Power BI Essentials: Understanding the Workflow and Interface

The end-to-end workflow you will use in every Power BI project

Most Power BI work follows a repeatable pipeline. Keeping this sequence in mind helps you diagnose issues quickly (for example: “Is this a data problem, a transformation problem, or a model problem?”) and build reports that refresh reliably.

1) Import data (connect)

You start by connecting Power BI Desktop to one or more sources (files, databases, web APIs). At this stage, focus on choosing stable sources and predictable file paths so refresh works the same way tomorrow as it does today.

2) Transform data with Power Query (shape)

Power Query is where you clean and standardize data before it enters the model: rename columns, set data types, remove errors, split/merge columns, filter rows, and create reusable logic. Power Query steps are recorded and replayed during refresh, which is why consistent source locations matter.

3) Model with relationships (structure)

After loading, you define how tables relate (typically a star schema): dimension tables (like Customers, Products, Dates) filter fact tables (like Sales). Correct relationships are the foundation for accurate totals and slicer behavior.

4) Add calculations with DAX (calculate)

DAX measures and calculated columns add business logic: revenue, margin, year-to-date, conversion rates, and more. Measures are evaluated at query time based on filter context, so they depend heavily on a clean model and correct relationships.

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5) Validate the semantic model (trust)

Before building many visuals, validate that the model behaves correctly: totals match expectations, filters flow as intended, data types are correct, and key measures return sensible results. This “semantic model check” prevents downstream rework.

• Quick validation examples: compare row counts to source, spot-check totals for a known month, verify that selecting a single product filters sales correctly, confirm that blank keys do not create unexpected “(Blank)” categories.

Power BI Desktop interface: practical navigation

Power BI Desktop is organized around three main views plus the Power Query Editor. You will switch between them constantly while building.

Report view (build visuals)

Use Report view to create pages, add visuals, and test slicers and interactions. This is where you consume the model: you drag fields into visuals, add measures, and format the report.

• When to use it: after data is loaded and relationships are set, to validate behavior and design the report.
• What to watch: if a visual total looks wrong, the cause is often in Model view (relationship) or in DAX (measure logic), not in the visual itself.

Data view (inspect tables and columns)

Use Data view to inspect loaded tables, check column values, confirm data types, and do quick sanity checks. It is not a replacement for Power Query; it is mainly for verification and light modeling tasks.

• When to use it: to confirm that transformations loaded correctly, to spot unexpected blanks, and to preview how columns look in the model.
• Practical tip: sort by a key column to quickly see duplicates or missing values.

Model view (relationships and model structure)

Use Model view to define and review relationships, set cardinality and cross-filter direction, and organize the model layout. A clean Model view layout makes troubleshooting much easier.

• When to use it: immediately after loading tables, and again whenever you add a new table or key.
• Practical tip: keep dimension tables grouped together and place fact tables centrally; hide technical key columns from report users once relationships are established.

The Fields pane (what you can use in reports)

The Fields pane lists tables, columns, and measures available to build visuals. It reflects your semantic model: names, folders, hidden fields, and measure organization.

• Practical tip: rename columns to business-friendly names early (preferably in Power Query) so report building is faster and less error-prone.
• Practical tip: create a dedicated “Measures” table later to keep measures organized (you can do this once you start writing DAX).

Where Power Query Editor fits

Power Query Editor is a separate window used for data ingestion and transformation. You open it from Power BI Desktop and return to Desktop after applying changes.

• Open: Home > Transform data
• Load changes back to the model: Home > Close & Apply
• Key idea: Power Query steps run during refresh; the model uses the output of those steps.

Guided setup: create a small practice dataset for reuse

You will reuse a small dataset across later chapters to practice transformations, relationships, and calculations. The goal is a repeatable setup: stable file locations, predictable refresh, and a model that can be extended.

Step 1: Create a consistent course folder structure

Create a dedicated folder on your computer (or a synced drive) and keep it unchanged throughout the course.

• Create a root folder, for example: C:\PowerBI_Essentials\
• Inside it, create: Data\, PBIX\, Exports\
• Save your Power BI file in PBIX\ and keep source files in Data\

This structure reduces broken paths and makes refresh behavior predictable.

Step 2: Create the practice files (CSV) in the Data folder

Create three CSV files in Data\. You can create them using a text editor (Notepad) or Excel (save as CSV). Ensure the filenames match exactly.

File 1: Sales.csv

SaleID,OrderDate,CustomerID,ProductID,Quantity,UnitPrice
S-1001,2024-01-03,C001,P01,2,25
S-1002,2024-01-03,C002,P02,1,120
S-1003,2024-01-04,C001,P03,3,15
S-1004,2024-01-05,C003,P02,2,120
S-1005,2024-02-01,C002,P01,1,25
S-1006,2024-02-02,C004,P04,5,9
S-1007,2024-02-10,C003,P03,2,15
S-1008,2024-03-12,C001,P02,1,120

File 2: Customers.csv

CustomerID,CustomerName,Segment,Country
C001,Alpine Bikes,Retail,USA
C002,City Sports,Wholesale,USA
C003,Delta Outfitters,Retail,Canada
C004,Evergreen Market,Online,USA

File 3: Products.csv

ProductID,ProductName,Category
P01,Helmet,Accessories
P02,Road Bike,Bikes
P03,Water Bottle,Accessories
P04,Energy Bar,Nutrition

These files are intentionally small so you can quickly see the impact of each step in Power Query and Model view.

Step 3: Start a new Power BI Desktop file and connect

In Power BI Desktop:

• Go to Home > Get data > Text/CSV
• Select Sales.csv from your Data\ folder
• Choose Transform Data (not Load) so you can set up clean steps from the start

Repeat for Customers.csv and Products.csv (Home > New Source > Text/CSV).

Step 4: In Power Query, standardize types and basic cleanup

In Power Query Editor, perform these checks for each query:

• Confirm column names are correct and readable.
• Set data types:
  • Sales[OrderDate] = Date
  • Sales[Quantity] = Whole Number
  • Sales[UnitPrice] = Decimal Number
  • IDs and names = Text
• Check for obvious issues: blank IDs, unexpected nulls, or incorrect date parsing.

Optional but useful: rename queries to singular nouns (Sales, Customers, Products) and ensure each query has a clear “Changed Type” step near the end.

Step 5: Load the data into the model

In Power Query Editor:

• Select Home > Close & Apply

This loads the three tables into the model so you can define relationships.

Step 6: Create relationships in Model view

Go to Model view and create (or confirm) these relationships:

• Customers[CustomerID] (one) to Sales[CustomerID] (many)
• Products[ProductID] (one) to Sales[ProductID] (many)

Use single-direction filtering from dimensions to fact (Customers/Products filter Sales). This supports predictable slicing behavior.

Step 7: Quick validation in Report view (no DAX required yet)

In Report view, add a simple Table visual to validate that filtering works:

• Add fields: Customers[CustomerName], Products[ProductName], Sales[Quantity], Sales[UnitPrice]
• Add a slicer for Customers[CustomerName] and confirm the table filters as expected.

This is not about formatting; it is a fast check that relationships and data types are behaving.

Step 8: Save the PBIX and establish a repeatable refresh routine

Save the file to your PBIX\ folder (for example: Practice_Model.pbix). Your repeatable refresh routine should look like this:

• Keep source CSVs in Data\ and do not rename or move them.
• When you edit a CSV, return to Power BI Desktop and select Home > Refresh.
• If refresh fails, first verify file paths in Power Query (Data source settings) and confirm the files still exist in the same folder.

This practice setup will be extended later (for example, adding a Date table, additional columns, and measures), so keeping it stable now saves time later.