Joins, Modeling, Relationships and Schemas

Agatha Nduku — Mon, 29 Jun 2026 11:33:51 +0000

Power BI has shown me how multifaceted and multi-purposed it can be. From the Data Cleaning process to data visualization.

Lets begin with some Term definitions;

Joins-These physically combine columns from different tables into one. They make it possible to assemble data from multiple sources.
Modeling-This is the process of defining how business data and tables are structured and connected. It structures chaotic data into organized "business objects". It uses relationships and DAX (Data Analysis Expressions), to ensure fast performance and accurate reporting.
Relationships- These are logical connections between tables that allow data from multiple sources to be combined and analyzed together. hey propagate filters automatically across tables, ensuring that visuals accurately reflect data based on shared keys or IDs.
Schemas-These define the structural organization of your data model, dictating how tables are connected and interact. The correct Schema influences the efficiency and performance of data queries and reports.

Now that we have defined our terms, lets see how they work or what each one does.

I will focus on Schemas because, modeling, joins and relationships are simply integral parts of them.

There are Two Types of Schemas;

Star Schema
This is the gold standard for Power BI modeling. It creates a simple, "star-like" pattern in your Model view. This is the most recommended Schema to use, because it is the simplest style and has the widest use.

The star Schema contains two tables;
• Fact Tables: Positioned at the center. They contain quantitative, measurable data (e.g., sales revenue, transaction quantities, dates) and unique ID.
• Dimension Tables: Surrounding the fact table. They contain descriptive data used to filter and group your facts (e.g., product categories, customer names, regions).

Benefits of star schemas

• Fact/dimensional models like star schemas are simple to understand and implement, and make it easy for end users to find the data they need. They can be applied to data marts and other data resources.
• Great for simple queries because of their reduced dependency on joins when accessing the data, as compared to normalized models like snowflake schemas.
• Adapt well to fit OLAP models.
• Improved query performance as compared to normalized data, because star schemas attempt to avoid computationally expensive joins.

Snowflake Schema
A snowflake schema is a logical database arrangement in a data warehouse where dimension tables are normalized and broken down into related sub-tables.
Resembling a snowflake, it places a central fact table at the core with branched, hierarchical dimension tables radiating outward

Characteristics of Snowflake Schema
• Requires less storage because dimension data is structured.
• Supports multiple hierarchies within a dimension.
• Performance decreases because of extra joins.
• Dimensions can be sourced from different systems and integrated cleanly.
• Good when dimension tables contain attributes at different grains.

Benefits of a Snowflake schema
• Improves data integrity through normalization.
• Reduces redundancy and storage usage.
• Supports detailed hierarchical drill-down.

How to determine which schema to use?

• Star schema and snowflake schema differ primarily in their structure, where snowflake schema is an expanded form of star schema that further normalizes data into additional sub-dimension tables.

• Star schema utilizes a denormalized structure with fewer joins to optimize query performance and speed, while snowflake schema uses normalization to achieve greater storage efficiency and data integrity.

• You can determine which schema is right for you by comparing their uses, benefits, drawbacks, and performance in key areas like scalability and ease of use.

This is just but a small overview of the subject title, to know more, grab yourself some data and sit in-front of your desk, get curious, get interactive. All the best!!-

"Excel in Real-World Data Analysis"

Agatha Nduku — Sat, 06 Jun 2026 10:25:32 +0000

What is Excel

According to Wikipedia:
Microsoft Excel, or simply Excel, is a spreadsheet editor developed by Microsoft for Windows, macOS, Android, iOS and iPadOS. It features calculation or computation capabilities, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications (VBA).

In simpler terms, Excel forms part of the Microsoft 365 and Microsoft Office suites of software and has been developed since 1985. It organizes data in columns/rows and allows you to do mathematical functions.

How is Excel used in real-world data analysis

From the definitions above, it is safe to say that the core roles of Excel are:

Data Recording/Storage
Data Analysis/Manipulation
Data Visualization/Presentation

The image below is a visual representation of an Excel Spreadsheet.

Lets explore these roles.

Data Recording/Storage: Excel has a main spreadsheet format which holds data in worksheets, charts, and macros. This allows supply of data to others for calculation and display.
Data Analysis/Manipulation: Excel comes with functions that allow a person to format or calculate data. It can edit and format text in cells, calculate formulas, search within the spreadsheet, sort rows and columns, freeze panes, filter the columns, add comments, and create charts among many others.
Data Visualization/Presentation: Excel comes with features that allow the data to be saved in the form of a table, graph, pie chart etc. which makes analyzing/visualizing the information much easier. Thus making it more readable and understandable.

Excel features or formulas

I will start with the basics and list these in no particular order of importance/functional hierarchy.

Autofill - It automatically populates cells with data or helps create a sequence.
Text Wrap - It makes overflowing content fit within a cell by expanding the cell vertically.
Find and Replace -It is used to quickly locate, edit and update data across the worksheet.
Data Validation - It is used to restrict cell inputs, prevent typos, and ensure users enter clean, standardized data.
Custom sort - It is used to sort data in a specific sequence (like months, sizes or priority levels) rather than alphabetically.
Conditional formatting - It is used to automatically change the color, font or border of a cell based on its value.
Functions - These are pre-built formulas designed to automate calculations and data analysis.

Now that we have seen some of the magic that excel can work on data, lets move on to something else. Within excel, of course!

My personal reflection:

How has learning Excel changed the way I see data?

I now see data not just as numbers on a spreadsheet but as a world of information, manipulatable to answer questions and tell narratives.

From a single excel spreadsheet, I have learnt that it is possible to extrapolate and/or interpolate a myriad of information and accurately project outcomes.

See how I am already using big words? That is the confidence I have acquired from my lessons in Data Science and Analytics so far.

Some of the direct benefits of using excel I have learnt so far include:

*Numeric precision *- its inbuilt features when correctly used allow consistent, error-free operations across massive data bases.

*Complex Formula Integrity *- Formulas correctly interpret massive data, preventing errors across multiple worksheets.

*Easy Data recovery *- If the information is written on a piece of paper, finding it may take longer, however, this is not the case with excel spreadsheets. Finding and recovering data is easy.

There you have it, an incentive to get acquainted with the excel world. Cheers!

DEV Community: Agatha Nduku