DEV Community: Tumusiime Kipruto

A Comprehensive Guide to Connecting Power BI to SQL Databases.

Tumusiime Kipruto — Sat, 18 Jul 2026 13:30:34 +0000

If you agree with me, once you open a large spreadsheet full of row's and columns of unending data. Many at times we fail to interpret the data or even fail to understand what the data is actually telling us. Now this is where the saviour's in the I.T world come in handy. Actually cheers to the mighty brains that are behind this very tools that have made the work of data scientists and analysts extremely enjoyable.

Now at this point you're probably wondering what i'm even talking abt so this are the tools;
-Power BI Desktop.

Power BI Service.
Power Query.
Power Pivot.
Excel Power Query. -Excel Power Pivot.

now all this tools stated above are the saviour's im talking about but unfortunately today ill be dwelling majorly on Power BI.

Power BI.

Now Power BI is a business intelligence tool that connects to SQL databases such as PostgreSQL, Microsoft SQL Server, MySQL, and Oracle to analyze and visualize data. Using Power Query, data can be extracted, cleaned, and transformed before creating interactive reports and dashboards that support informed decision-making.

Today ill take you through the process of connecting your local database to your postgres SQL database.

Step 1

Open your local database that is installed in your computer, laptop orr in rare case mobile phone.
Open any browser in your machine search for aiven choose thee postgres SQL service and make sure its running.
In your local database what i majorly advice is you just hold _ctrl+shift+N_ thus is easier and just gives you what you need instead of going through a lot. Now after pressing that a pop-up appears containing all the cloud managed databases available in that local database.
After choosing the service you want to use, go back to your aiven account by this time it is running and pick the credentials provided by that service database this is;

URL.
host. -password. -database.
username.
Authentication. after successfully transferring the above information to your local database before finishing the process its always a good practice to first test connection then after Finish

Power BI: The Ultimate Nightmare Reliever for Data Scientists.

Tumusiime Kipruto — Mon, 29 Jun 2026 22:25:49 +0000

Power BI.

Power BI is a freely available tool from Microsoft for business analytics. It helps you visualize data and seamlessly share the insights from it with stakeholders, Business Partners and Employers. Whether you’re a data scientist, an analyst, or a data engineer, Power BI is a must-know tool that can make your work a lot easier.

Power BI in full standing for Power Business Intelligence. Is a business analytics platform that helps an individual perform lots of tasks this include:

Transforming data.
Creating reports.
Dashboards etc.

Uses of Power BI.

Power BI is used for:

Data cleaning
Data transformation
Business reporting
Dashboard creation

This platform has a few primary elements this include:

Power BI Desktop.
Power BI service.
Power BI mobile.

All this platforms perform the same tasks. But for a better workspace Power BI desktop is advisable to use.

Power Query.

Is the data cleaning and transformation engine in Power BI.
It is used before loading data into Power BI. Power Query allows you to perform all the necessary changes needed for you to have an effective and sensible data set.

DAX.

Meaning Data Analysis Expression, is used to create calculations in Power BI. DAX is similar to excel functions but works with tables and columns.
Measures; this are calculations that are evaluated based on the current filter context .Measures are dynamic and respond to filers.

Insights.

Power Bi performs a number of functions this include; Logical functions, Filter functions, Date functions and so many more.

In today’s fast-paced business environment, speed is everything. Power BI has simplified analysis so much that decisions that used to take three weeks (ask for data, clean it, analyze it, present it) now barely takes three hours.

In short, Power BI hasn't just made data analysis manageable ; it has made it easier, faster and enjoyable. It removes the technical friction so that you can stop wrestling with spreadsheets and start focusing on what actually matters: understanding your business and making better decisions.

By: Tumusiime Kipruto.

https://github.com/dashboard

Excel In Real-World Data Analysis

Tumusiime Kipruto — Sun, 07 Jun 2026 14:02:17 +0000

Excel.

Being one of the most widely used spreadsheets application. Excel is a powerful tool used to organize, store, manipulate, and analyze data.
it contains a large sheet with a giant grid of boxes that help you calculate, sort, and filter data. It has a user friendly interface that enables learners to easily understand how it functions. And are able to easily learn how to use it.

Ways Excel is used in Real-World Data Analysis.

This are few practical examples of how excel is used in real-world data analysis:

Sales Analysis
Large Businesses such as supermarkets or restaurants can use excel to track daily, weekly and monthly sales. This helps the managers and owners of the businesses to analyze which product sell the most and determine which sector to invest in more.
Customer Data Analysis
organizations that collect customers information such as age, location and habits among others. Excel helps organize this data to so as to easily understand the customers preferences.
Academic Performance Analysis
Schools, institutions and universities use excel to manage student records and analyze examination results. This enable's teachers to estimate average scores and thus identify good students to students who need academic support.

Features And Formulas.

Excel has a wide range of features and formulas this include:

Conditional Formatting. This feature changes the color of a cell based on it's value.

-Average Function.
Written as =Average(B1:B10). The average function calculates the mean value of a set of numbers.

Personal Reflection.

At first large sets of data used to scare me out but after learning Excel I now know how to organize data and analyze it efficiently.
By the help of functions and formulas complex tasks have been made easy and chances of making errors greatly reduced. My ability to work with data and draw meaningful conclusions is now amplified and I believe I'm now more productive and better prepared for future professional opportunities.

Tumusiime Kipruto
https://github.com/users/TUMUSIIME-KIPRUTO/projects/1?template_dialog_tab=featured