DEV Community: Audrine Marion

How to publish a Power BI report and embed it into a website.

Audrine Marion — Sun, 05 Apr 2026 15:58:02 +0000

Microsoft Power BI is a powerful business intelligence tool that enables users to transform raw data into interactive dashboards and reports. One of its most useful features is the ability to publish reports to the Power BI Service and embed them into websites for sharing insights with a wider audience.

This guide walks through the full process step by step: creating a workspace, publishing a report, generating embed code, and embedding the report into a website.

Step 1: Create a Workspace in Power BI Service

A workspace is a collaborative environment where reports, dashboards, and datasets are stored before publishing or sharing.

Steps:

Sign in to Power BI Service (https://app.powerbi.com).
From the left navigation panel, select Workspaces.
Click New workspace.
Enter a workspace name and description.
Click Save.

Then proceed to add New Workspace

Why this matters:
Workspaces help organize reports and control access permissions for collaboration and publishing.

Step 2: Upload and Publish Your Report

Once your workspace is ready, the next step is publishing your Power BI Desktop report.

Steps:

Open your report in Power BI Desktop.
Click File → Publish or select Publish from the Home ribbon.
Choose your created workspace.
Click Select.
Wait for the confirmation message indicating successful publishing.

Why this matters:
Publishing moves your report from local development to the cloud where it can be shared and embedded.

Step 3: Generate the Embed Code

After publishing the report, you can generate embed code from Power BI Service.

Steps:

Open Power BI Service.
Navigate to your workspace.
Select your report.
Click File → Embed report → Publish to web (public).
Confirm the security warning.
Copy the generated HTML iframe embed code.

Why this matters:
The iframe embed code allows your report to be displayed inside a webpage.

Step 4: Embed the Report on a Website

Once the embed code is generated, you can place it inside your website's HTML.

Example HTML Code:

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

Steps:

Open your website HTML file.
Paste the iframe code where the report should appear.
Save changes.
Refresh your webpage.

Why this matters:
Embedding enables stakeholders and users to interact with dashboards without logging into Power BI.

Key Insights and Best Practices

Publishing and embedding Power BI reports makes insights accessible to a broader audience. However, it is important to understand visibility settings before sharing.

Key takeaways:

Workspaces help manage report access and organization.
Publishing moves reports from Desktop to Power BI Service.
Embed codes allow reports to be integrated into websites.
"Publish to web" makes reports publicly accessible, so sensitive data should not be included.

Conclusion

Publishing and embedding Power BI reports is a straightforward process that significantly enhances data accessibility and collaboration. By following these steps, you can successfully move your report from Power BI Desktop to a live website and share insights interactively with your audience.

Adding screenshots at each step improves clarity and helps users follow the workflow more easily.

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

Audrine Marion — Sun, 29 Mar 2026 20:30:09 +0000

Data modeling is the backbone of every effective Power BI report. If dashboards feel slow, filters behave incorrectly, or numbers don’t match expectations, the issue is often the data model not the visuals.

This guide explains how data modeling works in Power BI step‑by‑step. You’ll learn SQL joins, relationships, schemas, fact vs dimension tables, role‑playing dimensions, and how everything is created inside Power BI itself.

This article is beginner‑friendly but structured like a professional BI reference.

What is Data Modeling?

Data modeling is the process of organizing tables and defining how they relate so reports are accurate, scalable, and fast.

In Power BI, good data modeling helps you:

connect multiple datasets
control filter behavior
improve performance
enable time intelligence
prevent duplicate counting
support executive‑level reporting

Power BI primarily uses relationships instead of joins during analysis.

SQL Joins Explained (With Real Examples)

SQL joins combine tables physically in Power Query before loading data into the model.

Location in Power BI:

Transform Data → Merge Queries

INNER JOIN

Returns only matching records in both tables.

Example:

Customers Table

Orders Table

Result: Only customers who placed orders appear.

Real‑life analytics use case:

Analyzing purchasing customers only.

LEFT JOIN (LEFT OUTER JOIN)

Returns all rows from the left table and matching rows from the right.

Use case:

Customer engagement analysis including inactive customers

RIGHT JOIN (RIGHT OUTER JOIN)

Returns all rows from the right table and matching rows from the left.

Use case:

Transaction completeness audits

FULL OUTER JOIN

Returns all rows from both tables.

Use case:

Data reconciliation between systems

LEFT ANTI JOIN

Returns rows from the left table with no matches in the right table.

Use case:

Customer churn targeting

RIGHT ANTI JOIN

Returns rows from the right table with no matches in the left table.

Use case:

Data quality audits

Relationships in Power BI

Relationships connect tables logically instead of physically merging them.

Location:

Model View → Drag column between tables

OR

Home → Manage Relationships → New

Types of Relationships

One‑to‑Many (1:M)

Most common relationship type.

Example: DimCustomer → FactSales

DimCustomer (1)
┌──────────────┐
│ Customer ID │
│ Customer Name│
│ Region │
└──────┬───────┘
│
│
│
▼
FactSales (Many)
┌──────────────┐
│ Order ID │
│ Customer ID │
│ Sales Amount │
│ Quantity │
└──────────────┘
One-to-Many relationship: One customer can appear multiple times in the sales table.

Many‑to‑Many (M:M)

Occurs when both tables contain duplicate keys.

Example: Students ↔ Courses

Students
┌──────────────┐
│ Student ID │
│ Student Name │
└──────┬───────┘
│
│
▼
Enrollment (Bridge Table)
┌──────────────┐
│ Student ID │
│ Course ID │
└──────┬───────┘
│
│
▼
Courses
┌──────────────┐
│ Course ID │
│ Course Name │
└──────────────┘
Many-to-Many relationship resolved using a bridge table between Students and Courses.

One‑to‑One (1:1)

Rare but useful.

Example: Employee table ↔ Employee security table
Employees
┌──────────────┐
│ Employee ID │
│ Name │
│ Department │
└──────┬───────┘
│
│
▼
EmployeeSecurity
┌──────────────┐
│ Employee ID │
│ Access Level │
│ Login Role │
└──────────────┘
One-to-One relationship: Each employee record matches exactly one security profile.

Active vs Inactive Relationships

Power BI allows multiple relationships between tables but only one active at a time.
DimDate
┌──────────────┐
│ Date │
└──────┬───────┘
│
(Active) ──┼──────── OrderDate
│
(Inactive) ─ ─┼──────── ShipDate
│
(Inactive) ─ ─┼──────── DeliveryDate
│
FactSales

Active relationship (solid line) filters visuals automatically, while inactive relationships (dashed lines) require USERELATIONSHIP() in DAX.

Cardinality Explained

Cardinality describes table relationship structure:

One‑to‑Many, Many‑to‑One, Many‑to‑Many, One‑to‑One

Location:

Manage Relationships → Cardinality dropdown

Cross Filter Direction

Controls how filters move between tables.

Single Direction (recommended) vs Bi‑Directional

Difference Between Joins and Relationships

Power Query Joins: Combine tables physically, run before loading, increase table size
Relationships: Connect tables logically, run after loading, improve performance

Fact vs Dimension Tables

Fact Tables contain numeric values (Sales Amount, Revenue, Quantity)

Dimension Tables contain descriptive attributes (Customer Name, Product Category, Region)

Star Schema (Recommended Model)

Fact table at center, dimension tables surrounding.

Fast performance, simple relationships, scalable dashboards.

Snowflake Schema

Dimensions split into multiple related tables.

Reduced redundancy but more complex filtering.

                DimDepartment
              ┌──────────────┐
              │ Dept ID      │
              │ Dept Name    │
              └──────┬───────┘
                     │
                     │
                DimCategory
              ┌──────────────┐
              │ Category ID  │
              │ Category Name│
              └──────┬───────┘
                     │
                     │
                DimProduct
              ┌──────────────┐
              │ Product ID   │
              │ Product Name │
              └──────┬───────┘
                     │
                     │
             ┌───────▼────────┐
             │    FactSales   │
             │ Sales Amount   │
             │ Quantity       │
             │ Revenue        │
             │ Order ID       │
             └───────┬────────┘
                     │
                     │
                DimCustomer
              ┌──────────────┐
              │ Customer ID  │
              │ Customer Name│
              │ Region       │
              └──────────────┘

Flat Table (Denormalized / DLAT Model)

All fields stored inside one table.

Simple setup but poor scalability.

             Flat Table (Sales Dataset)
        ┌───────────────────────────────┐
        │ Order ID                      │
        │ Order Date                    │
        │ Customer ID                   │
        │ Customer Name                 │
        │ Region                        │
        │ Product ID                    │
        │ Product Name                  │
        │ Category                      │
        │ Department                    │
        │ Sales Amount                  │
        │ Quantity                      │
        │ Revenue                       │
        └───────────────────────────────┘

Example Flat Table (DLAT Model): All descriptive and transactional fields stored inside a single table without relationships between dimensions.

Role‑Playing Dimensions

One dimension reused multiple times (e.g., Date table as Order Date, Ship Date, Delivery Date).

Common Data Modeling Mistakes in Power BI

Circular relationships
Duplicate keys
Many‑to‑many misuse
Too many bi‑directional filters
Over‑joining tables in Power Query

Step‑by‑Step Modeling Workflow in Power BI

Home → Get Data
Transform Data → Power Query
Merge tables if required
Create relationships
Validate cardinality
Test filter flow

Generally

Strong data modeling transforms dashboards into decision systems.

Master joins, schemas, and relationships to move from report builder to analytics engineer.

How Excel is Used in Real-World Data Analysis

Audrine Marion — Mon, 23 Mar 2026 16:17:18 +0000

Introduction

Excel is one of the most widely used tools for data analysis across industries. Although newer technologies like Python, SQL, and Power BI are becoming more common, Excel remains a foundational skill for anyone working with data. It allows analysts to organize information, clean datasets, perform calculations, explore patterns, and present insights in a structured and accessible way.

Because of its flexibility and ease of use, Excel is often the first tool professionals rely on when working with raw data before moving into more advanced analytics environments.

What is Excel?

Excel is a spreadsheet application developed by Microsoft that allows users to store, organize, calculate, and analyze data using rows and columns. It provides built-in formulas, visualization tools, Pivot Tables, and automation features that help transform raw numbers into meaningful insights.

In real-world data analysis, Excel is commonly used for:

Cleaning messy datasets
Performing calculations
Summarizing large datasets
Identifying trends and patterns
Creating dashboards and reports
Supporting business decision-making

Real-World Applications of Excel in Data Analysis

1. Data Cleaning

Before analysis begins, datasets often contain missing values, duplicates, or formatting issues. Excel helps analysts prepare datasets using tools such as:

Remove Duplicates
Find and Replace
Text-to-Columns
Sorting and Filtering

These features ensure that data is accurate and ready for analysis.

2. Data Summarization Using Pivot Tables

Pivot Tables are one of Excel’s most powerful tools. They allow analysts to quickly summarize large datasets and answer important questions such as:

Which product generated the highest revenue?
Which region had the most customers?
Which month recorded the highest sales?

Instead of manually calculating totals, Pivot Tables automatically group and summarize information.

example of pivot table showing the sum of bonus by location

Pivot Tables help analysts transform raw datasets into structured summaries within seconds.

3. Using Formulas for Analysis

Excel formulas help analysts perform calculations efficiently and accurately.

SUM Function

Used to calculate totals such as total sales or expenses.

Example:

=SUM(B2:B100)

Used in scenarios like:

calculating monthly expenses
total company revenue
inventory totals

IF Function

Used to apply logical conditions to categorize data.

Example:

=IF(B2>50,"Pass","Fail")

Real-world uses include:

grading systems
customer segmentation
performance classification

VLOOKUP Function

Used to retrieve matching values from another table.

Example:

=VLOOKUP(A2,Sheet2!A:B,2,FALSE)

Analysts commonly use this when:

merging customer records
matching product IDs
combining datasets from multiple sheets

4. Data Visualization

Excel allows analysts to create charts such as:

Bar charts
Line charts
Pie charts
Column charts

These visualizations make it easier to communicate findings clearly to stakeholders who may not have technical backgrounds.

Example Excel line chart showing average salary across month of hire.

5. Conditional Formatting for Insight Discovery

Conditional formatting highlights important values automatically.

Example uses:

highlighting top-performing regions
identifying missing values
detecting unusually high expenses
spotting declining performance trends

Example of conditional formatting that highlights performance score greater than 5

This improves readability and makes patterns visible immediately.

Features I Have Learned and Applied When Working with Data

While learning Excel, I gained experience using several important features that support data analysis workflows.

Pivot Tables helped me summarize datasets quickly and identify trends without writing complex formulas.

The IF function allowed me to categorize data into meaningful groups, making it easier to interpret results.

VLOOKUP helped me combine information from multiple tables, which is especially useful when working with relational datasets.

Conditional formatting helped highlight important values such as high-performing regions or missing data points, making datasets easier to understand visually.

These tools significantly improved my ability to explore and interpret datasets efficiently.

Personal Reflection

Learning Excel has changed the way I approach data. Previously, I mainly viewed data as numbers arranged in tables, but Excel helped me understand how structured analysis can reveal meaningful insights and patterns.

Through practicing formulas, Pivot Tables, and visualization techniques, I became more confident in cleaning datasets, summarizing information, and presenting findings clearly. Excel also strengthened my problem-solving skills by teaching me how to break down datasets into manageable steps during analysis.

As someone building a career in data analytics and data science, learning Excel has been an important step in developing my analytical thinking. It has helped me move from simply observing data to actively exploring it and using it to support informed decisions.

Overall, Excel has become a valuable foundation in my data analysis journey, and it continues to support my growth as I expand my skills into more advanced tools and technologies.