DEV Community: Faith Rotich

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

Faith Rotich — Wed, 08 Apr 2026 13:43:46 +0000

Introduction
Microsoft Power BI is a powerful business intelligence tool used to transform raw data into interactive dashboards and reports. Once a report is built in Power BI Desktop, the next step is to publish it to the Power BI Service and embed it into a website so others can view and interact with it.
This article walks through the complete process:
• Creating a workspace
• Publishing a report
• Generating embed code
• Embedding the report into a website
• Uploading your .pbix file to GitHub

Overview of the Publishing Process The workflow looks like this:
Build report in Power BI Desktop
Publish to Power BI Service (cloud)
Store report in a workspace
Generate embed code
Embed into a website
Step 1: Create a Workspace
A workspace is where your reports, dashboards, and datasets are stored online.

🔹 Steps

Go to: https://app.powerbi.com
Sign in with your account
On the left panel, click Workspaces
Click + New workspace
Enter: o Name: Electronics Sales Dashboard o Description (optional)
Click Save
Step 2: Publish Your Report

🔹 From Power BI Desktop

Open your Electronics Sales Assignment (.pbix)
Click Home → Publish
Sign in (if prompted)
Select your workspace: o Electronics Sales Dashboard
Click Select
Step 3: Upload .pbix File to GitHub
This ensures version control and submission requirements.

🔹 Steps

Go to GitHub
Create a new repository: o Name: powerbi-electronics-sales
Click Add file → Upload files
Upload your .pbix file
Add a commit message: Added Electronics Sales Power BI report
Click Commit changes
Step 4: Generate Embed Code
There are two main ways to embed:
• Public (Publish to web)
• Secure (for organizations)

🔹 Method: Publish to Web (Public)

Go to your report in Power BI Service
Click File → Embed report → Publish to web
Click Create embed code
Confirm when prompted

Power BI generates:
• Embed link
• HTML iframe code

Example Embed Code:

Step 5: Embed Report into a Website 🔹 Option 1: Basic HTML Website
Open your HTML file
Paste the iframe code:

Electronics Sales Dashboard

Save and open in browser 🔹 Option 2: Blog Platforms (e.g., WordPress)
Open post/page editor
Switch to HTML view
Paste iframe code
Publish
Important Notes on Embedding
Publish to Web is PUBLIC
• Anyone with the link can view
• Do NOT use for sensitive data
✔ Use secure embedding for:
• Company dashboards
• Private data
Key Insights
• Power BI Desktop is used for building reports
• Power BI Service is used for sharing and collaboration
• Workspaces organize content
• Embedding allows reports to be integrated into websites
• GitHub helps with version control and submission tracking

Conclusion
Publishing and embedding reports from Microsoft Power BI transforms your analysis into a shareable, interactive experience. By combining Power BI Service with platforms like GitHub, you not only present insights but also maintain a professional workflow for collaboration and deployment.
Mastering this process is essential for any data analyst, as it bridges the gap between analysis and real-world application.

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

Faith Rotich — Tue, 31 Mar 2026 15:49:35 +0000

Data modeling is at the heart of building performant, flexible, and insightful Power BI reports. Whether you’re designing a simple dashboard or an enterprise-scale model, understanding how data relates—through joins, relationships, and schema design—is essential.

What Is Data Modeling?

Data modeling is the process of organizing data from multiple sources into a logical structure that supports analysis. In Power BI, this means:

• Cleaning and shaping data using Power Query.

• Defining tables (often fact and dimension tables).

• Creating relationships among those tables in the Model view.

• Building measures and visuals that leverage these relationships.

A well-designed model improves performance, reusability, and consistency of your insights.

SQL Joins Explained

Power BI uses SQL-like logic when merging queries in Power Query Editor. Understanding joins helps you know how your tables will combine.

INNER JOIN

Returns only matching rows between two tables.

Example:

Join Sales and Customers where Sales.CustomerID = Customers.CustomerID.

Use case:

You only want sales rows that belong to valid customers.

Diagram:

`A •─────● B ↑ Matching rows only

LEFT JOIN (Left Outer)
Returns all rows from the left table and matching rows from the right.

Example:

All Sales records, even if some customers no longer exist.

Diagram:

A (all rows) ←─── matched B (optional)

Nulls appear where no match exists.

RIGHT JOIN (Right Outer)
Returns all records from the right table, and matching ones from the left.

Example:

All Customers, even if they haven’t made a sale.

FULL OUTER JOIN
Returns all rows from both tables, with nulls where no match exists.

Use case:

Data reconciliation—find mismatched records between systems.

LEFT ANTI JOIN
Returns rows from the left table not matching the right table.

Use case:

Find sales records with no corresponding customer (data quality checks).

RIGHT ANTI JOIN
Opposite of left anti—returns unmatched rows from the right table.

Where to Create Joins in Power BI

In Power Query:
Go to Home → Combine → Merge Queries.

Choose your base table (left).

Select the table to join.

Pick the join kind (Inner, Left Outer, etc.).

Expand resulting columns as needed.

After loading, your joins become part of the query transformation steps.

Power BI Relationships

Once your data is in the model, relationships connect tables logically—similar to joins, but evaluated dynamically at query time.

Relationship Types

Type	Meaning	Example
1:M (One-to-Many)	A single value in one table can relate to multiple rows in another.	One customer → many sales
M:M (Many-to-Many)	Both sides can have duplicates; handled using a bridging table or direct M:M relationship.	Many customers share many accounts
1:1 (One-to-One)	Each row in one table matches exactly one in another.	Customer profile ↔ Customer address

Cardinality and Cross-Filter Direction
• Cardinality defines the relationship type (1:1, 1:M, M:M).

• Cross-filter direction controls how filters flow:

Single: Filters flow from one side (e.g., Dim → Fact).
Both: Filters flow both ways (use carefully—can cause ambiguity).

Active vs. Inactive Relationships

Power BI can store multiple relationships between tables, but only one can be active at a time (solid line in Model View).

Other (inactive) relationships still exist but must be activated in DAX using:

DAX CALCULATE( SUM(Sales[Amount]), USERELATIONSHIP(Sales[OrderDate], Calendar[Date]) )

Scenario: Active relationship on ShipDate, inactive on OrderDate.

Where to Manage Relationships
• Model View: Drag and drop fields to create relationships visually.

• Manage Relationships (Home → Manage Relationships): Create, edit, or delete relationships manually.

• When importing data via DirectQuery, relationships often auto-detect based on column names.

Difference Between Joins and Relationships

Aspect	Joins (Power Query)	Relationships (Model View)
Timing	Applied before data load	Applied after load, at query time
Use	Combine data physically	Connect data logically
Impact	Increases data size	Keeps data separate and dynamic
Analogy	“Data preparation” step	“Semantic modeling” step

Fact and Dimension Tables

A dimensional model organizes data into:

• Fact tables: contain measurable events (e.g., Sales, Orders).

• Dimension tables: contain descriptive attributes (e.g., Customers, Products, Dates).

Example:
• FactSales: ProductID, CustomerID, DateKey, Quantity, Amount.

• DimProduct: ProductID, ProductName, Category.

• DimDate: DateKey, Date, Month, Year.

This design supports intuitive filtering and slicing in reports.

Data Modeling Schemas
Star Schema

Fact table in the center, surrounded by dimension tables.

Diagram:

DimDate | DimProduct — FactSales — DimCustomer | DimRegion

Use case:

Best for Power BI — clean, performant, and intuitive.

Snowflake Schema

Dimensions are normalized into sub-dimensions.

Example:

DimProduct → DimCategory.

Use case:

When dimension tables are large or reused across multiple facts, but slightly more complex for DAX and visualization.

Flat Table (Denormalized / DLAT)
All data combined into a single wide table.

Use case:

Useful for simple datasets or prototypes, not optimal for large models due to redundancy and performance.

Role-Playing Dimensions

A role-playing dimension is one physical table used in multiple roles.

Example:

A single Date table linked to Sales as both OrderDate and ShipDate.

In Power BI:
Duplicate the Date table in Model View (or reference in Power Query).

Rename each instance to match its role.

Create separate relationships to Sales for each role.

Common Data Modeling Issues
• Ambiguous relationships: multiple “both-direction” filters create loops.

• Missing keys / mismatched data types: break relationships.

• Improper granularity: trying to relate tables at different levels (e.g., daily to monthly).

• Too many calculated columns: should use measures instead for efficiency.

Step-by-Step: Building a Data Model in Power BI
Import data (Excel, SQL, etc.) via Get Data.

Clean and transform data in Power Query:

Merge or append queries using joins.
Remove duplicates, fix data types.
Name tables clearly (Fact, Dim). Load to Data Model. Define relationships in Model View:
Set cardinality (1:M).
Adjust cross-filter direction.
Set active relationship if multiple exist. Build measures using DAX (SUM, CALCULATE`, etc.). Validate your model with visuals (Matrix, Card, Slicer).

Conclusion

A solid data model is the foundation of every great Power BI report.

By understanding joins, relationships, and schemas, you can:

• Keep data consistent and query-efficient.

• Avoid ambiguity and circular calculations.

• Empower flexible insights that scale with your organization.

Mastering these fundamentals ensures your Power BI reports are not just visually appealing — they’re analytically reliable.

Would you like me to include visual diagrams (Power BI model view, join examples, and schema structures) for this article? They can make the explanations much clearer.

How Excel is Used in Real-World Data Analysis

Faith Rotich — Fri, 27 Mar 2026 14:56:55 +0000

Introduction

In today’s data-driven world, the ability to analyze and interpret data is a critical skill across industries. While advanced tools like Python and R are often associated with data science, Microsoft Excel remains one of the most widely used and accessible tools for real-world data analysis. Its flexibility, ease of use, and powerful built-in features make it an essential starting point for anyone working with data.

This article explores what Excel is, how it is applied in real-world scenarios, and highlights key features and formulas that make it a practical tool for data analysis.

What is Excel?

Microsoft Excel is a spreadsheet software that allows users to organize, store, manipulate, and analyze data in a tabular format. Data is arranged in rows and columns, forming cells that can contain text, numbers, dates, or formulas.

Excel provides a wide range of functionalities including:

Data entry and storage
Sorting and filtering
Data cleaning and transformation
Statistical analysis
Visualization through charts and dashboards

Because of its versatility, Excel is used across multiple domains such as business, healthcare, education, finance, and logistics.

Excel in Real-World Data Analysis

Business and Sales Analysis

Organizations use Excel to track and analyze sales performance. For example:

A supermarket may analyze monthly sales to identify top-performing products.
A company may track revenue trends and customer purchasing behavior.

Using Excel, analysts can answer questions like:

Which products generate the highest revenue?
What are the sales trends over time?

Healthcare Data Analysis

In healthcare settings, Excel is used to:

Analyze patient records
Track wait times
Monitor health patterns

For instance, a hospital might analyze patient sleep patterns and mental health conditions to identify high-risk groups.

Education Sector

Schools and universities use Excel to:

Track student performance
Analyze exam results
Identify students needing intervention

Supporting Other Data Fields

Excel also plays a foundational role in:

Data Analytics: Exploring trends and patterns
Data Science: Preparing and cleaning datasets before modeling
Artificial Intelligence: Organizing training data and analyzing outputs
Data Engineering: Prototyping data structures and validating datasets
Key Excel Features Used in Data Analysis

Sorting and Filtering

Sorting helps organize data (e.g., arranging ages from lowest to highest), while filtering allows analysts to focus on specific subsets of data.

Example:

Filtering data to show only individuals with PTSD
Sorting sleep hours to identify lowest and highest values

This makes it easier to uncover patterns and isolate relevant data points.

Conditional Formatting

Conditional formatting visually highlights important patterns in data without changing the actual values.

Applications include:

Highlighting sleep hours less than 6 (risk indicator)
Using color scales to visualize high and low values
Identifying missing data or duplicates

This feature is especially useful for quickly spotting trends, outliers, and anomalies.

Data Cleaning

Before analysis, data must be cleaned to ensure accuracy.

Common cleaning tasks in Excel include:

Handling missing values (e.g., replacing blanks with “Unknown”)
Removing duplicates
Standardizing inconsistent entries (e.g., “U.S.” vs “USA”)
Trimming extra spaces using formulas

Clean data ensures reliable analysis and meaningful insights.

Data Validation

Data validation controls what users can enter into a cell, ensuring consistency and accuracy.

Example:

Restricting gender entries to: Male, Female, Non-Binary, Unknown
Setting age limits between realistic values (e.g., 10–110)

This prevents errors and maintains data quality over time.

Important Excel Formulas in Data Analysis

Aggregate Functions

These summarize numerical data:

=SUM(range) → Calculates total
=AVERAGE(range) → Finds the mean
=MEDIAN(range) → Finds the middle value
=MAX(range) / =MIN(range) → Finds highest/lowest values

Application:
Calculating average sleep hours or total sales revenue.

Counting Functions

Used to measure frequency:

=COUNT(range) → Counts numeric values
=COUNTA(range) → Counts non-empty cells
=COUNTIF(range, criteria) → Counts based on condition

Example:
Counting how many participants have PTSD.

Logical Functions

These help categorize and interpret data:

=IF(condition, value_if_true, value_if_false)
=AND(condition1, condition2)
=OR(condition1, condition2)

Example:

=IF(N2<6,"Poor Sleep","Adequate Sleep")

This classifies sleep quality based on hours slept.

Text Functions

Used for cleaning and formatting text data:

=TRIM() → Removes extra spaces
=PROPER() → Capitalizes text properly
=CONCAT() → Combines text

Application:
Cleaning inconsistent entries like names or country labels.

Lookup Functions

These retrieve data from tables:

=VLOOKUP()
=XLOOKUP()
=INDEX() + =MATCH()

Application:
Finding specific records such as customer details or product information.

Pivot Tables: The Core of Data Analysis in Excel

Pivot tables are one of Excel’s most powerful tools for summarizing large datasets.

They allow users to:

Count occurrences (e.g., number of PTSD cases)
Calculate averages (e.g., average sleep per disorder)
Compare categories (e.g., gender distribution across disorders)

Example Use Case:

Rows: Mental Health Disorder
Values: Count of participants

This quickly shows how many people fall into each category.

Pivot tables transform raw data into meaningful insights without altering the original dataset.

Dashboards and Data Visualization

Excel also enables the creation of dashboards—interactive, visual summaries of data.

Dashboards typically include:

Charts (bar, pie, line)
Pivot tables
Slicers for filtering

They help decision-makers quickly understand:

Trends
Comparisons
Key performance indicators

For example, a dashboard might show:

Disorder distribution
Average sleep by gender
Country-level comparisons
Personal Reflection

Learning Excel has fundamentally changed the way I understand and interpret data. Initially, data appeared as a collection of numbers and text with little meaning. However, through tools like sorting, filtering, formulas, and pivot tables, I now see data as a source of insights and decision-making.

Excel has taught me the importance of clean data, structured thinking, and asking the right questions. More importantly, it has shown me that analysis is not just about calculations—it is about uncovering patterns, identifying problems, and telling a story through data.

As I continue developing my skills, Excel serves as a strong foundation for exploring more advanced tools in data analytics and data science.

DEV Community: Faith Rotich

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

Electronics Sales Dashboard

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

How Excel is Used in Real-World Data Analysis