Introduction

As part of my SQL learning journey, I built a simple school database called Greenwood Academy using PostgreSQL. The goal of this project was to practice the most important SQL concepts, including database design, inserting data, filtering records, using operators, counting records, and applying conditional logic with CASE WHEN.

In this article, I’ll walk through the project step by step and explain what I learned.

Why This Project?

Real-world organizations store large amounts of structured data. A school, for example, needs to manage:

  • Students
  • Subjects
  • Exam results
  • Teachers
  • Academic departments

By modeling this information in a relational database, we can query and analyze it efficiently.

Lets dive in

Step 1 : Creating the Database Schema (DDL)
I started by creating a schema named Greenwood_academy. A schema helps group related database objects together.

create  schema greenwood_academy;
set search_path to greenwood_academy;

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Then I defined the three main tables:

  1. Students 
create table greenwood_academy.students(
 student_id serial 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)
 );

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  1. Subjects 
create table greenwood_academy.subjects(
subject_id serial primary key,
subject_name varchar(100) unique,
department varchar (50),
teacher_name varchar (100),
credits int
);

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  1. Exam Results Table 
create table greenwood_academy.exam_results(
result_id serial primary key,
student_id int not null,
subject_id int not null,
marks int not null,
exam_date date,
grade varchar (2),
foreign key(student_id) references greenwood_academy.students(student_id),
foreign key (subject_id) references greenwood_academy.subjects(subject_id)
);

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To make the tables relational we use a Foriegn Keys.

What I learned

  • SERIAL automatically generates unique IDs.
  • PRIMARY KEY uniquely identifies each record.
  • FOREIGN KEY creates relationships between tables.
  • NOT NULL ensures important fields cannot be left empty.

Modifying tables with ALTER
I also practiced modifying existing tables using the ALTER TABLE statement. In this project, I renamed a column, added a column and dropped a column, which helped me understand how database schemas can be updated as requirements change.

-- Alter table changing type
alter table greenwood_academy.students
add column phone_number varchar(20);

-- Renaming a column
alter table greenwood_academy.subjects
rename column credits to credit_hours;

-- dropping a column
alter table greenwood_academy.students
drop column phone_number;

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Step 2 : Inserting Data (DML)
With the structure in place, it's time to add data. INSERT statements can take multiple rows at once, which keeps things concise.

Inserting values into the Students table

insert into greenwood_academy.students(first_name,last_name,
gender,date_of_birth,class,city)
values ('Amina','Wanjiku','F','2008-03-12','Form 3','Nairobi'),
('Brain','Ochieng','M','2007-07-25','Form 4','Mombasa'),
('Cynthia','Mutua','F','2008-11-05','Form 3','Kisumu'),
('David','Kamau','M','2007-02-18','Form 4','Nairobi'),
('Esther','Akinyi','F','2009-09-14','Form 2','Nakuru'),
('Felix','Otieno','M','2009-09-14','Form 2','Eldoret'),
('Grace','Mwangi','F','2008-01-22','Form 3','Nairobi'),
('Hassan','Abdi','M','2007-04-09','Form 4','Mombasa'),
('Ivy','Chebet','F','2009-12-01','Form 2','Nakuru'),
('James','Kariuki','M','2008-08-17','Form 3','Nairobi');

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Inserting values into the Subjects table

insert into greenwood_academy.subjects(subject_name,department,
teacher_name,credit_hours)
values ('Mathematics','Sciences','Mr. Njoroge',4),
('English','Languages','Ms. Adhiambo',3),
('Biology','Sciences','Ms. Otieno',4),
('History','Humanities','Mr. Waweru',3),
('Kiswahili','Languages','Ms. Nduta',3),
('Physics','Sciences','Mr. Kamande',4),
('Geography','Humanities','Ms. Chebet',3),
('Chemistry','Sciences','Ms. Muthoni',4),
('Computer Studies','Sciences','Mr. Oduya',3),
('Business Studies','Humanities','Ms. Wangari',3);

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Inserting values into the Exam Results table

insert into greenwood_academy.exam_results(student_id,subject_id,marks,
exam_date,grade)
values(1,1,78,'2024-03-15','B'),
(1,2,85,'2024-03-16','A'),
(2,1,92,'2024-03-15','A'),
(2,3,55,'2024-03-17','C'),
(3,2,49,'2024-03-16','D'),
(3,4,71,'2024-03-18','B'),
(4,1,88,'2024-03-14','A'),
(4,6,63,'2024-03-19','C'),
(5,5,39,'2024-03-20','F'),
(6,9,95,'2024-03-21','A');

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Once the tables are populated, a quick sanity check confirms everything landed correctly.

select * from greenwood_academy.students;
select * from greenwood_academy.subjects;
select * from greenwood_academy.exam_results;

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Data isn't static, though. People move, mistakes happen, and records get cancelled. That's where UPDATE and DELETE come in.

-- Updating Esther Akinyi city from Nakuru to Nairobi. 

update greenwood_academy.students
set city = 'Nairobi'
where student_id = 5;

-- updating the exam_results table
update greenwood_academy.exam_results
set marks = 59, grade = 'C'
where result_id = 5;

-- Deleting result_id 9 
delete from greenwood_academy.exam_results
where result_id = 9;

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What I learned

  • How to insert multiple rows in a single statement.
  • The importance of matching the order of columns and values.
  • Using realistic sample data for testing queries

Step 3 : Querying the Data
I used SQL queries to retrieve and filter relevant data from the database for analysis and reporting.

-- all students in form 4
select first_name, last_name
from greenwood_academy.students
where class = 'Form 4';

-- subjects in the sciences department
select subject_name
from greenwood_academy.subjects
where department = 'Sciences';

-- exams results where marks is greater then of equal to 70
select *
from greenwood_academy.exam_results
where marks >=70;

-- Finding all females
select first_name,last_name
from greenwood_academy.students
where gender = 'F';

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You can combine conditions with AND and OR to ask more specific questions.

-- students in form 3 and from Nairobi
select first_name,last_name
from greenwood_academy.students
where class = 'Form 3' and city = 'Nairobi';

-- students that are either in form 2 or form 4
select first_name,last_name
from greenwood_academy.students
where class = 'Form 2' or class = 'Form 4';

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What I learned

  • WHERE filters records.
  • Comparison operators (=, >=, <=) are essential for analysis.
  • Using AND and OR operators helps understand how to combine multiple conditions to create more accurate and flexible queries.
  • Selecting only the columns you need improves readability.

Step 4 : Range, Membership & Search Operators
The BETWEEN operator is used to retrieve values that fall within a specified range.

-- marks between 50 and 80 (inclusive)
select *
from greenwood_academy.exam_results
where marks between 50 and 80;

-- exam date between 15th march 2024 and 18th march 2024
select * 
from greenwood_academy.exam_results
where exam_date between '2024-03-15' and '2024-03-18';

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IN and NOT IN for membership checks much cleaner than a chain of OR

-- students who live in nairobi,mombasa,or Kisumu
select first_name,last_name
from greenwood_academy.students
where city in ('Nairobi','Mombasa','Kisumu');

-- students not in form 2 or form 3
select first_name,last_name
from greenwood_academy.students
where class not in ('Form 2', 'Form 3');

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LIKE for pattern matching % is a wildcard for any characters.

-- students whose first name starts with a or e
select first_name
from greenwood_academy.students
where first_name like 'A%' or first_name like 'E%';

-- subject that contain the word 'studies'
select subject_name
from greenwood_academy.subjects
where subject_name like '%Studies%';

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What I Learned

  • BETWEEN simplifies range filtering.
  • IN is cleaner than multiple OR conditions.
  • LIKE is useful for pattern matching.

Step 5 : Counting Records
Counting helps turn raw records into summaries.

-- number of students from form 3
select count(*) as num_students
from greenwood_academy.students
where class = 'Form 3';

-- number of exams with marks 70 and above
select count(*) as num_marks_above_70
from greenwood_academy.exam_results
where marks >=70;

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What I learned

  • COUNT(*) returns the number of matching rows.

Step 6 : Categorizing Data with CASE WHEN
The CASE statement is used to add conditional logic inside SQL queries. It checks conditions one by one and returns a value as soon as a matching condition is found.

Grading Exams Performance

select 
    result_id,
    marks,
case 
    when marks >= 80 then 'Distinction'
    when marks >= 60 then 'Merit'
    when marks >= 40 then 'Pass'
    else 'Fail'
end as class_performance
from greenwood_academy.exam_results;

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Classifying Class Level

select 
    first_name,
    last_name,
    class,
case 
    when class = 'Form 3' or class = 'Form 4' then 'Senior'
    when class = 'Form 2' or class = 'Form 1' then 'Junior'
end as student_level
from greenwood_academy.students;

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What I learned

  • CASE WHEN works like an IF-ELSE statement inside SQL.
  • CASE WHEN helps categorize data or transform values dynamically.
  • CASE WHEN can be used in SELECT, UPDATE, ORDER BY and other clauses.

Conclusion

Building the Greenwood Academy database gave me practical experience in designing relational databases, inserting and querying data, using operators such as AND and OR, applying aggregate functions, and implementing conditional logic with CASE WHEN. This project helped me move beyond SQL theory and understand how databases are structured and analyzed in real-world scenarios