DEV Community: Blinton Kiarie

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

Blinton Kiarie — Mon, 06 Apr 2026 17:26:42 +0000

A complete, step-by-step guide

Introduction to Power BI

In today’s data-driven world, businesses rely on clear, interactive insights to make decisions. Power BI, developed by Microsoft, is a powerful business analytics tool that transforms raw data into visually rich dashboards and reports.

One of its most valuable capabilities is the ability to publish reports online and embed them into websites, making insights accessible to a wider audience—whether internal teams or the public.

This guide walks you through the entire publishing and embedding process, from workspace creation to live website integration.

Step 1: Create a Workspace

A workspace in Power BI is a collaborative environment where reports and dashboards are stored and managed.

Steps:
Log in to Power BI Service (https://app.powerbi.com
)
From the left panel, click Workspaces
Select + New workspace
Enter:
Workspace Name
Description (optional)
Click Save

Step 2: Upload and Publish Your Report

Once your workspace is ready, you need to upload your Power BI report.
Steps:
Open your report in Power BI Desktop
Click Publish (top menu)
Sign in if prompted
Select the workspace you created
Click Select

Alternatively (via browser):

Go to workspace → Click Upload → Select .pbix file

Step 3: Generate the Embed Code

After publishing, Power BI allows you to generate an embed link or iframe.

Important:

There are two main embedding methods:

Public (Publish to Web) → Anyone can view
Secure Embed → Requires authentication
Steps (Public Embed):
Open your report in Power BI Service
Click File → Embed report → Publish to web

Click Create embed code
Copy the generated iframe code
Example Code:

<iframe width="800" height="600"
src="https://app.powerbi.com/view?r=YOUR_REPORT_ID"
frameborder="0" allowFullScreen="true"></iframe>

Step 4: Embed the Report on a Website

Now you can place your report directly into any webpage.

Steps:
Open your website’s HTML file (or CMS editor)
Paste the iframe code where you want the report displayed
Save and publish your webpage
Example:

<div class="report-container">
    <iframe width="100%" height="600"
    src="https://app.powerbi.com/view?r=YOUR_REPORT_ID"
    frameborder="0" allowFullScreen="true"></iframe>
</div>

secure embedding

For internal or sensitive data:

Use Power BI Embedded (Azure)
Requires authentication
Ensures only authorized users can access reports

key insights
What You Learned:
Workspaces organize and manage reports efficiently
Publishing connects Power BI Desktop to the cloud
Embed codes allow seamless integration into websites
Public embedding is easy—but not secure
Tips:
Use responsive iframe sizes for mobile compatibility
Avoid “Publish to Web” for confidential data
Optimize report performance before embedding
Use role-based security for enterprise dashboards

Conclusion

Publishing and embedding a Power BI report transforms static analysis into interactive, accessible insights. Whether you're showcasing dashboards to stakeholders or integrating analytics into a website, this process bridges the gap between data and decision-making.

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

Blinton Kiarie — Tue, 31 Mar 2026 18:19:15 +0000

Introduction

Data modeling is the backbone of any effective Power BI report. It transforms raw, disconnected datasets into a structured format that enables accurate analysis, fast performance, and meaningful insights.

In this article, you’ll learn:

What data modeling is and why it matters
All SQL joins (with examples and diagrams)
Power BI relationships and how they work
The difference between joins and relationships
Fact vs Dimension tables
Data modeling schemas (Star, Snowflake, Flat)
Role-playing dimensions and common pitfalls
Step-by-step instructions to implement everything in Power BI

What is Data Modeling?

Data modeling is the process of organizing data into tables and defining how those tables relate to each other.

Why it matters:

Improves performance
Ensures accurate calculations
Simplifies report building
Enables scalability

Think of it as building a map so Power BI knows how different pieces of data connect.

SQL Joins (Power Query Joins in Power BI)

Joins combine data from two tables based on a common column (key).

In Power BI, joins are created in Power Query Editor using Merge Queries.

1. INNER JOIN

Definition: Returns only matching records from both tables.

Example:
Customers and Orders → only customers who placed orders.

Customers       Orders
ID Name         ID Order
1  A            1  X
2  B            3  Y

Result:
1 A X

Use case: When you only want valid matches.

2. LEFT JOIN (Left Outer)

Definition: Returns all records from the left table + matching from right.

Customers       Orders
1 A             1 X
2 B             -

Result:
1 A X
2 B NULL

Use case: Keep all customers even if they didn’t order.

3. RIGHT JOIN (Right Outer)

Definition: Returns all records from the right table + matching from left.

Customers       Orders
1 A             1 X
-               2 Y

Result:
1 A X
NULL 2 Y

Use case: Keep all orders even if customer info is missing.

4. FULL OUTER JOIN

Definition: Returns all records from both tables.

Customers       Orders
1 A             1 X
2 B             -
-               3 Y

Result:
1 A X
2 B NULL
NULL 3 Y

Use case: Data reconciliation.

5. LEFT ANTI JOIN

Definition: Rows in left table with NO match in right.

Customers       Orders
1 A             1 X
2 B             -

Result:
2 B

Use case: Find customers with no orders.

6. RIGHT ANTI JOIN

Definition: Rows in right table with NO match in left.

Customers       Orders
1 A             1 X
-               2 Y

Result:
2 Y

Use case: Identify orphan records.

How to Create Joins in Power BI

Open Power BI Desktop
Click Transform Data (Power Query)
Select a table
Click Merge Queries
Choose second table
Select matching columns
Choose join type:

Inner
Left Outer
Right Outer
Full Outer
Left Anti
Right Anti
1. Click OK
2. Expand merged columns

Power BI Relationships

Unlike joins, relationships do NOT merge tables. They define how tables interact at query time.

Types of Relationships

1. One-to-Many (1:M)

Most common
Example: Customers → Orders

CustomerID (1) → (M) Orders

2. Many-to-Many (M:M)

Both sides have duplicates
Example: Students ↔ Courses

Use carefully—can cause ambiguity.

3. One-to-One (1:1)

Rare
Example: User ↔ Profile

Cardinality

Defines relationship type:

One-to-Many
Many-to-One
Many-to-Many
One-to-One

Active vs Inactive Relationships

Active: Used by default
Inactive: Must be activated using DAX

Example:

Order Date (active)
Ship Date (inactive)

Use:

CALCULATE(SUM(Sales[Amount]), USERELATIONSHIP(Date[Date], Sales[ShipDate]))

Cross Filter Direction

Controls how filters flow:

Single Direction

One → Many
Best for performance

Both Direction (Bi-directional)

Filters flow both ways
Useful but can create ambiguity

How to Create Relationships in Power BI

Method 1: Model View

Go to Model View
Drag a field from one table to another
Relationship auto-created

Method 2: Manage Relationships

Click Manage Relationships
Click New
Select tables and columns
Choose:

Cardinality
Cross filter direction
Active/Inactive
1. Click OK

Joins vs Relationships

Feature	Joins (Power Query)	Relationships (Model View)
When applied	Data load time	Query time
Output	Merged table	Separate tables
Performance	Can increase size	More efficient
Flexibility	Less flexible	Highly flexible

Best practice: Use relationships over joins unless necessary.

Fact vs Dimension Tables

Fact Table

Contains measurable data
Example: Sales, Transactions

Sales:
OrderID | ProductID | Amount | Date

Dimension Table

Descriptive attributes
Example: Customer, Product, Date

Product:
ProductID | Name | Category

Data Modeling Schemas

1. Star Schema

Fact table in center
Dimension tables around it

     Product
        |
Customer — Sales — Date

Advantages:

Fast
Simple
Easy to maintain

2. Snowflake Schema

Dimensions are normalized

Product → Category → Department

Advantages:

Reduces redundancy

Disadvantages:

More complex
Slower queries

3. Flat Table (Denormalized / DLAT)

Everything in one table

Sales + Customer + Product

Advantages:

Simple setup

Disadvantages:

Large size
Poor performance
Hard to maintain

Role-Playing Dimensions

A single dimension used multiple times.

Example: Date Table

Order Date
Ship Date
Delivery Date

Solution:

Create multiple relationships
Only one active
Use DAX USERRELATIONSHIPS for others

Common Data Modeling Issues

1. Ambiguous Relationships

Multiple paths between tables
Causes incorrect results

2. Many-to-Many Misuse

Leads to double counting

3. Bi-Directional Filters Overuse

Slows performance

4. Missing Keys

No proper joins/relationships

5. Circular Relationships

Causes model errors

Conclusion

Mastering data modeling in Power BI is essential for building scalable, high-performance reports. Understanding joins helps in data preparation, while relationships power dynamic analysis.

By using the right schema, defining proper relationships, and avoiding common pitfalls, you can transform your Power BI reports from basic dashboards into powerful analytical tools.

How Microsoft Excel is Used in Real-World Data Analysis

Blinton Kiarie — Sun, 29 Mar 2026 18:21:08 +0000

EXCEL

Definition:Microsoft Excel is a spreadsheet program developed by Microsoft that allows you to Collect, Organize, analyze and visualize data using tables and charts.

Through examples I will explain to you on how data analysts' use Ms Excel on the day to day basis when working with datasets

DATA ORGANIZATION

Many companies deal with very large amounts of data and when it comes to organizing and cleaning the data, thats where Excel comes in.
Many data comes very messy ,unstructured and with missing values and it needs to be formatted and cleaned to get better structured datasets for analysis
WAYS TO FORMAT AND CLEAN DATA

One of the ways to clean your data is by Remove duplicate
To remove duplicates you simply Select your dataset, go to the Data tab
click Remove Duplicates
below is an example

changing columns to their respective data types

Data analysis with formulas

Excel allows users to perform calculations quickly
Below are the different types of formulas used in excel by majority of data analyst
COUNTIF
Is a function used to count the number of cells that meet a specific condition.

SUMIFS this is mostly used while dealing with multilple columns

Pivot tables

PivotTables are one of Excel’s most powerful features that help in
summarizing large datasets, grouping data by categories and compare trends

Data visualization

Excel can turn raw data into charts:

Bar charts → compare categories
Line charts → track trends over time
Pie charts → show proportions

CONCLUSION

Excel is so popular because its easy to learn but very powerful
and it works for small and large datasets with no coding required (but supports it) but most important of it is widely used across industries

Introduction to MS Excel for Data Analytics

Blinton Kiarie — Sun, 25 Jan 2026 16:16:42 +0000

MICROSOFT EXCEL

Definition this is one of the most used tools in the corporate world to store clean and organize data .in ourcase we will be using it in a data analytics point of view and how it can help and simplify our analysis

EXCELS INTERFACE AND FUNCTIONS

Once you open your Microsoft excel you will have to know the difference between a Row , column , and a cell
The picture below illustrates more

column this is the highlighted part of the worksheet that goes downwards
row this is the highlighted part of the worksheet that goes across
cell this is where the row and column meet and its where we record our data

BASIC FUNCTIONS IN MS EXCEL

We use functions to simplify on finding certain values easily with no struggle when dealing with large datasets
There are various common functions that are the mostly used in excel that a beginner should grasp when starting off
listed below are various functions used and will further explain some of them

. SUM
. AVERAGE
. COUNT
. COUNTA
. COUNTIF
. SUMIF
. MAX
. MIN
. IF

SUM

This function is basically for doing the normal addition of numbers
that we do on a normal maths class or on our day to day life while counting our money
below is the formula used for sum

Above is the total bonus of all employees using the SUM formula

AVERAGE

This formula helps you find the mean performance for any column in a data
as illustrated below

so we basically calculated the average age of the employees

MAX

This basically identifies or finds the highest performance

In the above screenshot we were looking for the max of the project count column

IF

This makes decisions of classifies data in different categories
for example you can decide to promote or demote different personnel according to a certain performance

Above screenshot describes that if a certain employee project count is 15 or less than 15 is retained at the position and those above 15 will be promoted

COUNTIF

This formula helps in counting how many cells meet a condition that you specifically want
for example we can decide to count how many peoples project count were above 15

This shows that 186 people's project count were above 15

CONCLUSION

This are just a few of the basic functions as a beginner you should know.
Take time to learn on the other functions listed and many more
All it takes is practice and you will have fun analyzing any data
Thats it for my beginner friendly introduction to excel

ADIOS

GIT BEGINNERS GUIDE(understanding version control and how to push and pull a code)

Blinton Kiarie — Sat, 17 Jan 2026 15:41:59 +0000

UNDERSTANDING GIT VERSION CONTROL

Git is one of the version control systems used to track changes in files or set the files over time
For Git to work perfectly it needs platforms that host it's repositories online for various reasons like collaboration, management etc
There are various platforms that host git repositories but the commonly used platform is GitHub

Git and GitHub installation and connection

First check if you have git installed by simply going to your command system and type the below command

git --version

If not installed you can simply go to your browser and search git and make sure you install it according to the operating system your machine is using e.g windows, linux, mac
next step is setting up your username and email using the below codes

git config --global user.name "yourname"
git config --global user.email "youremail"

Check settings using the below code

git config --list

The next step is connecting your git to your repository platform for our case we will be connecting to GitHub
To connect your git to your GitHub you have to have created an account with GitHub
So simply go to your browser and search for GitHub then sign up with your Gmail account, enter your details i.e name, email, bio, location etc
So connecting your git to your GitHub you need to generate and SSH KEY using your Git by entering the below command

ssh-keygen -t ed25519 -C "your email"

Start SSH Agent by using below command

eval "$(ssh-agent -s)"

after generating it copy the SSH KEY and link it with your GitHub account by
just simply go to your GitHub account, click your profile, go to settings, click on SSH and GPG keys, click on new SSH key and paste the generated SSH key.

HOW TO PUSH AND PULL A CODE

In order to push a code you first Check what changed by using the below command

git status

the next step is adding the file for the changes by using the following command

git add .

then you can easily write what you changed by using this command

git commit -m "Describe what you changed"

next thing is Pushing the file to the remote repository. for our case we are using github

git push origin main

The next thing is how to pull the code to download the changes we've made

git pull origin main

Conclusion
This just a beginner friendly article
on the basic overview of git
by an aspiring data analyst
thank you