Relationship Schemas and Joins in Power BI

Danson Kuria — Mon, 29 Jun 2026 10:50:30 +0000

A popular BI software named Power BI comes handy when you’re looking to scrutinize data from various sources. One remarkable feature of this tool is that you could define relationship schema for and join tables and present their information together to be utilized in your reports and analyzes.
The Relationship Schemas and Joins are the relationships you build between various tables (data sources). Relationship Schemas are usually defined on the bases of common keys between tables whereas there exist specific kinds of joins that bring data of two tables together when they’re combined by some criteria. Power Bi utilizes various schema such as Star schema and Snowflake schema.
The Star Schema contains one fact table and multiple dimension tables where fact table has transactional data in some columns and dim table store referential information of any particular entity like Products, dates etc.
There can be one to many, one to one and many to many relationships. A common type is to create relationships with the help of unique keys between the tables that would facilitate to get reports from various tables and use them in filters.
Joining table allows you to put all tables into the data model and use different joins while combine them so that you can fetch meaningful data and do some calculations with that. Here are few join operations that you can see in the Power BI -:
InnerJoin - Returns the rows that match both tables’ Join Expression (selects rows that have same values in the both table).
Left OuterJoin - Returns all rows from the first (left) table and the matching rows from the second (right) table.
Right OuterJoin - Returns all rows from the second (right) table and the matching rows from the first (left) table.
Full OuterJoin - Returns all rows from both tables.
AntiJoin - Returns only rows where JoinExpression in the left table doesn’t match anything in the right table.
This practice can help in reduce data redundancy, avoid conflicts while importing data, and build reliable, efficient, and easy to use reports that could facilitate the better business decision-making processes and that too efficiently.

Introduction to Excel: The Usage of Excel in Real World data analysis.

Danson Kuria — Sat, 06 Jun 2026 19:36:19 +0000

In the age of data driven business, the information gathered on a daily basis is tremendous. Data analysis tools have been of immense importance in turning data into valuable insights for the professionals. Microsoft Excel is a spreadsheet application that helps you analyze, visualize, organize and manage data with ease. It has found extensive use in many different scenarios.

Business decision making is one of the areas that uses Excel on a large scale. In order to know how their business is doing and making informed decision, businesses track their sales and monitor the overall performance of their product by analysis of the relevant data collected in their excel worksheets. It can help businesses determine which of their products are selling more and where in the business needs to work.

Financial reports and budgeting have been some of the major uses of Excel. Businesses collect, record and manage their finances on Excel. It can help organizations manage and operate efficiently. Excel helps organizations to track their income, expenses and losses and to create budgets and produce financial reports.

Marketing data analysis has also come a long way with Excel. The sales and marketing professionals have the ability to gather data from various campaigns and then analyze it using Excel in order to track performance indicators such as sales conversions and return on investment to make improvements in marketing efforts.

My experience in learning Excel during first week has made me aware of certain important functions and aspects of excel. Sorting, which helps us arrange data in a readable order and cleaning, which is very important in identifying and removing errors from the data and making it understandable for the purpose of analysis have become a vital component to me.

I was also made aware of data sets, rows and columns that form the structure of data. A data set is a collection of related information that is structured in a table. Rows represent different entries/records whereas the columns provide information of one particular type. Understanding this structure has become very important in data analysis.

I was introduced to useful formulas such as Sum (), average () and count () which help the analysts do quick calculation on the data set. These formulas provide quick analysis on data sets.

Learning excel has changed my perspective on data, which earlier appeared as merely numbers and text. But now I know that data can actually narrate meaningful stories and help make logical decision. I have learned that organizing, cleaning and analysis of data are of critical importance. This first week has laid a strong foundation for my future learning process.

DEV Community: Danson Kuria

Relationship Schemas and Joins in Power BI

Introduction to Excel: The Usage of Excel in Real World data analysis.