DEV Community: Joseous Ng'ash

A comprehensive guide to Publishing And Embedding Power BI Reports on Website with iFrames.

Joseous Ng'ash — Mon, 06 Apr 2026 17:11:50 +0000

What exactly is Microsoft Power BI?

In simple and understandable terms, it refers to business analytic tool by Microsoft that helps you visualize data, create reports and share insights.

Key components

Power BI Desktop – Build reports on your computer.
Power BI Mobile – View reports on phones/tablets.
Power BI Service – Cloud platform for sharing and collaboration.

Why Use Power BI?

It provides real time analytics
It is easy to share and embed
It can connect to multiple data sources
Drag and Drop dashboards

Power BI Publishing

Power BI publishing means moving your report from Power BI Desktop to Power BI Service(cloud) so others on the same team can access it.

Power BI Report Publishing Overview

Highlight Of Steps Involved In Power BI Report Publishing

Create report in Power BI Desktop
Save your report
Click publish on Power BI desktop
Sign in to publish
Choose workspace
Upload to Power BI Service
Access via browser

Detailed Power BI Publishing steps

STEP ONE

Create Report In Power BI Desktop
To create Power BI reports, you have to import data, clean and transform the data then build visuals.
Importing Data; Data can be imported from different sources, eg excel as flat file or excel workbook, different database, SQL server.
One way to import data, open Power BI desktop and it will open to home page where you have different options to select from to get data.
Home Panel with different data sources

Another way to import data to create Power BI reports, click on Get Data on the far left on navigation panel/home panel and click on your preferred data source or depending on where the data you are working with is saved.

Get Data Option to import data to power BI desktop

After selecting your preferred data source, click connect located on the bottom right of Get Data navigation panel and wait for the data to be connected from source then click the connected table to select and preview the data.
In this demo I used an Excel Workbook as my data source.

What to expect after connecting to your preferred data source, my table is called Sheet1 containing detailed imported data

Data Cleaning And Transformation(Power Query)
Raw data is often messy -- Missing Values, Inconsistent format, Duplicates. This process of data cleaning and transforming ensures the report is accurate and reliable. In simple terms data cleaning and transforming in Microsoft power BI is the process of preparing raw data so it becomes accurate, consistent, and ready for analysis.

Data Cleaning Involves

Removing Duplicates: This ensures each record is unique and avoid double counting during analysis. To start data cleaning, on the navigator on the bottom right click on Transform option which will take you direct to Power Query What is Power Query: It is a tool used to connect, clean, transform, and load data into Power BI for analysis.

Transform Option On Navigator

Power Query Interface

Main sections in Power Query
1️⃣ Ribbon(Top Menu): It gives access to all transformation tools and commands, key tabs includes;

Home → load data, close and apply
Transform → Change data types, pivot/unpivot
View → Toggle panels (like formula bar)
Add Column → Create calculated columns

2️⃣ Queries Pane (Left Side): it contains a list of all tables which help in navigating between different datasets and organize queries(rename, group or duplicates)

3️⃣ Data Preview (Center Table View): it serve the purpose of viewing and interacting with your dataset and applying transformation directly on columns.

4️⃣ Applied Steps Pane (Right Side): it shows all applied steps during data cleaning and transformation like removed columns, filter row or change type.

5️⃣ Query Settings Panel (Right Side – Top): it is located on top of applied steps, which is used to rename query and add description.

6️⃣ Formula Bar: it is used to display formulas and uses M Language

7️⃣ Column Headers: this is the top row of every column which offers quick transformation through dropdown menus.

Handling Missing Data: This helps in replacing missing values with Nulls, Not Provided, Missing, N/A or remove incomplete rows.
Fixing Data Types: Convert columns into correct formats eg number to text or text to date.
Select on the column you want to change data type and right click then select Change type and select to the type you want to change your data into(decimal, data, text, number amongst others) as part of data cleaning.

Renaming Columns: make a column make sense or meanifull.

Data Transformation
This involves reshaping data for analysis and visualization. The process involves different tasks which includes:

Data filtering
Creating new columns
Grouping and aggregating
Pivoting and unpivoting
Splitting and merging columns
Data sorting

After the data is imported, cleaned and transformed. The data is ready to be used to Build Visual.
Reports visuals are prepared in Power BI. To make sure the cleaned data is loaded to Power BI, in the Power Query to save changes, click on Home Tab and then click Close and Apply button to save data and load to Power BI directly

Close and Apply Button In Power Query Editor

After Power Query is closed and changes saved, the data is loaded to Power BI to build visual reports.

Report View In Power BI Home Page

It displays table contain our data, panel where visuals appear, filter panel, visualization panel with different charts, a ribbon for different commands and functionality.

Building Visuals In Power BI
After closing Power Query and the cleaned and transformed data is loaded to Power BI, the next step is to use DAX (Data Analysis Expressions) to create necessary measure to help in data analysis.

What Is DAX (Data Analysis Expressions): is a formula language used in Power BI and Analysis Services to perform data calculations and analysis.
Example Of DAX

Profit margin = DIVIDE(SUM(Electronics_sales[Profit]), SUM(Electronics_sales[SalesAmount])) * 100

After we have done the necessary calculations, the next step is choosing the right charts to represent the analysis visually.
Some of the available charts in Power BI include;

Bar Charts
Column charts
Pie charts
Line charts

Charts panel in Power BI

After creating required KPIs and visualizing the report, the next step is publishing to Power BI service.

Complete Power BI Report Looks like demonstrated below.

STEP TWO

Saving Power BI Report(.pbix format)
After you have completed the Power BI Report, the next step is saving the report in the appropriate format.
Always save your Power BI report with extension **.pbix format.
Power BI file save in local machine

STEP THREE

Click Publish
In Power BI desktop home tab Click Publish on the top right of the application.

STEP FOUR

Sign In
After clicking publish button, you will be prompted to Sign In. Use your Microsoft Account to sign in or school email for students and organization account for the organization you are working for for collaborations.
Sign In Window from Power BI Desktop

STEP FIVE

Choose Workspace
After you have provided the Sign In credentials, next you choose the workspace already created from Power BI Service.
Window to choose your workspace from

STEP SIX

Upload to Power BI Service
After choosing workspace, click select to upload to Power BI Srvice.
Select Option

🌐 Embedding Power BI Reports on the Web (iFrame)

After publishing the Power BI report from Desktop, this are the step involved in Embedding report on the web

Go to Power BI service and sign in with your account information.
Go to Workspace where you published power bi file and open your report. Published Power BI report on Power BI service

Click: File → Embed report → Website or Portal

Confirm and generate embed code

Copy the generated iFrame code to code editor like Vs code Copied code in VS code

Save the embedding code to a folder in your PC and open the index.html using your preferred browser and share the link to your team or organization to access the Power BI report.

Conclusion

Creating visuals in Power BI desktop is just the beginning which equips you with data understanding skills and data analysis to derive business insights. After the report is complete, it is only accessible to you and any other person using your computer because it is only available into local machine. To make sure all involved parties get to see the final report for decision making, you have to go an extra mile into publishing it to Power BI service and share the link to the web to relevant parties. For any individual who aspire to be data analyst, it is important to understand the process of publishing the report and embedding into a website.

Power BI Data Modeling.

Joseous Ng'ash — Tue, 31 Mar 2026 08:02:15 +0000

What is Data Modeling in Power BI

In simple and understandable terms, Data Modeling is simply a way to organize, connect and structure your data in a way it can be easily analyzed and visualized.

Raw data = Messy datasets
Data modeling = organize and arrange the dataset into meaningful structure

Data Modeling in Power BI: Creating relationships between tables and structuring data into a format that enables accurate analysis and reporting.

Data Modeling Involves

Creating relationship
Defining structure(Schema)
Organizing Data into tables
Setting rules(Cardinality & Filters)

Data modeling is important because;

We can derive accurate analysis
We can get better insights
Can create clear and accurate dashboards

Power BI uses some of components to help in data modeling

Main Components of Data Modeling in Power BI.

Joins In Power BI
Joins are used when combining tables into one (This is usually done in Power Query Editor).

Types Of Joins In Power BI

Inner Join: It only returns matching rows in both tables and it is used when you only want related data. How Inner join looks like.

Left Outer Join: This is the most commonly used join. It returns all rows from the left table plus matching rows from the right.

Right Outer Join: This join act opposite of the Left Outer Join. It returns all rows from the right table plus matching rows from the left table.

Full Outer Join: This type of join in power bi, returns all the rows from both table, matched or unmatched.

Anti Join(Left/Right): This type of joins are used to return the unmatched rows, usually help in finding missing data.

Relationships In Power BI
In Relationship tables are connected without merging them. Instead of combining data physically, Power BI links tables logically.

Key Concept

Primary Key → Unique column (e.g., ProductID in Products)
Foreign Key → Repeated column (e.g., ProductID in Sales)

Cardinality: defines how table relate, example;

One-to-many(1:):* commonly used relationship, meaning one product many sales.
Many-to-one(:1):* it is the same as one-to-many but reversed.
One-to-one(1:1): this relationship is rarely used during data modeling.
Many-to-many(:): this type of relationship create ambiguity and should be used carefully.

Cross Filter Direction

Single direction: the filter flow from one direction and its the recommended filter.
Both Direction: the filter flows both ways though it can cause confusion if misused.

Schemas (Data Modeling Structures)
Schemas define how your tables are organized.
Star Schema (best practice)
This is the recommended approach in Power BI.

Structure
Fact Table: contains measurable data eg sales, revenue, quantity.
Dimension Tables Eg customers, dates, products

Benefits of star schema includes;

It is easy to understand
Better performance in Power BI
It is easy and fast

Snowflake Schema
This is a more complex version of star schema

Structure
In this type of schema, dimension tables are split into Sub-Tables though it is slow in performance and harder to manage.

Flat Table (Single Table)
This type, the data is in one table and has poor performance, hard to manage and also data redundancy.

Conclusion

Data modeling in Power BI is the process of organizing data into structured tables and creating relationships between them to enable accurate analysis and reporting. It involves arranging data in an efficient format, such as a star schema, to improve performance and ensure that filters and calculations work correctly. Overall, good data modeling helps transform raw data into meaningful insights for better decision-making.

Excel Real world Applications

Joseous Ng'ash — Fri, 27 Mar 2026 11:44:09 +0000

HOW EXCEL IS USED IN REAL WORLD DATA ANALYSIS

What really is Excel, in simple terms it is just an application from Microsoft to help ordinary people to keep their records either groceries records, building materials records, names of people traveling, basically anything involving records keeping for future references.
But for professionals like Data Analytics it is a software used to organize, analyze and visualize data. It is like a digital notebook comprising of columns and rows where you can:

create charts
perform calculations
store data of different data type eg; text, dates, numbers

Excel worksheet is made of collection of cells.

What you can do with Excel

Data analysis
Data Visualization
Automation
Data Organization
Perform Calculations

How Excel is Used in Real-world:

Track monthly expenses either by an individual or in an industrial set up.
Widely used in finance, engineering, business and data analysis.
Used to clean and prepare data before its used in other tools like SQL and Python.
It is an essential skill for careers like accountant, data engineer and data analyst.
Analyzing products performance and help in decision making in a company.
Create reports to derive clear insights.

Key Features found in Microsoft Excel

Charts And Graphs

Charts and graphs are used for data visualization which help in creating dashboard reports, some examples of charts and graphs includes:

Bar Charts
Line Graphs
Pie Charts

Example of a Pie and Bar Chart

An example of dashboard reports created from different charts and graphs

Sorting And Filtering

It is a feature used to Analyze large datasets quickly, you can either sort data from largest to smallest, smallest to largest or filter specific data.
Filter marks are always located on top of every column but are activated from the tab editing under sort and filter.

Filter Marks on every column to filter specific data

Pivot Tables

It is one of the powerful feature used for analysis in Excel

Example of how pivot table is used in data analysis

Conditional Formatting

The feature automatically changes the color of cell based on highlight rule either;

High/Low values
Duplicates
Trends Example of conditional formatting used to highlight top most performing product, using Top/Bottom Rule

Data Validation

The feature helps to keep data clean and consistent.

Data Organization

Helps make the data readable and manageable by;

Storing data in rows and columns
Format as table
structured referencing

Formulas And Functions

This feature helps excel to perform calculations using formulas
Formulas and functions are excel built-in features which are activated by typing equal sign (=) followed by specific formula or function example (VLOOKUP()) on any excel cell containing data to be filtered, aggregated or edited.

Basic operators in Excel: +,-,*,/
Functions:
- SUM() adds Values
- AVERAGE() finds mean
- IF() logical decisions
- VLOOKUP()/XLOOKUP() search data

Import And Export Data

With this feature will help in importing data from CSV files and Databases, and Export to a different formats

Conclusion
Learning Microsoft excel has transformed how I understand and interpret data, before data felt overwhelming, I would look at dataset without knowing how to extract useful insights.

Features like formulas and functions helped move far from manual calculations and start focusing on what dataset actually represent.

features like filtering, pivot table and sorting showed me data is not just static, it can be explored from different perspectives to reveal hidden insights.

Excel has also made me improve on data handling, the importance of missing values, ensuring data validation. Working with any dataset has made me more careful knowing small errors can lead to incorrect conclusions.

Overall, learning Excel has helped in a way that when I look at the data , I can understand, question and develop analytical reports to the organization for better decision making.