Forem: Ibrahim0695

Publishing a Power BI report and Embedding it in a Website

Ibrahim0695 — Sun, 05 Apr 2026 18:54:29 +0000

Introduction to Power BI and Publishing

Power BI is a powerful business analytics tool by Microsoft that transforms raw data into interactive visualizations and reports. Once you've created a compelling report with charts, tables, and insights, the next step is sharing it with your audience. Power BI provides seamless publishing capabilities that allow you to publish your reports to the Power BI Service (the cloud platform), generate embed codes, and integrate those reports directly into websites for broader accessibility.

Publishing a Power BI report involves uploading your .pbix file from Power BI Desktop to the Power BI Service, organizing it within a workspace, and then using the embed functionality to display the report on any webpage. This guide walks you through the entire process with detailed steps and visual guidance.

What you need:

Before you begin, ensure you have:

Power BI Desktop installed
A Power BI Pro or Power BI Premium license
A Power BI account (sign up at app.powerbi.com)
A report saved as a .pbix file
A website where you can embed HTML/JavaScript code

Step 1: Creating a Workspace in Power BI Service

A workspace in Power BI is a holder of your reports, datasets, dashboards, and workbooks. Think of it as a project folder where related content lives together.

Screenshot: Power BI Service - Create Workspace

Step-by-step:

Sign in to Power BI Service
- Open your browser and navigate to app.powerbi.com
- Sign in with your Microsoft account

Create a New Workspace
- In the left navigation pane, click on "Workspaces"
- Click the "Create a workspace" button (a plus icon or "New" button)
- You will see the workspace creation panel slide out

Configure Your Workspace
- Name: Enter a descriptive name such as "Sales Analytics" or "Marketing Reports"
- Description: Add a brief description explaining the workspace's purpose
- Workspace settings: Under "Advanced," you can configure:
  - License mode: Choose between Pro or Premium Per User (PPU)
  - Storage: Set a capacity if using Premium
  - Contact list: Add team members who will receive updates
  - OneDrive: Optionally connect to a SharePoint/OneDrive for seamless file management

Save the Workspace
- Click "Save" to create the workspace
- Your new workspace will appear in the workspaces list

Step 2: Publishing Your Report to Power BI Service

With your workspace ready, the next step is to publish your .pbix file. This uploads both your data model and your report design to the cloud.

Step-by-step in Power BI Desktop:

Open Your Report
- Launch Power BI Desktop
- Open the .pbix file you want to publish

Initiate the Publish Process
- Click the "Publish" button on the Home ribbon tab
- Alternatively, use the keyboard shortcut: Alt + F → H → P
Select Your Destination
- The "Publish to Power BI" dialog box appears
- Under "Destination," expand the dropdown
- Select the workspace you created (e.g., "Sales Analytics")
- Note: You can also publish to "My Workspace" for personal use

Confirm and Publish
- Click the "Publish" button
- Power BI Desktop will validate your data, upload the dataset, and then publish the report
- A success message appears with a link: "Open 'Your Report' in Power BI"
Verify the Upload
- Click the success link or go to app.powerbi.com
- Navigate to your workspace
- Confirm that your report and dataset appear

Step 3: Generating the Embed Code

Once your report is published, you can generate an embed code to display it on any website. Power BI offers two embedding methods: Embed for your organization (requires viewer authentication) and Embed for your customers (using embed tokens for public access).

Step-by-step:

Navigate to Your Report
- In Power BI Service, go to your workspace
- Click on the report you published

Access the Embed Options
- In the report view, look for the "File" menu or the "Share" button
- Alternatively, click the "..." (More options) menu on the report tile
- Select "Embed" → "Website or portal"
Configure Embed Settings
- The embed dialog shows your report with configuration options:
  - Security: Choose "Organization" (requires sign-in) or "Public" (embed token)
  - Size: Select canvas size (e.g., 16:9, 4:3, or custom)
  - Filters: Enable/disable filter pane visibility
  - Navigation: Enable/disable page navigation
  - Title and Banner: Show or hide report title and action bar
  - Background: Set transparency level
Generate the Embed Code
- After configuring settings, click "Apply"
- Power BI generates an iframe embed code and optionally an HTML/JavaScript snippet

Copy the Embed Code
- Copy the iframe code or the embed link to your clipboard
- You can also download the embed script for advanced integration

Embed Code Example:

<iframe 
    title="Sales Report" 
    src="https://app.powerbi.com/reportEmbed?reportId=abc12345&groupId=xyz67890" 
    frameborder="0" 
    allowFullScreen="true">
</iframe>

Screenshot Location: Power BI Service → Report View → File → Embed → Website or portal

Step 4: Embedding the Report on a Website

Now that you have the embed code, you can paste it into your website's HTML. The exact method depends on your website platform (WordPress, custom HTML, SharePoint, etc.).

Method 1: Embedding in a Standard HTML Website

Step-by-step:

Open Your Website Editor
- Access the HTML or source code of the page where you want the report
- Use a code editor or the built-in HTML editor of your CMS
Paste the Embed Code
- Locate the section of the page where you want the report to appear
- Paste the iframe code you copied from Power BI
Adjust Size and Styling
- Modify the width and height attributes to fit your layout:

   <iframe 
       title="Sales Report" 
       width="100%" 
       height="600" 
       src="https://app.powerbi.com/reportEmbed?reportId=abc12345&groupId=xyz67890" 
       frameborder="0" 
       allowFullScreen="true">
   </iframe>

Save and Preview
- Save your changes and preview the page
- The Power BI report will load and be interactive within the iframe

Method 2: Embedding in WordPress

Switch to HTML/Code View
- In the WordPress editor, click the "Code editor" or "</>" button
Paste the Embed Code
- Paste the iframe code in the desired location within the page/post
Option: Use a Plugin
- Install a Power BI embed plugin for WordPress
- Use a shortcode provided by the plugin instead of raw iframe code

Method 3: Embedding in SharePoint Online

Edit Your SharePoint Page
- Navigate to the SharePoint site and click "Edit"
Add a Web Part
- Click the "+" icon to add a new web part
- Search for and select "Power BI" (if available) or use the "Embed" web part
Paste the Embed Link
- If using the Embed web part, paste the report URL (not the full iframe code):

   https://app.powerbi.com/reportEmbed?reportId=abc12345&groupId=xyz67890

Save the Page
- Click "Publish" or "Republish" to make the report visible

Method 4: Using Power BI Embedded (For Developers)

For advanced embedding with custom authentication and user-specific views, use the Power BI Embedded API:

Register an Application in Azure
- Go to the Azure Portal → Azure Active Directory → App registrations
- Create a new application registration
- Note down the Client ID and Client Secret
Obtain an Embed Token
- Use the Power BI REST API to generate an embed token for your report
- Example using PowerShell or a backend script:

   POST https://api.powerbi.com/v1.0/myorg/groups/{groupId}/reports/{reportId}/GenerateToken

Embed Using the JavaScript SDK
- Add the Power BI Client SDK to your webpage:

   <script src="https://npmcdn.com/powerbi-client@2.22.0/dist/powerbi.min.js"></script>

Initialize and embed the report:

   const embedConfiguration = {
       type: 'report',
       id: 'YOUR_REPORT_ID',
       embedUrl: 'YOUR_EMBED_URL',
       accessToken: 'YOUR_EMBED_TOKEN',
       settings: {
           filterPaneEnabled: true,
           navContentPaneEnabled: true
       }
   };

   const report = powerbi.embed(reportContainer, embedConfiguration);

Key Insights and Best Practices

1. Choose the Right Embedding Method

Publish to Web: Quick and easy for public reports, but no row-level security. Use only for non-sensitive data.
Embed for Organization: Leverages existing Power BI licenses. Users must sign in to view the report. Supports full security and row-level security.
Power BI Embedded (ISV): Best for embedding in applications you sell. Requires Azure registration and backend token generation.

2. Security Considerations

Never expose your .pbix file credentials or embedded tokens publicly
For sensitive data, always use row-level security (RLS) and embed with organizational authentication
Regularly rotate your application secrets in Azure

3. Performance Optimization

Use the Power BI Service to schedule dataset refreshes so your embedded report always shows current data
Avoid embedding too many reports on a single page — each iframe consumes resources
Consider using Power BI paginated reports for print-ready layouts instead of standard reports

4. Mobile Responsiveness

Design your reports with phone layouts in Power BI Desktop
Use responsive iframe sizing (width="100%") in your website to ensure the report adapts to different screen sizes

5. Collaboration and Governance

Use workspaces to organize reports by department or project
Leverage Power BI deployment pipelines for moving reports between Development, Test, and Production environments
Set up workspaces with Premium capacity if you need faster performance and larger dataset sizes

6. Tracking and Analytics

For public embeds, you can track usage via Power BI's usage metrics
For embedded applications, implement custom telemetry to monitor how users interact with your reports

Conclusion

Publishing and embedding a Power BI report is a straightforward process that transforms how you share data insights. By creating a workspace in the Power BI Service, publishing your .pbix file, configuring embed settings, and pasting the embed code into your website, you can deliver interactive, real-time dashboards directly to your audience — whether they are internal stakeholders or external customers.

Remember to choose the embedding method that aligns with your security requirements, optimize your reports for performance, and leverage Power BI's governance features to maintain control over your analytics assets. With Power BI's publishing and embedding capabilities, your data stories can reach anyone, anywhere, on any device.

Understanding Data Modeling in Power BI: Joins, Relationships, and Schemas Explained

Ibrahim0695 — Sun, 29 Mar 2026 20:24:56 +0000

INTRODUCTION
Following the first week of messing around with ‘backend’ data at LuxDevHQ, this week, we were given a chance to mingle with the data analytics group again. For what you might ask, well, Power BI. Honestly, I didn’t think I would enjoy ‘frontend’ data like this! A lot has been learnt, but I think the most intriguing part of Power BI is data modelling, because to me it has a sort of likeness to data engineering in that it improves performance, reduces errors, and makes analysis easier. Who wouldn’t want data that is organized in structures that allows for fast and accurate analysis and visualization... I mean who?

In this article I’m planning to dive into data modelling in BI, placing focus on joins, relationships and schemas.

So what is data modelling?
It is simply the process of connecting different data sources, defining how they relate to one another(tables), and organizing them into a structure that provides easy analysis. A good way to think of it is the way movie shops, categorizes and indexes its movies and series in a way that is easy to retrieve and view.

SQL Joins in Power BI simplified

SQL joins are implemented through the "Merge Queries" feature in the Power Query Editor, which combines rows from two tables based on a shared column.

Power Bi supports several join types, below are their description and a simple example to get you up to speed;

Inner join; Returns only rows where the key exists in both tables.

Targeted Marketing: A list of customers who have both an active account and a registered email address.

Left Outer(left join); Keeps all rows from the "Left" table and adds matching data from the "Right.

Sales Report: All products in your catalog, including those that haven't sold yet (which show as null).

Right Outer( right join); Keeps all rows from the "Right" table and adds matching data from the Left.

Audit: All transactions in a bank ledger, matching them to customer profiles (useful if some transactions lack profile data).

Full Outer JOIN; Returns all rows from both tables, matching them where possible.

Consolidated Inventory: Merging stock lists from two different warehouses to see every item available in the company.

Left Anti JOIN; Returns rows from the Left table that do not have a match in the Right.

Inventory Cleanup: Finding products in your warehouse that have never appeared in a sales record.

Right Anti JOIN; Returns rows from the Right table that do not have a match in the Left.

Error Tracking: Finding sales records that are missing a corresponding "Product ID" in your master product list.

How to Create Joins in Power BI:

Open Power BI;

- Click Transform Data on the Home ribbon to open the Power Query Editor.
- Select your primary table from the "Queries" pane on the left.
- In the Home ribbon, click Merge Queries.
- In the dialog box, select the second table from the dropdown.
- Crucial Step: Click the matching column in both table previews (e.g., ProductID).
- At the bottom, choose your Join Kind (e.g., Left Outer).
- Once merged, click the **Expand **icon (two arrows) in the new column header to choose which columns from the second table you want to pull in.

Relationships in BI

In Power BI, Relationships are the secret sauce that allows you to connect different tables so they can work together as one. Without them, you would just have a bunch of isolated lists that can’t speak to each other.

Imagine you have a list of Sales and a list of Products. A relationship tells Power BI that the "Product ID" in your Sales list is the same as the "Product ID" in your Product list.

The Core Concept: Fact vs. Dimension Tables
Before looking at the lines connecting tables, you need to know the two types of tables:

- Fact Tables(The "What happened"): These are long tables that store events or transactions (e.g., Sales, Temperatures, Stock levels). They usually have many numbers.
- Dimension Tables(The "Context"): These are shorter, unique lists that describe things (e.g., a list of every Product you sell, a list of every Store, or a Calendar).

TYPES OF RELATIONSHIPS

1. Cardinality (The "How Many")

When you drag a line between two tables in the Model View, Power BI looks at how many times a value appears on each side.

One-to-Many(1:M) The "Gold Standard"
This is the most common and healthiest relationship.

Example: One Product (in your Dimension table) can be sold Many times (in your Sales/Fact table).

One-to-One (1:1)
Every row in Table A matches exactly one row in Table B.
This is rare and usually means the to tables should probably just be merged into one.

Many-to-Many(M:M)
This happens when values repeat in both tables.

Example: A list of Students and a list of Classes (One student has many classes; one class has many students).

Warning: Beginners should avoid this if possible, as it can make your numbers "double-count" or act strangely.

2. Cross-Filter Direction (The "Flow")

The arrow in the middle of the relationship line tells Power BI which way the "filtering power" flows.

Single Direction(One-way arrow): The filter flows from the "One" side to the "Many" side. If you click a "Category" in a slicer, your "Sales" total updates. This is the safest and fastest setting.

Both Directions (Two-way arrow): The filter flows both ways. While it sounds helpful, it can cause performance lag and confusing results in large models.

3. Active vs. Inactive Relationships

Power BI only allows one active path between two tables.

Active (Solid Line): This is the "Main Highway." Power BI uses this for all your charts by default.

Inactive (Dotted Line): This is a "Backroad." It exists, but Power BI won't use it unless you write a specific piece of code (DAX) to tell it to take that path. This is common if you have both an Order Date and a Ship Date in the same table.

How to Create a Relationship (Step-by-Step)

Click the Model View icon on the far left of Power BI Desktop (it looks like three small boxes connected by lines).
Find the column that exists in both tables (e.g., CustomerID).
Click and drag the column name from one table and drop it directly onto the same column name in the other table.
A line will appear. You can double-click that line to open the "Edit Relationship" window if you need to change the Cardinality or Filter Direction.

** Managing Relationships**

Model View: Click the Model icon (the third one down on the left-hand pane).
Drag & Drop: Simply click a field in one table and drag it onto the corresponding field in another. Power BI will attempt to guess the cardinality.
The Dialog Box: Double-click any relationship line (or click Manage Relationships in the top ribbon) to manually toggle it between Active/Inactive or change the Cross-filter direction.

Schemas

A **Schema **is simply the way you arrange your tables and the relationships between them. Think of it like a map: it shows where the data lives and how a filter in one place travels to update a number in another.

To understand schemas like relationships, you first need to know the two types of tables that live inside them:

Fact Tables: The "What happened." These are long lists of numbers (Sales, Temperature readings, Stock levels).

Dimension Tables: The "Who, Where, and When." These provide context (Product names, Date calendars, Store locations).

Here are the three most common ways to organize these tables:

1. The Star Schema (The Gold Standard)
The Star Schema is the most recommended layout for Power BI. It features one Fact table in the center, directly connected to several Dimension tables. It looks like a star because the dimensions radiate out from the middle.

**Why use it? **It is the fastest for Power BI to calculate. Because every dimension is only "one jump" away from the fact table, your reports will be snappy and your DAX formulas will be simpler.

Tip: If you aren't sure which one to use,** always try to build a Star Schema.**

2. The Snowflake Schema
A Snowflake Schema is just a Star Schema where some of the "arms" (dimensions) are broken down into even smaller tables. For example, instead of having a "Product" table with the Category and Sub-category inside it, you have three separate tables: Product → Sub-Category → Category.

Why use it? It is very organized and saves space because it doesn't repeat words as often.

The Downside: It’s "colder" and slower. Power BI has to "jump" through multiple tables to get an answer (e.g., to find Sales by Category, it has to go from Category to Sub-Category to Product to Sales). This can slow down large reports.

3. The Flat Table (DLAT)
This isn't really a "schema" in the traditional sense; it’s just one giant table that contains everything—the sales, the product names, the dates, and the customer addresses all in one spreadsheet-style view.

Why use it? Beginners often start here because it’s how Excel works. It’s easy to understand at first glance.

The Downside: It is very inefficient. If you have 1 million sales of the same "Red Hammer," Power BI has to store the words "Red Hammer" 1 million times. This makes your file size huge and makes "Time Intelligence" (like comparing this month to last month) very difficult to calculate.

How to Build These in Power BI
You manage these schemas in the Model View (the icon on the far left that looks like three little boxes connected by lines).

Drag and Drop: Click a column (like ProductID) in one table and drag it onto the matching column in another table to create the "arm" of your schema.

Check the Direction: Look for the arrow on the line. In a good Star Schema, the arrow should point away from the Dimension and toward the Fact table. This means the Dimension is "filtering" the Fact.

Common Modeling Issues to Watch For

Role-Playing Dimensions: This happens when a dimension (like Date) needs to filter the Fact table multiple times. Solution: Use Inactive Relationships and the DAX function USERELATIONSHIP.
- Many-to-Many Hazards: Relating two tables on a non-unique key can lead to "Double Counting." Solution: Use a "Bridge Table" with unique values to sit between them.
- Circular Dependencies: When filters loop back on themselves. Power BI will block these to prevent infinite calculation loops

Conclusion

It is crucial that before you throw data into Power BI, model it first This is go along way in avoiding confusion and lag.