# Power BI, From Zero Most people meet Power BI backwards. Someone hands them a `.pbix` file, or drops them into an existing report, and they learn by poking at buttons until something moves. That works, eventually - but it leaves gaps you don't notice until they cost you: a report that recalculates the wrong number for last quarter, a dashboard that quietly breaks when the source file gets renamed, a measure that's actually a calculated column wearing a trench coat. This guide builds the tool the other way: from the ground up, so every piece you add sits on a model you actually understand. Power BI looks like four different products stapled together - a query editor, a modeling layer, a formula language, and a drag-and-drop canvas - and that's roughly what it is. **Power Query** gets data in and cleans it up. The **data model** decides how your tables relate to each other. **DAX** is the formula language that turns rows into answers. And the **report canvas** is just the last mile - the part everyone sees, built on everything underneath it. Learn them in that order and Power BI stops feeling like a pile of menus and starts feeling like a pipeline: data in, shaped, modeled, calculated, shown. The spine of this guide is one idea that shows up everywhere in analytics tooling once you know to look for it: the **star schema** - one central table of *facts* (the numbers you measure) surrounded by *dimension* tables (the things you slice by: date, product, region, customer). Power BI's data model is a star schema in different clothes, and once that clicks, relationships, DAX context, and even your report design start making sense as consequences of that one shape rather than as separate things to memorize. If you want the concept on its own first, [The Star Schema, Explained](/guides/star-schema-explained) covers it without any Power BI in the way - worth a detour before phase 4 if the term is new to you. We go mental-model-first throughout: before any button or formula, you'll understand what the thing actually *is* and why it's shaped that way. By the end you won't just have a report - you'll have one that refreshes on a schedule, shows the right numbers to the right people, and that you'd trust with your own name on it. ## How to read this - **Never opened Power BI? Read 1-6 in order.** Phases 1-3 get real data into a clean shape. Phase 4 is the turning point - the data model - and it changes how you think about everything after it. Phases 5-6 build DAX from first principles instead of throwing syntax at you. - **Come from Excel?** You already know rows, columns, and formulas like `SUM()`. The adjustment is phase 4 (a model isn't one big flat table) and phase 5 (DAX formulas live on the *model*, not a cell - there's no "cell reference" to drag down). Skim 1-3, slow down from 4 onward. - **Already building reports, want to stop guessing?** Jump to [Phase 6: Measures vs Calculated Columns](06-measures-vs-calculated-columns.md) - the single most common source of "why is this number wrong" - then keep going through time intelligence, visuals, and publishing. ## The phases 1. **[What Power BI Actually Is & Getting Set Up](01-what-power-bi-actually-is-and-getting-set-up.md)** 🟢 - the four layers (Query, Model, DAX, canvas), Desktop vs Service, and installing Power BI Desktop. 2. **[Connecting to Data Sources](02-connecting-to-data-sources.md)** 🟢 - Excel, CSV, databases, and web sources; import vs DirectQuery, and why that choice matters early. 3. **[Shaping Data with Power Query](03-shaping-data-with-power-query.md)** 🟡 - get & transform, the Applied Steps list, and why Power Query records *how* you cleaned the data, not just the result. 4. **[The Data Model & Relationships](04-the-data-model-and-relationships.md)** 🟡 - a star schema in different clothes: fact tables, dimension tables, relationships, and cardinality. 5. **[DAX From Intuition](05-dax-from-intuition.md)** 🟡 - row context vs filter context, the idea that makes every DAX formula make sense once it clicks. 6. **[Measures vs Calculated Columns](06-measures-vs-calculated-columns.md)** 🟡 - the distinction that causes most "why is this number wrong" bugs, and how to pick the right one every time. 7. **[Time Intelligence](07-time-intelligence.md)** 🔴 - `CALCULATE`, a real date table, and year-to-date/prior-period comparisons that don't silently lie. 8. **[Visualizations That Do Not Lie](08-visualizations-that-do-not-lie.md)** 🟡 - choosing the chart that matches the question, axis and scale traps, and what to leave out. 9. **[Building Reports & Dashboards](09-building-reports-and-dashboards.md)** 🟡 - pages, slicers, drill-through, bookmarks, and report vs dashboard (they are not the same thing). 10. **[Publishing & Sharing (Workspaces, Apps, RLS)](10-publishing-and-sharing-workspaces-apps-rls.md)** 🔴 - workspaces, Apps, and row-level security so the right person sees only their own data. 11. **[Refresh, Gateways & Capacity Basics](11-refresh-gateways-and-capacity-basics.md)** 🔴 - scheduled refresh, the on-premises data gateway, and what capacity limits mean for you in practice. > Power BI's DAX engine and the star schema underneath it aren't unique to Power BI - the same shape > powers Tableau, Looker, and most modern warehouses. Learn it here and it transfers.