Relationship Schemas and Joins in Data Modeling

Raphael Njeri — Sun, 05 Jul 2026 17:32:37 +0000

This week I learned about relationship schemas and joins, and I now understand why they are important when working with databases. Before this topic, I thought all the data could just be stored in one table, but I have realized that this is not the best approach because it creates a lot of repeated information and makes the database harder to manage.

From what I have understood, a relationship schema is simply the way different tables in a database are connected. Instead of putting everything in one table, the data is separated into different tables depending on what it represents. For example, students, teachers, and payments can each have their own table, and then they are linked using IDs. This makes the data more organized and easier to update.

I also learned that there are different types of relationships. A one-to-one relationship is where one record is connected to only one other record. A one-to-many relationship is the one I found easiest to understand because one teacher can teach many students, but each student is assigned to only one teacher. There is also a many-to-many relationship where one student can take several courses and one course can have many students. In such a case, another table is needed to connect the two.

Another concept I learned is joins. Joins are used when information is stored in different tables but we want to see it together. The INNER JOIN only shows records that match in both tables. The LEFT JOIN returns everything from the first table even if there is no matching record in the second table. The RIGHT JOIN does the opposite, while the FULL OUTER JOIN returns all records from both tables whether they match or not.

As I continue practicing data modeling and using Power BI, I can now see why relationship schemas and joins are necessary. They help in creating a proper data model instead of using one flat table. This makes reports more accurate, reduces duplicated data, and makes it easier to analyze information. Although I still need more practice, I now have a much better understanding of how tables relate to each other and how joins help retrieve data from those related tables.

How excel is used in real world Data analysis.

Raphael Njeri — Sun, 07 Jun 2026 10:54:16 +0000

Microsoft Excel is a spreadsheet software used to organize, calculate, analyze and visualize data. As I begin my journey in Data Science and Analytics, I have discovered that Excel is much more than just a spreadsheet application. It is a powerful tool that helps transform raw data into meaningful information.

One of the first things I learned is how Excel helps with data entry and organization. Data can be arranged neatly in rows and columns making it easier to manage records, track information, and maintain accuracy. Proper organization is important because it makes data easier to understand and work with.

I also learned the importance of data cleaning. In many cases raw data contains errors, duplicates, or missing information. Excel provides useful tools such as Find and Replace, Remove Duplicates, Text to Columns and Filters that make it easier to clean and prepare data for analysis.

Another key lesson has been data analysis. Excel offers a variety of formulas and functions including SUM, AVERAGE, COUNT, MAX, MIN and IF, which help perform calculations and uncover insights from data. These tools make it possible to identify trends, measure performance, and support decision-making.

From my experience so far, Excel has shown me that no matter how data is collected, it becomes much easier to clean, organize and analyze when using the right Excel tools. It is an essential skill for anyone starting a career in Data Science and Analytics.

DEV Community: Raphael Njeri

Relationship Schemas and Joins in Data Modeling

How excel is used in real world Data analysis.