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

martin georges — Sun, 29 Mar 2026 19:49:33 +0000

Introduction

Last week in our Power BI class we focused on one of the most important concepts in data analysis: data modeling. Data Modeling is the process of analyzing and defining different data type that is collected or produced. I learnt that before creating dashboards or visualizations, it’s critical to properly structure and connect your data. Our teacher illustrated the importance of using power query to remove duplicates, handle blanks , filter and sort data and merge and reshape datasets.

Joins vs Relationships

Joins are used in tools like SQL or Power Query to physically combine tables into one. In Power BI, joins usually happen in Power Query before the data is loaded into the model.
Relationships in Power BI link tables without merging them, allowing for more flexible and efficient analysis.

we were taught that Power BI prefers relationships over joins because they help maintain clean, scalable data models.

SQL join types

An inner join returns only the rows that have matching values in two tables.

A left join keeps all the rows from the left table and only brings in matching rows from the right table.

A right join does the opposite—it keeps all rows from the right table and matches what it can from the left.

A full outer join returns all rows from two tables, whether there’s a match or not.

A left anti join returns rows from the left table that don’t have a match in the right.

Similarly, a right anti join shows rows from the right table that don’t have a match in the left.

Types of Relationships

We explored different types of relationships:

One-to-Many is the most common (e.g., one product category to many products)
Many-to-One is the reverse of one-to-many
Many-to-Many is more complex and is used when both tables contain duplicates

I also learned about cross-filter direction, which determines how data flows between tables.

Data Schemas

Another important concept was data schemas, especially:

Star Schema
One central fact table
Connected to multiple dimension tables
Best practice for Power BI models

Snowflake Schema
More normalized (dimension tables split further)
Can be more complex but sometimes necessary

The star schema stood out as the most efficient and recommended approach.

Practical Application

During the session, I practiced:
Creating relationships between tables in Power BI
Defining primary and foreign keys
Avoiding common issues like circular relationships
Structuring datasets into a star schema format

Personal Reflection

Before this class, i knew power BI as a tool for only presenting data. Now i can use it for cleaning data and creating visuals.I understand how to connect data and that how data is connected is just as important as the data itself.

How Excel is used in Real-World Data Analysis

martin georges — Sat, 28 Mar 2026 16:58:33 +0000

Introduction

The definition of Microsoft excel according to a simple google search is " A powerful electronic spreadsheet application developed by Microsoft, used to organize, analyze, and store data in rows and columns." I have used it to organize, analyze, and visualize all sorts of data in my science career. excel shines because everyone knows how to use it , it requires no coding skills, and is quicker in data analysis compared to other data analysis software ( may be because it is 'lighter' for the processor) coupled with the fact that it requires no dependencies, environments and deployments.

How is it used in real-world scenarios

Excel is applied in a myriad of sectors to support in decision making.

In health care public health professionals are able to collect and store data from health facilities and public agencies using excel. Analysis of the data can estimate the number of people showing up in hospitals with particular infections or cancers and they can use that data to guide them on prioritization during allocation of resources.
In banks and financial institutions operations, excel is used for budgeting, financial modeling, forecasting, and risk analysis. The business development team uses excel in account opening tracking, performance dashboards, and market analysis.

Features I Have Learned and Their Applications

Data cleaning: This is the first process for any data set. Here we try to understand our data set by checking the type of data each column/cell has. we then align the data type to the operation we want to do with the data. for example if the format of a cell with a number is not appropriate calculations involving that cell will bring erroneous value. By right clicking and changing the format of the cell or column. this is corrected.

NB/: A column with more than 60% blanks should be completely excluded from the dataset.

Formulas & Functions (e.g., SUM IF, VLOOKUP/XLOOKUP): Used to automate calculations, categorize data, and retrieve information efficiently.

Consider the image below to illustrate the features I am going to explain:

I would use sumifto get the total value of sales before a particular date. =SUMIF(A2:A10, "Apple", B2:B10) sums values in
if corresponding cells in B2 to B10 is equal "Apple".
Countif can be used to count cells that meet a single specific criterion e.g =COUNTIF(A2:A10, "Apple") counts how many cells in the range A2:A10 contain "Apple". this should return a value of 3 in the cell you create the formula
Vlookupand Xlookupto find a specific value in a column or whole data set. this is particularly helpful when handling blanks in a dataset.=VLOOKUP("Apple", A2:B9, 2, FALSE) would bring up 100
CONCAT is used to combine text from multiple cells. =CONCAT(A1, " ", B1) will combine a name in cell A1 and cell A2 leaving a space between the words. i.e Apple 100
MAX/MIN: Returns the highest/lowest value. =MAX(B2:B9) 200

Pivot Tables: can be used to summarize datasets without writing formulas. It is crucial when identifying trends. see example below:

Personal experience

Before my training, I viewed datasets as rigid structures with very limited possibilities for manipulation. Now, I understand that data is flexible. Even blank cells can be meaningfully handled and included in analysis. I’ve also learned how to create automated formulas that reduce workload and deepen my understanding as I analyze data.

Most importantly, Excel has been a stepping stone into data science, helping me build a strong foundation in working with structured data and translating it into meaningful insights.

DEV Community: martin georges

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

Introduction

Joins vs Relationships

SQL join types

Types of Relationships

Data Schemas

Practical Application

Personal Reflection

How Excel is used in Real-World Data Analysis

Introduction

How is it used in real-world scenarios

Features I Have Learned and Their Applications

Personal experience