DEV Community: Shueib Ali

From Rows to Results: How to Start Writing SQL Queries

Shueib Ali — Sun, 12 Apr 2026 16:22:48 +0000

From Rows to Results: How to Start Writing SQL Queries

UNDERSTANDING SQL BASICS: DDL, DML, FILTERING, AND DATA TRANSFORMATION

I was new to all the terms above like SQL, DDL, DML, Filtering, and Data Transformation. Today I will take you through how I understood the above terms with examples.

1. What is SQL?

SQL (Structured Query Language) is a language used to:

Talk to a database
Store data
Get data
Change data Think of it like giving instructions to a computer about data.

2. DDL (Data Definition Language)

DDL is used to create or change the structure of a database. It helps you build or design the database
Examples:

CREATE → create a table
ALTER → change a table
DROP → delete a table

-- Create a table
CREATE TABLE students (
    id INT PRIMARY KEY,
    name VARCHAR(50),
    age INT
);

-- Add a new column
ALTER TABLE students
ADD gender VARCHAR(10);

-- Delete table
DROP TABLE students;

3. DML (Data Manipulation Language)

DML is used to work with the data inside the table.
It helps you add, change, or remove data
Examples:

INSERT → add data
UPDATE → change data
DELETE → remove data

-- Insert data
INSERT INTO students (id, name, age)
VALUES (1, 'Ali', 20);

-- Update data
UPDATE students
SET age = 21
WHERE id = 1;

-- Delete data
DELETE FROM students
WHERE id = 1;

4. Filtering Data

Filtering means selecting only the data you want.
You pick specific records, not everything
Keyword used:

WHERE
AND, OR
IN, BETWEEN, LIKE

-- Get all students aged 20
SELECT * FROM students
WHERE age = 20;

-- Using AND
SELECT * FROM students
WHERE age > 18 AND gender = 'Male';

-- Using OR
SELECT * FROM students
WHERE age = 18 OR age = 20;

-- Using IN
SELECT * FROM students
WHERE age IN (18, 20, 22);

5. Data Transformation

Data transformation means changing how data looks when you display it.
You modify or format data while viewing it
Examples:

Change text to uppercase
Combine columns
Do calculations

-- Convert names to uppercase
SELECT UPPER(name) AS student_name
FROM students;

Let's create queries with a school settings,

CREATE TABLE students (
    student_id INT PRIMARY KEY,
    first_name VARCHAR(50) NOT NULL,
    last_name VARCHAR(50) NOT NULL,
    gender VARCHAR(1),
    date_of_birth DATE,
    class VARCHAR(10),
    city VARCHAR(50)
);

The above does the following:

Sets student_id as the PRIMARY KEY
Makes first_name and last_name NOT NULL
Keeps other fields optional (can accept NULL values)

Lets create another table with differnt columns as shown below

 CREATE TABLE subjects (
    subject_id INT PRIMARY KEY,
    subject_name VARCHAR(100) NOT NULL UNIQUE,
    department VARCHAR(50),
    teacher_name VARCHAR(100),
    credits INT
);

sql
Another table example
Create using this;

CREATE TABLE exam_results (
    result_id INT PRIMARY KEY,
    student_id INT NOT NULL,
    subject_id INT NOT NULL,
    marks INT NOT NULL,
    exam_date DATE,
    grade VARCHAR(2)
);

Lets say we want to add students in our tables above, we do the following;

INSERT INTO students (student_id, first_name, last_name, gender, date_of_birth, class, city) VALUES
(1, 'Ali', 'Hassan', 'M', '2005-03-15', 'Form 4', 'Nairobi'),
(2, 'Amina', 'Mohamed', 'F', '2006-07-22', 'Form 3', 'Mombasa'),
(3, 'Brian', 'Otieno', 'M', '2005-11-10', 'Form 4', 'Kisumu'),
(4, 'Diana', 'Njeri', 'F', '2007-01-05', 'Form 2', 'Nakuru'),
(5, 'Kevin', 'Mwangi', 'M', '2006-09-18', 'Form 3', 'Eldoret'),
(6, 'Fatma', 'Ali', 'F', '2005-06-30', 'Form 4', 'Garissa'),
(7, 'John', 'Kamau', 'M', '2007-04-12', 'Form 2', 'Nyeri'),
(8, 'Grace', 'Wanjiku', 'F', '2006-12-25', 'Form 3', 'Thika'),
(9, 'Peter', 'Ochieng', 'M', '2005-08-09', 'Form 4', 'Kisii'),
(10, 'Esther', 'Chebet', 'F', '2007-02-14', 'Form 2', 'Kericho');

Insert all 10 subjects into the subjects table.

INSERT INTO subjects (subject_id, subject_name, department) VALUES
(1, 'Mathematics', 'Science'),
(2, 'English', 'Languages'),
(3, 'Kiswahili', 'Languages'),
(4, 'Biology', 'Science'),
(5, 'Chemistry', 'Science'),
(6, 'Physics', 'Science'),
(7, 'History', 'Humanities'),
(8, 'Geography', 'Humanities'),
(9, 'Computer Studies', 'Technology'),
(10, 'Business Studies', 'Commerce');

Insert all 10 exam results into the exam_results table

INSERT INTO exam_results (result_id, student_id, subject_id, marks, exam_date, grade) VALUES
(1, 1, 1, 85, '2024-11-10', 'A'),
(2, 2, 2, 78, '2024-11-10', 'B'),
(3, 3, 3, 65, '2024-11-11', 'C'),
(4, 4, 4, 88, '2024-11-11', 'A'),
(5, 5, 5, 72, '2024-11-12', 'B'),
(6, 6, 6, 60, '2024-11-12', 'C'),
(7, 7, 7, 90, '2024-11-13', 'A'),
(8, 8, 8, 55, '2024-11-13', 'D'),
(9, 9, 9, 80, '2024-11-14', 'A'),
(10, 10, 10, 68, '2024-11-14', 'C');

Write a query to find all students who are in Form 4.

SELECT * 
FROM students
WHERE class = 'Form 4';

Write a query to find all exam results where the marks are greater than or equal to 70.

SELECT * 
FROM exam_results
WHERE marks >= 70;

Write a query to find all exam results where marks are between 50 and 80

SELECT * 
FROM exam_results
WHERE marks BETWEEN 50 AND 80;

Write a query to find all exams that took place between 15th March 2024 and 18th March 2024.

SELECT *
FROM result
WHERE exam_date BETWEEN '2024-03-15' AND '2024-03-18';

Write a query to find all students who live in Nairobi, Mombasa, or Kisumu - use IN.

SELECT student_id, first_name, last_name, city
FROM student
WHERE city IN ('Nairobi', 'Mombasa', 'Kisumu');

Write a query using CASE WHEN to label each exam result with a grade description - 'Distinction' if marks >= 80

SELECT 
    result_id,
    student_id,
    subject_id,
    marks,
    CASE 
        WHEN marks >= 80 THEN 'Distinction'
        ELSE 'Not Distinction'
    END AS grade_description
FROM result;

Conclusion

Mastering a query language like SQL is much like learning to speak directly to your data. As we've explored throughout this article, whether you are narrowing down specific date ranges, filtering records across multiple categories with the IN operator, or transforming raw numbers into meaningful labels using CASE WHEN, SQL gives you the exact vocabulary needed to ask the right questions.

HOW TO PUBLISH A POWER BI REPORT AND EMBED IT INTO A WEBSITE.

Shueib Ali — Sun, 05 Apr 2026 16:55:08 +0000

HOW TO PUBLISH A POWER BI REPORT AND EMBED IT INTO A WEBSITE.

Microsoft Power BI is a tool made by Microsoft that helps you turn data into useful information.
With Power BI, you can:

Connect data from Excel, databases, or online sources
Create charts, graphs, and dashboards
Understand trends and make better decisions It is popular because it is easy to use and shows data in a clear, visual way.

Power BI Publishing Process

Publishing means sharing your report with others online.

Create your report Build your charts and visuals in Power BI Desktop.
Save your work Save the report on your computer.
Sign in Log in to your Power BI account.
Click “Publish” Upload your report to the Power BI Service (online platform).
Choose a workspace Select where you want the report to be stored.
Share the report Send the link or give access to other people so they can view it.

Lets use the below dashboard as an example as we go step by step until we publish our report.

1. Creating a workspace

A workspace is like a folder in Microsoft Power BI where you keep your reports.

You create it online
It helps you organize your work
You can also invite others to work with you Think of it like a shared folder for reports.

2. Uploading and publishing your report

This means sending your report from your computer to Power BI online.

You click “Publish”
Your report is uploaded to the workspace
Now it is available on the internet

3. Generating the embed code

An embed code is a special code.

Power BI gives you this code
It looks like a small piece of text (HTML)
It is used to show your report on a website

4. Embedding the report on a website

This means putting your report on a website.

You copy the embed code
Paste it into your website
Your report will appear on that site

How Excel is Used in Real-World Data Analysis

Shueib Ali — Fri, 27 Mar 2026 21:43:23 +0000

How Excel is Used in Real-World Data Analysis

Excel is one of the most commonly used tools for real-world data analysis because it is simple, powerful, and widely available. Here’s an easy explanation of how it is used:

1. Organizing Data

Excel helps people store data in rows and columns (tables).
Example:

A company keeps sales records (date, product, price, quantity).
A school stores student marks.

👉 This makes data easy to read and manage.

2. Performing Calculations

Excel can quickly do math using formulas.
Examples:

SUM → adds numbers
AVERAGE → finds the mean
IF → makes decisions (e.g., pass/fail)

👉 Instead of calculating manually, Excel does it instantly.

3. Analyzing Data

Excel helps find useful information from data.
Examples:

Sorting data (highest to lowest sales)
Filtering (show only certain results)
Using Pivot Tables to summarize large data

👉 This helps businesses make better decisions.

4. Creating Charts and Graphs

Excel turns numbers into visuals like:

Bar charts
Pie charts
Line graphs

👉 Makes it easier to understand trends (e.g., sales growth over time).

5. Cleaning Data

Real-world data is often messy. Excel helps:

Remove duplicates
Fix errors
Format data properly

👉 Clean data = accurate analysis.

6. Forecasting & Trends

Excel can predict future results using past data.
Example:

A business predicts next month’s sales.

👉 Helps in planning and budgeting.

7. Real-Life Uses

Excel is used in many fields:

Business → sales tracking, budgeting
Accounting → financial reports
Journalism (like your background) → analyzing survey data
Marketing → customer analysis
Logistics/Aviation → tracking shipments and schedules

8. Below are examples of what excel is used for, using charts and slicers:

Learning Excel has completely changed the way I understand and interpret data. Before, numbers often felt overwhelming and difficult to analyze, but Excel has made it easier to organize, visualize, and draw meaningful conclusions from information. Tools like charts, formulas, and pivot tables have helped me see patterns and trends that I would have otherwise missed. Now, instead of just looking at raw data, I can confidently turn it into useful insights that support better decisions. Overall, Excel has transformed my approach to data from confusion to clarity, making me more efficient and analytical in my work.

For more on excel follow my Github.