DEV Community: Justine Kimeli

A Step-by-Step Guide: Connecting Power BI to SQL Databases

Justine Kimeli — Sun, 05 Jul 2026 20:54:47 +0000

Introduction

Many companies and organizations interact with large amount of data from collection to creating insights from it. Transforming these data will bring meaningful insights and there making informed decions for the end users. Power BI is a powerful tool that enables one to create interactive dashboards and reports that communicates what the data is all about. Connecting Power Bi to SQL databases now allows onre to analyze data in real time or through a scheduled time.
In this article demonstrates a step-by-step guide on how to connect Power BI to SQL databases both on local network and cloud-based, and start transforming your data into meaning visualizations.

Part 1: Connecting to a local PostgreSQL Database

Ensure that your PostgreSQL is downloaded and installed in your local machine. Launch Power BI desktop and select get data from the home tab.

Choose SQL Server From get data,select more,search for database then PostgreSQL database and connect which will require sever connection details.
Connection Details Enter the servername and database name. In this case I will user postgres as username and the server is *localhost:5432 *
Choose import data querry mode and enter your login credentials.
The window will then lists tables and you will select the one to work on with.For data cleaning click Transform data and the load for creating creating new measure and data models.

The navigator window will now show the tables available in the local PostgreSQL database.

Part 2: Connecting to a Cloud Based PostgreSQL on Aiven

Log in to your Aiven account, create a new service or the one existing and ensure it is running. The dashboard has Host Port Database name (usually defaultdb) Username (usually avnadmin) Password, for connection details

Download CA certificate, navigate to manage certificate manage,Trusted Root Certificate, right click certificates and import the certificate from your downloads.

Open Power BI Desktop, navigate to home tab, Get Data, More, select PostgreSQL, enter login credentials

After selecting what you want to transform like the local connection, performing data cleaning and load for modelling and all DAX irregardless of where the data is in whether local database or cloud.

Conclusion

Connecting Power BI to SQL databases whether hosted on local or cloud enables organizations to transform raw data into meaningful and interactive insights.
The steps of connecting Power BI to SQL databases are similar in that the cloud connection require SSL certificate for security access to be grated , otherwise every operation works the same.

Understanding Relationship, Schemas and Joins In Power BI

Justine Kimeli — Mon, 29 Jun 2026 22:59:24 +0000

Introduction

Power BI is a powerful business analytics and visualisation platform used to connect, transform and analyze raw data from various sources. This platform helps organizations to generate reports and dashboards that helps them make better and reliable decisions.
In this article, we are going to learn what are relationship,schemas and joins and how they play a role in performing power querry in Power BI.

Relationship in Power BI

In basic terms, relationships means a link between two things. In Power BI, relationship shows how rows in one table are connected to other rows in another table based on a unique key.

Types of Relationship

There are 4 types of relationship which includes; one-to-one, one-to-many, many-to-one and many-to-many. This relationship helps in combining information in multiple columns without duplicating it.

One-to-One Relationship

This relationship means each row in one table corresponds to exactly one row in another table. A perfect example is a student having one id to access a student portal account.

One-to-Many Relationship

This type of relationship that is commonly used in relational databases and in Power BI.It means that one record in a parent table can be associated with many records in a child table, but each record in the child table can be associated with only one record in the parent table.
An example is one student can register for many courses.

Many-to-One Relationship

This is basically the reverse of one-to-many type of relationship. An example is that many patients seeks medication from one doctor.

Many-to-Many Relationship

This type of relationship means multiple records in both tables share the same values.For example students enroll in multiple courses and each course has may students.

Schemas

A schema is basically how data is defined or structered in a database. It defines how data is organized including tables, columns, keys,data types and relationship between the tables.

Star Schema

A star schema is data modelling technique that is used to warehouse data. It organizes data into a central fact tables with different dimension tables forming a shape that is like a star.
In start schema, fact table stores numerical values and foreign keys that directly links the dimension table while dimension table provides descreptive context behind the numerical values in the fact table.

Snowflake Schema

This is a database design where dimension tables are normalized into multiple related sub-tables to reduce duplicate data.

Joins In Power BI

A join is a connection between two tables using a common column so that related data can be combined together. Joins are done in power querry editor.

Power BI jons

Inner join: It returns matchinhg rows only.
Left outer join: It returns all rows fro the first table and matching rows from the second table.
Right outer join: It returns all rows from the second table and matching rows from the first table.
Full outer join: It returns all rows from both tables.
Left anti join:It returns rows from the table with no match in the second table.
Right anti join: It returns rows from the second table with no match in the first table.

Conclusion

Finally, understanding relationships, schemas, and joins is important for building effective data models in Power BI. Relationships enable tables to work together, while schemas provide a structured framework for organizing data. Joins make it possible to combine data from different sources into a single, meaningful dataset. Mastering relationships, schemas, and joins is therefore a key skill for developing efficient Power BI reports, dashboards and making better insightful decisions.

Linux Fundamentals for Data Engineering

Justine Kimeli — Sun, 07 Jun 2026 20:11:38 +0000

Introduction

Data engineering is the process of collecting, moving, cleaning, storing, and preparing data such that it is easily accessed by data analysts, data scientists, AI systems, and businesses.
Basically data engineering is building systems that make data available, usable and reliable.

When learning data engineering, there is much focus tools such python, SQL and cloud platforms. While these technologies are of importance, there is one data system called linux which is the background in this field of data engineering.

In this article, I am going to share how linux commands is playing a vital role in data engineering and how it is used in a linux environment.

Why Linux is important in Data Engineering

Linux is widely used in real-world data engineering because it provides a stable, efficient, and flexible environment for handling large amounts of data.

Most companies stores data in a database, processes large datasets, schedules automated workflows, and deploys applications to the cloud.

Linux is used by data engineers to connect to remote servers,install and configure databases,transfer files between systems,monitor server performance, manage permissions and troubleshoot production issues in a linux environmemt. Therefore, without a basic understanding of Linux, working with most of the projects become challenging.

Basic Linux commands

ssh (secure shell) ->This commands allows one to access a remote server which has a host name and authenticate with a password.

pwd -> This command is used to show current working directory

ls -> Lists all files and directories

cd -> This command is used to change a directory

mkdir -> Create a new directory

ls -a -> Displays all files including hidden files

ls -l -> Displays long-list of files

ls -lh -> Displays file size in human- readable format

mv -> Moving files and directories
cp-> Copying files and directories
scp -> secure copying of files from a local machine to a remote server.
rm -> Removing files
touch -> Create an empty file
echo -> Print text or variables
exit -> Clears the terminal

Installing postgres
sudo apt install postgresql postgresql contrib -y
Starting postgres ->sudo systemctl start postgresql
Checking the status of postgres -> sudo systemctl status postgresql

Checking the psql version -> psql --version

Connecting from Linux server to Postgress
sudo -u postgres psql

Conclusion

Linux is a fundamental skill in data engineering that helps data engineers access, manage databases, transfer data, automating tasks. Through the practice that I have been doing, I have learned how to access a remote server perform various basic linux commands. I also learnt how to install postgres in linux environment and be able create database, schema, and perform basic SQL querries in linux environment.Finally, linux skills is essential for building, managing, and maintaining reliable data engineering solutions.