Forem: EricMWaimiri

First Week Reflections on SQL

EricMWaimiri — Mon, 13 Apr 2026 14:06:35 +0000

DDL vs DML
In SQL, two families of commands shape how we interact with data: Data Definition Language (DDL) and Data Manipulation Language (DML).

DDL commands (CREATE, ALTER, DROP) are like the architect’s blueprint, they define the structure of the database.
DML commands (INSERT, UPDATE, DELETE, SELECT) are the everyday tools. They let us add, change, or remove the actual records inside those structures. Here is how I tackled the first assignment:
CREATE built the schema nairobi_academy and three tables (students, subjects, exam_results).

INSERT populated each table with 10 rows of realistic data.

UPDATE corrected mistakes: Esther Akinyi’s city changed from Nakuru to Nairobi, and marks for result_id 5 were fixed from 49 to 59.

DELETE removed the cancelled exam result with result_id 9.

Filtering with WHERE
The WHERE clause is the gatekeeper of SQL. Without it, every row would be affected — a dangerous mistake!

I used it to:
_- Find students in Form 4 (WHERE class = 'Form 4').

Retrieve exam results with marks >= 70.
Combine conditions (WHERE class = 'Form 3' AND city = 'Nairobi').
Explore operators like:
- BETWEEN for ranges (marks between 50 and 80).
- IN for multiple matches (cities in Nairobi, Mombasa, Kisumu).
- LIKE for patterns (names starting with A or E). _

It’s the precision tool that makes queries powerful and safe.

CASE WHEN: Transforming Data
Raw numbers are useful, but categories tell stories. With CASE WHEN, I transformed exam marks into performance labels: Distinction, Merit, Pass, or Fail.

I also classified students as Senior (Form 3 or 4) or Junior (Form 1 or 2).

The most challenging part was remembering to always pair UPDATE and DELETE with WHERE. Forgetting it could wipe out entire tables in seconds. The most interesting part was experimenting with CASE WHEN. It was like giving the database a voice: instead of just numbers, it could tell me who excelled, who struggled, and who was senior or junior. This week, I realized that the beauty of SQL lies in precision and creativity.

Publish a Power BI Report and Embed It into a Website.

EricMWaimiri — Tue, 07 Apr 2026 09:20:49 +0000

Power BI transforms raw numbers into interactive dashboards that are full of insights. The real magic, however, happens when you share those dashboards with the world. Publishing and embedding a Power BI report lets your audience explore the data directly on your website, turning a static page into a dynamic story.

- Create a Workspace
Workspaces organize your reports and control who can access them.

Go to Power BI Service; Workspaces; Create Workspace
Log in to the Power BI Service app.powerbi.com
Click Workspaces in the left navigation
Select Create a Workspace and give it a name

- Upload and Publish Your Report
Publishing moves your report from Power BI Desktop to the cloud.

In Power BI Desktop; Home; Publish
Open your report in Power BI Desktop
Click Publish on the ribbon

Choose the workspace you created

Confirm upload; the report will appear in the Power BI Service

- Generate the Embed Code
The embed code allows you to integrate the report into a website.

Go to Power BI Service; Report (In Workspace); File; Embed Report
Open your report in the Power BI Service Click File → Embed Report → Website or Portal
Copy the iframe embed code provided

- Embed the Report on Your Website

Open a text editor
Write or generate a code to embed a power BI report on your website
Paste the iframe embed code where you want the report to appear

Save and publish your website
Open the embedded report on your browser to ensure it loads and is interactive

To understand all the technicality, think of it like this:

Workspaces are the foundation for organizing and securing reports.
Publishing moves reports from desktop to the cloud.
Embed codes are the bridge between Power BI and your website.
Embedded reports remain fully interactive, giving your audience a dynamic data experience.

Power BI and Data Modeling

EricMWaimiri — Sun, 29 Mar 2026 22:51:25 +0000

Data modeling in Power BI is basically about organizing your data properly so you can analyze it easily. Instead of just loading random tables, you structure them in a way that makes sense.
This involves:

Creating and defining tables
Connecting them using relationships
Choosing the right structure (schema)
Combining data using joins

Joins (When Combining Data):
When working in Power Query or SQL, joins help you decide how two tables should be combined:

INNER JOIN: Only returns matching data in both tables
LEFT JOIN: All data from the left table + matches from the right
RIGHT JOIN: All data from the right table + matches from the left
FULL OUTER JOIN: Everything from both tables, matched where possible
LEFT ANTI JOIN: Data in left table with no match in right
RIGHT ANTI JOIN: Data in right table with no match in left

In Power BI, you do this by:

Going to Power Query: Home: Merge Queries
Selecting your tables and the type of join

Relationships (How Tables Connect):
After loading data, you connect tables using relationships:

One-to-One which is rare like, Employee : Employee Details
One-to-Many which is very common like, Customer : Orders
Many-to-Many which is more complex like, Students : Courses

Important Relationship Properties:

Active vs Inactive: Only one active relationship at a time
Cardinality: Defines the type
Cross-filter direction; Single (default) which filters in one direction or Both whose filters move both ways

In Power BI:
Go to Model View; Manage Relationships

Joins vs Relationships
Joins Combine tables into one while relationships keep tables separate but connected

Fact vs Dimension Tables:
Fact Tables are the numbers you want to analyze like sales or revenue while Dimension Tables is the descriptive info like customer name, product category etc. Remember to always keep facts numeric and dimensions descriptive if you want to have clean reports.

Schemas (How You Structure Data):

Star Schema (The recommended option): One central fact table connected to dimension tables. It is rather simple and efficient.
Snowflake Schema - More complex (dimensions split into smaller tables) and is more popularly used for large datasets.
Flat Table (DLAT): Everything in one table and is easy to build but not efficient for big data

Role-Playing Dimensions:
This is when one table is used in multiple ways.

Some Common Mistakes:

Ambiguous relationships
Many-to-many confusion
Poor table structure
Forgetting to activate relationships

So a simple Workflow would go like this:

Load data; Power Query
Combine data; Merge Queries (joins)
Connect tables; Model View
Choose structure; Star/Snowflake schema
Add role-playing dimensions if needed
Check everything; relationships, filters, and structure

Good data modeling makes your reports faster, cleaner, and easier to understand. Without it, even simple analysis becomes confusing.

Real World Data Analysis with Excel

EricMWaimiri — Sun, 29 Mar 2026 15:02:31 +0000

Microsoft Excel looks like just any other spreadsheet; however, it’s an absolute powerhouse for turning raw data into insights. It's core function - organizing information into rows and columns - makes it easier to perform complex calculations, look over patterns, and bring data to life through various visualizations.

Excel is a critical asset across various industries:

Business Reporting: Excel helps organizations and businesses track performance and make smarter decisions.
Financial Analysis: While forecasting revenue, calculating margins, and evaluating investment opportunities analysts use excel to work with precision.
Project Management: Teams rely on Excel to map timelines, allocate resources, and keep projects running smoothly to the end.
Research: Raw survey data is turned into clear insights using statistical analysis and visual storytelling.

Excel's formulas and functions keep us from doing redundant work and reduces human error, allowing us to focus on insight and strategy. Some of the most popular and important functions include:

SUM() - instantly totals values e.g. calculating weekly sales.
IF() - a logical function that checks whether something is true or false, and then returns one result if it’s true, and another result if it’s false.
VLOOKUP() / XLOOKUP() - Vertical Lookup or VLOOKUP searches for a value in the first column of a table and returns a corresponding value from another column in the same row. XLOOKUP is a modern replacement for VLOOKUP. Unlike VLOOKUP it can search in any direction and is easier to use.
PivotTables - Visual tools that summarize and analyze large amounts of data quickly. Instead of manually calculating totals or averages, a PivotTable lets you organize your data to see it from different angles.
Conditional Formatting - makes it easier to see trends and outliers with nothing important going unnoticed.

Excel for me feels like a lifesaver when it comes to transformation and calculation of data. I will continue to dive into the excitement as I discover new functionalities while exploring different datasets.