data model, using scenario tables, moving averages, and measures to project profits over time.

Learn how related distinct counts work within Power BI by using expanded tables and CALCULATE to count unique cities sold per day, across suburbs and regions.