MongoDB Document Modeling – Practice Assignment

 

🧾 MongoDB Document Modeling – Practice Assignment

Title: Design a NoSQL Document Structure for a School Management System

Level: Beginner to Intermediate
Objective: Learn how to organize and model data using MongoDB’s document structure.

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📌 Assignment Brief

You are designing a database for a School Management System using MongoDB. Your task is to model documents for students, courses, and marks using a proper JSON-like structure.

You need to:

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🔹 1. Create a students collection

Each student document should include:

• student_id (Number)

• name (String)

• class (String)

• date_of_birth (ISO Date format)

• contact_info (Nested object with phone and email)

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🔹 2. Create a courses collection

Each course document should include:

• course_id (Number)

• title (String)

• teacher_name (String)

• schedule (Array of days, e.g., ["Mon", "Wed", "Fri"])

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🔹 3. Embed marks within each student document

Instead of a separate marks collection, embed a list of subjects and scores within the student document like this:

"marks": [
  { "subject": "Math", "score": 85 },
  { "subject": "Science", "score": 90 }
]

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🧠 Optional Bonus Task

Write a MongoDB query to:

• Find students who scored above 80 in Math

• Project only the student's name and Math score

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🧪 Expected Sample Document (students collection)

{
  "student_id": 101,
  "name": "Aisha Khan",
  "class": "10A",
  "date_of_birth": "2008-05-10T00:00:00Z",
  "contact_info": {
    "phone": "9876543210",
    "email": "aisha.khan@example.com"
  },
  "marks": [
    { "subject": "Math", "score": 85 },
    { "subject": "Science", "score": 90 }
  ]
}

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🧠 Why This Matters

• Reinforces document structure and nesting in MongoDB

• Helps students understand when to embed vs reference

• Mirrors real-world use cases like student performance tracking

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solution :

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CRUD Operations in SQL vs NoSQL Explained with Simple Examples

🔷 Part 6: CRUD Operations in SQL vs NoSQL – A Beginner's Guide

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This post will:

• ✅ Compare how to Create, Read, Update, and Delete data in both systems

• ✅ Use simple, realistic examples

• ✅ Help beginners understand key syntax differences

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📍 Introduction

In any application or system that works with data, you need to perform four basic operations:

• Create new data

• Read existing data

• Update current data

• Delete data you no longer need

These are called CRUD operations — and whether you're using SQL or NoSQL, they form the core of working with databases.

Let’s explore these operations in both SQL (like MySQL/PostgreSQL) and NoSQL (like MongoDB) with clear, side-by-side examples.

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🔸 Assumed Data Structure

We’ll use a simple students table/collection with:

• StudentID

• Name

• Class

• Marks (embedded in NoSQL)

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📌 1. CREATE – Inserting Data

✅ SQL (MySQL/PostgreSQL)

INSERT INTO Students (StudentID, Name, Class)
VALUES (1, 'Aisha', '10A');

✅ NoSQL (MongoDB)

db.students.insertOne({
  student_id: 1,
  name: "Aisha",
  class: "10A",
  marks: [
    { subject: "Math", score: 85 }
  ]
});

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📌 2. READ – Retrieving Data

✅ SQL

SELECT * FROM Students WHERE StudentID = 1;

✅ NoSQL

db.students.findOne({ student_id: 1 });

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📌 3. UPDATE – Changing Data

✅ SQL

UPDATE Students
SET Class = '10B'
WHERE StudentID = 1;

✅ NoSQL

db.students.updateOne(
  { student_id: 1 },
  { $set: { class: "10B" } }
);

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📌 4. DELETE – Removing Data

✅ SQL

DELETE FROM Students
WHERE StudentID = 1;

✅ NoSQL

db.students.deleteOne({ student_id: 1 });

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📊 Quick Comparison Table

Operation	SQL Syntax	MongoDB (NoSQL) Syntax
Create	INSERT INTO ... VALUES (...)	insertOne({ ... })
Read	SELECT * FROM ... WHERE ...	findOne({ ... })
Update	UPDATE ... SET ... WHERE ...	updateOne({ ... }, { $set: { ... } })
Delete	DELETE FROM ... WHERE ...	deleteOne({ ... })

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🧠 Summary

SQL	NoSQL (MongoDB)
Structured (tables/rows)	Flexible (documents/JSON)
Uses SQL language	Uses JavaScript-like syntax
Schema-based	Schema-less
Great for complex queries	Great for rapid, dynamic data

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✅ What’s Next?

In Part 7, we’ll explore Normalization vs Denormalization — how SQL and NoSQL structure data differently for performance and flexibility.

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