--- title: Introduction to DAX: Visually Explained | Power BI Course 27 description: An introductory lesson on DAX (Data Analysis Expressions) in Power BI, covering the full learning roadmap and foundational concepts. Explains the two main... canonical: https://daily.dev/posts/introduction-to-dax-visually-explained-power-bi-course-27-zpdfggyzd twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Introduction to DAX: Visually Explained | Power BI Course 27 | daily.dev og:description: An introductory lesson on DAX (Data Analysis Expressions) in Power BI, covering the full learning roadmap and foundational concepts. Explains the two main... og:url: https://daily.dev/posts/introduction-to-dax-visually-explained-power-bi-course-27-zpdfggyzd og:image: https://api.daily.dev/og/posts/zPDFGGYzd.png og:image:alt: Introduction to DAX: Visually Explained | Power BI Course 27 og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Introduction to DAX: Visually Explained | Power BI Course 27 **[Data with Baraa](https://daily.dev/sources/datawithbaraa)** · 46 min read · 1 upvotes · 0 comments ## Summary An introductory lesson on DAX (Data Analysis Expressions) in Power BI, covering the full learning roadmap and foundational concepts. Explains the two main types of DAX calculations: calculated columns (persisted data) and measures (on-the-fly calculations). Covers DAX syntax basics, when to use columns vs. measures vs. Power Query vs. SQL/Python, the four 'homes' of DAX (calculated columns, calculated tables, measures, and visual calculations), implicit vs. explicit measures, quick measures, and practical tips for validating measure results. Emphasizes understanding concepts over memorizing 200+ functions, and recommends pushing data preparation upstream to SQL/Python when possible. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the difference between a DAX calculated column and a DAX measure in Power BI? A calculated column persists its result physically in the data model, row by row, and is recalculated only when the data is refreshed, increasing file size. A measure stores only the formula as metadata, taking no extra space, and is calculated on the fly each time it is used in a visual, which can slow report rendering if calculations are complex. _Compare column versus measure trade-offs on daily.dev before deciding how to model Power BI reports._ ### Why does a Power BI measure formula have to start with a function like SUM? Measures must start with a function because they always return a single aggregated value, and a function is needed to collapse multiple row values into one result, such as SUM(FactSales[Amount]). Typing just a column name gives no suggestions and produces an error, whereas calculated columns can use direct column references and math operators without a leading function. _Developers puzzling over DAX syntax errors can dig deeper into measure rules on daily.dev._ ### Where should data transformations be done in a Power BI project: SQL, Power Query, or DAX? Transformations should be pushed as far upstream as possible, ideally in SQL or Python before the data reaches Power BI, since these tools handle data preparation better and the results become reusable across multiple projects. Power Query is the next best option, followed by DAX calculated columns, which should be reserved for cases needing full visibility into the data model's relationships and filters. _Teams choosing between SQL, Power Query, and DAX for data prep can weigh options via daily.dev._ ## Similar posts on daily.dev - [Comparing Explicit Measures to Calculation Groups in Tabular Models](https://daily.dev/posts/comparing-explicit-measures-to-calculation-groups-in-tabular-models-vyr7qz5dl) · Towards Data Science · 0 upvotes · 0 comments - [How to Use AI to Write DAX Measures: 5 Powerful Prompts for Power BI](https://daily.dev/posts/how-to-use-ai-to-write-dax-measures-5-powerful-prompts-for-power-bi-derqgkqwu) · Medium · 0 upvotes · 0 comments --- Tags: [#data-analysis](https://daily.dev/tags/data-analysis), [#bi](https://daily.dev/tags/bi), [#power-bi](https://daily.dev/tags/power-bi) [View this post on daily.dev](https://daily.dev/posts/introduction-to-dax-visually-explained-power-bi-course-27-zpdfggyzd) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Introduction to DAX: Visually Explained | Power BI Course 27","url":"https://daily.dev/posts/introduction-to-dax-visually-explained-power-bi-course-27-zpdfggyzd","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/introduction-to-dax-visually-explained-power-bi-course-27-zpdfggyzd"},"datePublished":"2026-07-28T12:57:02.288Z","dateModified":"2026-09-14T06:40:22.288Z","description":"An introductory lesson on DAX (Data Analysis Expressions) in Power BI, covering the full learning roadmap and foundational concepts. 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