DEV Community: Gabriel Mbuva

Power BI: Modelling, Schemas, Joins and Relationships

Gabriel Mbuva — Mon, 29 Jun 2026 06:03:40 +0000

Power BI is a data visualization and business intelligence tool designed for business professionals with varying levels of data knowledge. Power BI's dashboard can be used to report and visualize data in a wide range of styles, including charts, graphs and more. It allows everyone from beginners to experts to easily see what is happening in their business and make faster, smarter decisions.

Data Modelling

Data modelling is the process of organising, structuring, and defining relationships between your data table. It uses diagrams, symbols, and textual definitions to visually represent how data is captured, stored and used.

Reasons for data modelling
Seamless Data Exploration: Builds clear hierarchies and drill-down paths. This lets report consumers quickly navigate tables to uncover hidden trends.

Maximum Query Performance: Directly dictates how fast your reports load. Clean modeling prevents redundant data and messy relationships from delaying visual renders.

Guaranteed Report Accuracy: Secures the consistency, dependability, and integrity of your source records. This ensures decision-makers are always working with precise insights.

Simplified DAX Writing: Keeps your Data Analysis Expressions short and clean. Well-structured tables eliminate the need for complex, unmaintainable override formulas.

Schemas

Schemas refer to the logical structure and organization of your data model. In Power BI we have two types of schemas; Star and snowflake schema.

Star Schema: Is the most widely recommended and optimal design in Power BI. The center of the star is a Fact Tables which contains measurable, quantitative data, for example, sales revenue, quantity sold, or transaction amounts. The fact tables contain foreign keys that link to dimension tables. Dimension tables form points of the star. They provide descriptive context for the facts. Dimension tables contain primary keys and are used to filter and group data in your reports.

Snowflake Schema: Is an extension of the star schema. In a snowflake schema, dimension tables are normalized, which means they are broken down into further sub-dimension tables. It reduces data redundancy but requires more complex relationships between tables, which can decrease query performance in Power BI.

Joins

A join is a method used to merge tables together to combine information based on a shared column.
Different types of joins in Power BI include:

Left Outer: Returns all rows from the left table and matching rows from the right table.

Right Outer: Returns all rows from the right table and matching rows from the left table.

Full Outer: Returns all rows from both tables, regardless of whether there is a match.

Inner: Returns only the matching rows present in both tables.

Left Anti: Returns only rows from the left table that have no match in the right table.

Right Anti: Returns only rows from the right table that have no match in the left table.

Relationships

Relationships are logical connections between tables based on shared columns. They are the foundation for a data model, enabling visuals to slice, filter, amd aggregate data seamlessly across multiple data sets.

Types of Relationship Cardinality
Cardinality defines the nature of the relationship between two tables:

One-to-Many: The most common and recommended relationship. One row on the "one" side relates to multiple rows on the "many" side . Filters flow strictly from the "one" side to the "many" side.

Many-to-Many: Occurs when both tables have multiple rows matching the shared key. These are generally not recommended because they create unpredictable filter paths, unreliable totals, and performance bottlenecks.

One-to-One: Both tables contain only one unique row for the related key. Used infrequently; usually suggests the data should have been merged into a single table initially.

How Excel is Used in Real-World Data Analysis

Gabriel Mbuva — Fri, 05 Jun 2026 06:42:53 +0000

Introduction

Excel is one of the most used tools for data analysis. It allows beginners like myself to easily clean, organize, analyze and visualize data.Excel enables users to work with large datasets and extract meaningful insights without requiring advanced technical skills.

What is Excel

Excel is a spreadsheet that allows you to collect, organize, analyze, calculate, and visualize data efficiently.Despite the emergence of other data analysis tools like SQL and Power BI, Excel remains one of the most widely used tools for both personal and professional data management.This can be credited to its ease of access, learning, and use.

Ways Excel is used in real-world data analysis

This week, I had the opportunity to explore how Excel is used in real-world data analysis.I discovered that Excel is not just a basic spreadsheet tool, but a powerful application that helps make sense of data and support decision-making.

Data organization and cleaning
Excel is used to structure raw data, remove duplicates, and fix errors. This improves data quality, making it easier to analyze and more reliable for decision-making.This improves data quality, making it easier to analyze and more reliable for decision-making.

Financial reporting
Excel is commonly used in finance to create budgets, calculate profits and losses, and monitor expenses.It helps organizations keep accurate financial records and understand their financial situation.

Business decision-making
Businesses use Excel to track sales, compare performance over time, and identify trends.This helps managers understand what is working well and what needs improvement.

Excel features and formulas

In just a week, I have learned several Excel formulas that simplify data management and make working with data more efficient.

SUM function
The SUM function is used to add a range of values together in Excel, making it one of the most essential tools for quick calculations.It's used to automatically add a range of numerical values together, eliminating the need for manual calculations. This not only saves time but also reduces the chances of errors when working with large datasets.

Data validation
Data validation in Excel is a feature used to control what type of data can be entered into a cell. It helps ensure that only correct and meaningful information is added to a dataset, reducing errors and improving data quality.

Conditional formatting
Conditional formatting is a useful Excel feature that automatically changes how cells look based on their values. It helps highlight important information in a dataset, making patterns and trends easier to see at a glance.

IF and IFS functions
The IF function in Excel is used to make decisions based on a condition. It checks whether something is true or false and then returns a result.The IFS function is used when there are multiple conditions.Instead of using many IF statements, it allows you to test several conditions in one formula.

Reflection

Learning Excel has completely changed how I understand and interact with data.At first, I used to see spreadsheets as simple grids filled with numbers that didn’t really mean much.Now, I view them as powerful tools that reveal patterns and support decision-making when used correctly.As I worked more with Excel, I became more comfortable cleaning datasets, organizing information, and drawing meaningful insights from raw data. Features like formulas, data validation, and conditional formatting made it easier for me to structure data properly and quickly identify important trends. I also learned to approach problems more logically by breaking down datasets into smaller, manageable parts during analysis.Excel has given me a strong foundation in data analysis and it has motivated me to keep improving as I move toward more advanced tools and techniques in the field.