DEV Community: Feddy Mwanjumwa

Connecting Power BI to PostgreSQL: A Step-by-Step Guide for Beginners.

Feddy Mwanjumwa — Mon, 20 Jul 2026 13:41:46 +0000

Introduction.

Power BI is one of the most popular tools for transforming raw data into interactive dashboards and reports. Many beginners start by importing Excel files, big organizations often store their data in relational databases such as PostgreSQL.

Connecting Power BI directly to PostgreSQL allows you to analyze data from a centralized source, reduce manual data imports, and create reports that can be refreshed whenever the database changes.

In this article, I'll take you through the process of connecting Power BI Desktop to a local PostgreSQL database.

What is PostgreSQL?
PostgreSQL is a powerful, open-source relational database management system (RDBMS). It is widely used because it is:

Free and open-source
Reliable and secure
Highly scalable
Supported natively by Microsoft Power BI Because of these features, PostgreSQL is an excellent choice for storing and managing business data.

Prerequisites
Before connecting Power BI to PostgreSQL, I ensured I had the following installed:

PostgreSQL
Power BI Desktop
A PostgreSQL database containing the tables I wanted to analyze.

Step 1: Open Power BI Desktop
Launch Microsoft Power BI Desktop.

From the Home ribbon, select:

Home → Get Data

A window containing all supported data sources will appear.

Step 2: Choose PostgreSQL Database

In the Get Data window:

Search for PostgreSQL Database.
Select it.
Click Connect.

Power BI will open the PostgreSQL connection dialog.Step 2: Choose PostgreSQL Database

In the Get Data window:

Search for PostgreSQL Database.
Select it.
Click Connect.

Power BI will open the PostgreSQL connection dialog.

Step 3: Enter Your Database Details

Provide your PostgreSQL connection information.

For a local PostgreSQL server, the details are typically:

An example
Server localhost:5432 or 127.0.0.1:5432
Database postgres (or your database name)

Step 4: Authenticate

Power BI will ask for your PostgreSQL credentials.

Enter :

Username
Password

Then click Connect.

If the credentials are correct, Power BI will establish a connection to your PostgreSQL database.

Step 5: Select the Tables

After connecting successfully, the Navigator window will display the available tables.

Select the tables you want to import into Power BI.

You can either:

Load the data directly into Power BI, or
Select Transform Data if you want to clean or modify the data first.

Step 6: Transform the Data (Optional)

If you choose Transform Data, Power Query Editor opens.

Power Query allows you to:

Change data types
Rename columns
Remove duplicates
Handle missing values
Filter rows
Split or merge columns

Preparing the data before building reports helps improve accuracy and keeps your model organized.

Step 7: Build Your Report

Once the data has been loaded, you're ready to create visualizations.

Power BI offers many built-in visuals, including:

Bar charts
Line charts
Pie charts
Tables
KPI cards
Maps
Slicers

These visuals help transform raw database records into meaningful insights that users can explore interactively.

This process may come with alot of common connection issues.
If Power BI cannot connect to PostgreSQL, check the following:

Is the PostgreSQL service running?
Is the server name correct?
Is the port number correct?
Is the database name correct?
Are the username and password correct?
Has the PostgreSQL driver been installed?
Is a firewall blocking the connection?

Most connection problems can be resolved by checking these settings.

Benefits of Connecting Power BI to PostgreSQL

Using PostgreSQL as a Power BI data source offers several advantages:

Centralized data storage
Improved data consistency
Faster reporting
Reduced manual imports
Easier collaboration
Support for scheduled refreshes

Instead of repeatedly importing spreadsheets, reports can be built directly from the database.

Conclusion
Connecting Power BI to PostgreSQL is a valuable skill for anyone working in data analytics. It allows you to access data directly from a relational database, prepare it for analysis, and create interactive dashboards that can support informed decision making.

Whether you're working on personal projects or organizational data, learning to connect Power BI to PostgreSQL is an important step toward building efficient and professional reports.

How Excel is used in Real World Data Analysis- How deep, intresting and useful Excel is in the Tech space.

Feddy Mwanjumwa — Sat, 06 Jun 2026 20:26:15 +0000

Introduction.

My first encounter with Microsoft Excel was about 8 years ago in primary school. At the time, I only used a few basic features such as entering data into cells. To me, Excel seemed like nothing more than a digital spreadsheet where one could write some data figures like exam results.

Fast forward to 2026, and my perspective has completely changed. As I began learning Excel more seriously as part of my data analytics journey, I quickly realized that it is far more powerful than I had imagined. Beyond storing data, Excel can perform calculations, automate repetitive tasks, analyze large datasets, and help users make critical decisions.

What once looked like a simple spreadsheet application has proved itself to be a very useful tool used by students, businesses, researchers, accountants, and data analysts around the world. In this article, I share some of the key lessons and discoveries from my first week of learning Excel and how they have changed the way I view this software.

Understanding Excel at a deeper level.

One of the first things I learned was how Excel organizes information using rows, columns, and cells. This might look simple, but understanding how data is structured is important because almost every task in Excel depends on it.

My Introduction to Data Cleaning.

One thing that i found intresting to me during my first week of learning Excel was data cleaning. Before this course, I assumed that data collected from surveys, businesses, or other sources was always ready for analysis. However, I learned that raw data often contains errors, duplicates, missing values, and inconsistencies.

Excel has several tools that help clean and prepare data before analysis. Tools such as Find and Replace, Remove Duplicates, Text to Columns, and filtering make it easier to identify and correct problems within a dataset.

I came to understand that data cleaning is one of the most important steps in the data analysis process. Even the most advanced analysis can produce misleading results if the data is inaccurate or poorly organized. Learning about data cleaning gave me a new appreciation for the amount of work that happens before meaningful conclusions can be made from data.

I also learned how to format cells by changing fonts, colors, borders, and number formats. Proper formatting makes spreadsheets easier to read and gives them a more professional appearance.

My First Experience with Excel Formulas.

The most exciting part of learning Excel was discovering formulas. Instead of performing calculations manually, Excel can calculate values automatically.

Some of the first formulas I learned include:

SUM() for adding values
AVERAGE() for finding the mean
MAX() for finding the largest value
MIN() for finding the smallest value

These functions demonstrated how Excel can save time and reduce human error when working with data.

Sorting and Filtering Data.

Another feature that has the ability to sort and filter data. Sorting allows data to be arranged in ascending or descending order, while filtering helps display only the information that meets specific order

For example, in a dataset containing employee records, filtering can be used to display only employees from a particular department or those with a specific marital status. This makes it much easier to work with large amounts of information.

Final thoughts.

After just one week of learning Excel, I can already see how much depth the tool has beyond what I initially thought. What once felt like a simple spreadsheet application is now revealing itself as a powerful platform for working with data in a structured and efficient way.

This early stage has shown me that small skills like organising data, using formulas, and cleaning datasets are the foundation of much larger concepts in data analysis. I now understand that mastering Excel is not about memorizing functions, but about learning how to think logically about data.

Overall, this first week has completely changed how I view Excel. What seemed basic at first now feels like an essential skill that I will keep building on throughout my learning journey.

Share your journey on how you viewd excel when learning it for the first time, i would love to hear from you too!!!

What i learnt about Excel after a week of data science classes- How deep, intresting and useful Excel is in the Tech space.

Feddy Mwanjumwa — Sat, 06 Jun 2026 20:26:14 +0000

Introduction.

Understanding Excel at a deeper level.

My Introduction to Data Cleaning.

I also learned how to format cells by changing fonts, colors, borders, and number formats. Proper formatting makes spreadsheets easier to read and gives them a more professional appearance.

My First Experience with Excel Formulas.

The most exciting part of learning Excel was discovering formulas. Instead of performing calculations manually, Excel can calculate values automatically.

Some of the first formulas I learned include:

SUM() for adding values
AVERAGE() for finding the mean
MAX() for finding the largest value
MIN() for finding the smallest value

These functions demonstrated how Excel can save time and reduce human error when working with data.

Sorting and Filtering Data.

Final thoughts.

Overall, this first week has completely changed how I view Excel. What seemed basic at first now feels like an essential skill that I will keep building on throughout my learning journey.

Share your journey on how you viewd excel when learning it for the first time, i would love to hear from you too!!!