Forem: NelimaL

How to Publish a Power BI Report and Embed it into a Website

NelimaL — Mon, 06 Apr 2026 12:30:02 +0000

Introduction
As s student data plumber, there are very high chances are you've come across Power BI, Microsoft’s powerful business intelligence tool that transforms raw data into meaningful insights through interactive dashboards and reports.
So far, as a data student I’ve explored Power BI queries, DAX, data modeling, joins, charts, dashboards, and reporting. But creating a report is only half the job. The real value comes when you share it with others.

In this article, I’ll walk you through how to:

Publish a Power BI report
Generate an embed code
Embed the report into a website By the end, you’ll be able to make your reports accessible and interactive for users beyond Power BI.

What is Power BI Publishing?
Publishing in Power BI means uploading your report from Power BI Desktop (.pbix file) to the Power BI Service (cloud platform). Once published, your report can be shared, collaborated on, or embedded into websites.

Step 1: Create a Workspace
A workspace is like a container where your reports, dashboards, and datasets live.
Steps:
Go to Power BI Service (https://app.powerbi.com)
Sign in with your account
On the left sidebar, click Workspaces
Click + New workspace
Enter: Workspace name(e.g., Linda Nelima Workspace)
Description (optional)
Click Save

Step 2: Upload and Publish Your Report
Now that your workspace is ready, let’s upload the .pbix file.
Steps:
Open your project in Power BI Desktop
Click File → Publish → Select Workspace
Choose the workspace you created
Click Select

Alternatively (via browser):
Open your workspace in Power BI Service
Click Upload → Browse
Upload your .pbix file
Once uploaded, your report and dataset will appear in the created workspace.

Step 3: Generate the Embed Code
Power BI allows you to embed reports using an iframe.
iframe:(short for inline frame) is an HTML element that embeds another document, such as a video, map, or webpage, within the current HTML webpage
Steps:
Open your report in Power BI Service
Click File → Embed report → Publish to web (public)
Click Create embed code
Copy the generated iframe code
Note:“Publish to web” makes your report publicly accessible. Avoid using this for sensitive data.

Step 4: Embed the Report on a Website
Now let’s embed the report into a simple HTML page.
Example:
<!DOCTYPE html>

Power BI Report

Kenya Crops Dashboard

<iframe width="800" height="600"
    src="YOUR_EMBED_LINK_HERE"
    frameborder="0"
    allowFullScreen="true">
</iframe>

Steps:
Replace YOUR_EMBED_LINK_HERE with your copied embed URL
Save the file as index.html
Open it in a browser

You should now see your interactive Power BI report embedded on the page.

Key Insights
Power BI makes it easy to move from data analysis to sharing insights
Workspaces help organize and manage reports efficiently
Embedding reports allows integration with web apps and dashboards
Always be cautious with data privacy when using public embed options

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

NelimaL — Mon, 30 Mar 2026 14:40:50 +0000

Introduction

When I first started working with Power BI at LuxedevHQ, I quickly realized that building reports isn’t just about visuals, it’s about how well your data is structured underneath. That structure is called data modeling, and it’s the backbone of every meaningful dashboard.

What is Data Modeling
Data modeling is the process of organizing data into tables and defining how those tables relate to each other so that analysis becomes efficient, accurate, and scalable. In Power BI, data modeling involves:
• Structuring tables (Fact & Dimension)
• Creating relationships
• Defining cardinality and filter behavior
• Optimizing for performance and usability
Think of it as designing a map before starting a journey. Without it, you’ll get lost in your data.

SQL Joins
Before Power BI relationships, you need to understand joins, because they define how tables combine.
Let’s use this simple scenario
Customers
| CustomerID | Name |
|------------|------|
| 1 | John |
| 2 | Mary |
Orders
| OrderID | CustomerID | Amount |
|---------|------------|--------|
| 101 | 1 | 200 |
| 102 | 1 | 150 |

1. INNER JOIN
This returns only matching records.
Result
| Name | Amount |
|------|--------|
| John | 200 |
| John | 150 |
Mary is excluded because she has no orders and this only shows customers who made a purchase
2. LEFT JOIN
This returns all records from the left table and matches.
Result
| Name | Amount |
|------|--------|
| John | 200 |
| John | 150 |
| Mary | NULL |
Keeps all customers, even without orders.
3. RIGHT JOIN
This join returns all records from the right table and matches.
Use case: Show all orders, even if customer data is missing.
4. FULL OUTER JOIN
This returns everything from both tables.
Use case:Data reconciliation between systems.
5. LEFT ANTI JOIN
The Left Anti Join returns rows in left table with NO match in right.
Result
| Name |
|------|
| Mary |
Use case:Customers who never purchased.
6. RIGHT ANTI JOIN
Returns rows in right table with NO match in left.
Use case:Orders with missing customer records.

How to Create Joins in Power BI
In Power Query:

Click Transform Data
Select a table
Click Merge Queries
Select second table
Choose join type:
Inner
Left Outer
- Right Outer
- Full Outer
- Left Anti
- Right Anti
Click OK
Expand columns This is where SQL joins happen inside Power BI.

Power BI Relationships
Unlike joins, relationships don’t merge tables—they connect them.
Types of Relationships
1. One-to-Many (1:M)
This is the Most common and the default best practice.
Here One value in Table A relates to many in Table B.
Example
One customer → many orders
One product → many sales

Many-to-Many (M:M) Both tables have duplicate values. **Example: Students ↔ Courses
A student can take many courses
A course can have many students** Problem: This can create ambiguous calculations It is harder to control filtering 3. One-to-One (1:1) Here each value appears once in both tables. Example: User table ↔ User profile table This is rare in real-world analytics

Active vs Inactive Relationships
Active Relationship This is a default relationship used automatically
It is represented by a solid line in Model View
Inactive Relationship Exists but is NOT used unless explicitly activated
Represented by a dashed line

Cardinality
Cardinality defines the structure of the relationship
Power BI usually auto-detects this, but you should always verify it.

Cross-Filter Direction
Defines how filters flow:

Single direction: One table filters another
Both direction: Tables filter each other

How to Create Relationships in Power BI
Method 1: Model View

Go to Model View
Drag one column onto another
Relationship is automatically created Method 2: Manage Relationships
Go to Home → Manage Relationships
Click New
Select:
Tables
Columns
Cardinality
Cross-filter direction
Click OK

Joins vs Relationships (Key Difference)
| Feature | Joins | Relationships |
| ----------- | -------------- | --------------- |
| Where used | Power Query | Data Model |
| Output | Combined table | Separate tables |
| Performance | Heavier | More efficient |
| Flexibility | Less | More |
TIP: Use relationships for analysis and joins for data cleaning

Data Modeling Schemas

Star Schema: A central fact table surrounded by denormalized dimension tables, designed for high-performance querying in business intelligence and data warehouses.
Structure

Customers
|
Products — Sales — Date
|
Stores
Why it’s great?
Fast
Easy to understand
Recommended in Power BI
2. Snowflake Schema
An extension of the star schema where dimension tables are normalized into multiple related tables, reducing redundancy but increasing complexity.
Example:
Customer → City → Country
3. Flat Table (Denormalized / DLAT)
Everything is in one table.
Pros: it is simple
Cons: Large in size, Hard to maintain

Common Data Modeling Issues

Many-to-Many Confusion, Leads to incorrect totals.
Circular Relationships: Power BI blocks these, avoid loops.
Ambiguous Paths: Multiple filter paths lead to wrong calculations.
Poor Naming: Bad names leads confusion later.
Overusing “Both” Filters

Final Thoughts
Data modeling isn’t just technical, it’s strategic.
When you: Choose the right schema, Use relationships properly and
Understand joins, Your Power BI reports become faster, cleaner, and more reliable.

How Excel is Used in Real-World Data Analysis

NelimaL — Tue, 24 Mar 2026 10:46:33 +0000

When I first started learning data analytics, I thought tools like Python or advanced software would dominate everything. But I quickly realized something surprising: Microsoft Excel is still one of the most powerful and widely used tools in real-world data analysis. It’s not just a spreadsheet application it’s a practical, flexible, and accessible platform for exploring, cleaning, and interpreting data.

So, what is Excel?
Excel is a spreadsheet tool that lets you organize data into rows and columns, perform calculations, and create visual reports. That sounds simple, but it goes much deeper than that You can clean messy data, analyze patterns, automate repetitive tasks, and even build small data models all in one place. And the best part is, you don’t need to be a programmer to start using it effectively.

Where Excel shows up in the real world
a) In business and finance, Excel is used to track income, expenses, and profits. People build forecasts and budgets using spreadsheets, helping companies plan ahead.
b) In marketing, teams use Excel to track campaign performance. By analyzing that data, they can figure out what’s working and what’s not.
c) Human resources departments use it to manage employee records, monitor attendance, and even analyze staff perfomance.
d) Even in operations, Excel plays a role in managing inventory and tracking supply chains. It helps businesses stay organized and avoid costly mistakes.

*Excel Features and formulas *

As I’ve been practicing, a few Excel features have really stood out to me because of how practical they are.

1. SUM and AVERAGE
These are basic but essential functions.
=SUM(A1:A10)adds up values.
=AVERAGE(A1:A10) calculates the mean.
In real-world scenarios, these are used to calculate total , average scores, or overall performance metrics.
2. IF Statements
The IF function allows conditional logic:
=IF(A1>50, "Pass", "Fail")
This is useful when categorizing data. For example, a company could use it to label customers as “High Value” or “Low Value” based on their spending.
3. VLOOKUP (and XLOOKUP)
These functions help retrieve data from tables:
=VLOOKUP(A2, A10:B20, 2, FALSE)
In practice, this is used to match and merge datasets like finding a customer’s details based on their ID.
4. Pivot Tables
Pivot tables are one of the most powerful tools in Excel. They allow you to summarize large datasets quickly. For example, you can take thousands of rows of sales data and instantly see:
• Total sales per region
• Sales by product category
• Monthly performance trends This is something analysts use regularly to turn raw data into insights.
5. Charts and Visualization
Excel also allows you to create charts like bar graphs, line charts, and pie charts. These help communicate findings clearly. Instead of just showing numbers, you can visualize trends making it easier for others to understand the data.

Applying this to actual data
What I’m starting to understand is that Excel is not just about knowing formulas, it’s about how you think about data. Before analyzing anything, you often need to clean it. Remove duplicates, fix errors, and make sure everything is consistent. Then you organize it in a way that makes sense.
From there, you can start asking questions:
What’s increasing? What’s decreasing? Is there a pattern? Excel helps answer those questions step by step.

My personal reflection
Learning Excel has definitely changed how I see data. Before, I would look at numbers and not think much about them. Now, I find myself trying to understand what those numbers are actually saying. I pay more attention to patterns, trends, and differences. It’s also made me more curious. Instead of just accepting information, I want to explore it and understand where it comes from. I used to think data analysis was something very technical and complicated. But Excel showed me that it actually starts with simple tools and the ability to think critically.
For me, learning Excel hasn’t just been about spreadsheets, it’s been about learning how to make sense of information in a completely different way.