A plain-English guide for everyone who's ever looked at a dashboard and wondered:
"Where did this number come from?"
↑ Data flows bottom → top · Click any layer to explore
Power BI connects to your data wherever it already lives. Before any chart, any formula, any dashboard — the data has to come from somewhere.
⚠️ The Golden Rule
Garbage in, garbage out. If the source data is messy, incomplete, or wrong — Power BI will show you a prettier version of that mess.
Data quality at the source matters more than any formula you write downstream.
Every great dashboard starts with ugly, raw data. Power Query is where the transformation happens — invisibly, automatically, every time.
Raw data arrives from all over — databases, spreadsheets, websites. It's messy. Duplicates, nulls, wrong formats.
Power Query is your kitchen prep station. Every transformation is recorded — like a recipe. Change an ingredient? The whole recipe re-runs.
| Date | Product | Sales |
|---|---|---|
| 01/01/24 | Widget A | $1,200 |
| 02/01/24 | Widget B | $840 |
| 03/01/24 | Widget A | $2,100 |
Clean data loads into the model. Now the DAX measures can do their magic.
💡 Why this matters for you: If a number looks wrong on your dashboard, the problem is usually HERE — in the prep kitchen, not the chart itself. Always check Power Query first.
Tables don't talk to each other by magic. Relationships wire them together — and when you click a filter, the whole model responds instantly.
Every Power BI model has a hierarchy. Here's how it works.
"The Team Lead doesn't store details themselves — they ask their specialists. That's a Star Schema."
📐 What's Cardinality?
One calendar date → many sales. One customer → many orders. This "one-to-many" relationship is called Cardinality.
Think of it like one teacher → many students. The teacher (date) doesn't repeat themselves, but the students (sales) can refer to that same teacher many times.
🔍 What's Context Transition?
Imagine you're sitting at a restaurant table. When a calculation needs totals, the waiter doesn't look at all tables — instead, he takes your table number and turns it into a filter, so the kitchen knows to calculate only for your table.
That conversion — from "this row" to "a filter" — is Context Transition. It happens whenever CALCULATE is called inside a row context (like inside SUMX or a calculated column). DAX converts each row into an equivalent set of filters before evaluating the expression.
Charts, tables, KPI cards, slicers, maps — this is the UI. It sits on top of all the logic below. And here's the thing most beginners miss:
🔍 Where to look when something's wrong
Number incorrect? → Check the DAX measure
Data missing? → Check Power Query
Wrong rows showing? → Check relationships in the Semantic Model
No data at all? → Check the data source connection
The visual is almost never the problem. It's a window — not the room.
Power BI isn't one app — it's three. Knowing which does what will save you a lot of confusion.
Every click triggers a chain reaction across the entire model — here's what happens in under a second.
Plain-English explanations for the Power BI jargon everyone throws around.
Your charts are the tip of the iceberg. The real work — cleaning, modeling, calculating — happens in the layers below, invisible to most viewers.
Every click you make sends a message through the data model, triggering a chain reaction that updates every visual on the page — instantly.
The numbers aren't stored — they're calculated fresh, every time, just for you and your current filter context. That's what makes Power BI so powerful.