Databases don’t always have to be tables and rows — welcome to the world of MongoDB, where data lives as flexible documents!
In this post, we’ll explore how to perform the four basic operations — Create, Read, Update, and Delete (CRUD) — using a simple college student schema in MongoDB.
We’ll be using MongoDB Atlas, a free cloud-based platform that allows you to create and manage MongoDB databases online — no installation needed!
- Steps to Set Up.
- Head over to MongoDB Atlas and sign up for a free account.
- Create a new Cluster (choose the free M0 shared tier).
- Once your cluster is ready, go to Collections → Create Database.
- Name your database collegeDB and add a collection named students.
- Open the Atlas Data Explorer — this is where you’ll run all your MongoDB commands.
Schema: Student Collection
We’ll use a collection called students, where each document looks like this:
{
  "student_id": "S001",
  "name": "Santhosh",
  "age": 20,
  "department": "CSBS",
  "year": 2,
  "cgpa": 9
}
Create (Insert Documents)
We’ll begin by inserting five sample student records.
Read (Query Documents)
Once data is inserted, let’s retrieve it using different query conditions.
Display All Student Records
db.students.find();
Find Students with CGPA > 8
db.students.find({ cgpa: { $gt: 8 } });
Find Students from the Computer Science Department
db.students.find({ department: "CSBS" });
Update (Modify Documents)
Update a Specific Student’s CGPA
db.students.updateOne(
  { student_id: "S001" },
  { $set: { cgpa: 8 } }
);
Increase the Year of Study for All 3rd-Year Students
db.students.updateMany(
  { year: 3 },
  { $inc: { year: 1 } }
);
Delete (Remove Documents)
Delete One Student by ID
db.students.deleteOne({ student_id: "S005" });
Delete All Students with CGPA < 7.5
Summary
✔ Create → Add new student documents
✔ Read → Query and filter documents efficiently
✔ Update → Modify existing records dynamically
✔ Delete → Safely remove unwanted data
MongoDB makes database handling simple, fast, and flexible — perfect for modern applications that evolve with changing data needs.
 
 
              














 
    
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