Showing posts with label SQL vs NoSQL. Show all posts
Showing posts with label SQL vs NoSQL. Show all posts

Complete Solution: Fund Transfer with Transactions

Here's the complete solution to the Practice Task on Transactions and Consistency, for both SQL and MongoDB, including full code, output, and explanations.


Complete Solution: Fund Transfer with Transactions


๐Ÿ”น Part A: SQL Solution


๐Ÿ“ 1. Table Creation

CREATE TABLE Accounts (
  AccountID INT PRIMARY KEY,
  Name VARCHAR(100),
  Balance DECIMAL(10, 2)
);

๐Ÿ“ฅ 2. Insert Sample Data

INSERT INTO Accounts (AccountID, Name, Balance) VALUES
(1, 'Aisha', 1000.00),
(2, 'Ravi', 500.00);

๐Ÿ” 3. Transaction Script

START TRANSACTION;

-- Step 1: Deduct ₹200 from Aisha
UPDATE Accounts
SET Balance = Balance - 200
WHERE AccountID = 1;

-- Step 2: Add ₹200 to Ravi
UPDATE Accounts
SET Balance = Balance + 200
WHERE AccountID = 2;

-- Final step: Commit the transaction
COMMIT;

๐Ÿ“Œ If an error occurs during either update, you would use:

ROLLBACK;

๐Ÿ“ค Expected Output (after COMMIT):

SELECT * FROM Accounts;
AccountID Name Balance
1 Aisha 800.00
2 Ravi 700.00

✅ Data is consistent and correct. Both updates were applied atomically.


๐Ÿงช Optional Test Failure Scenario

Try this (invalid ID):

START TRANSACTION;

UPDATE Accounts SET Balance = Balance - 200 WHERE AccountID = 1;
UPDATE Accounts SET Balance = Balance + 200 WHERE AccountID = 999; -- wrong ID

ROLLBACK;

๐Ÿ” This would rollback everything. Aisha’s balance remains unchanged.


๐Ÿ”น Part B: MongoDB Solution


๐Ÿ“ฅ 1. Insert Documents

db.accounts.insertMany([
  { _id: 1, name: "Aisha", balance: 1000 },
  { _id: 2, name: "Ravi", balance: 500 }
]);

๐Ÿ” 2. MongoDB Transaction with Session

const session = db.getMongo().startSession();
session.startTransaction();

try {
  // Deduct from Aisha
  db.accounts.updateOne(
    { _id: 1 },
    { $inc: { balance: -200 } },
    { session }
  );

  // Add to Ravi
  db.accounts.updateOne(
    { _id: 2 },
    { $inc: { balance: 200 } },
    { session }
  );

  // Commit the transaction
  session.commitTransaction();
  print("✅ Transaction committed.");
} catch (error) {
  session.abortTransaction();
  print("❌ Transaction aborted:", error.message);
} finally {
  session.endSession();
}

๐Ÿ“ค Expected Output After Querying:

db.accounts.find().pretty();
[
  { _id: 1, name: "Aisha", balance: 800 },
  { _id: 2, name: "Ravi", balance: 700 }
]

✅ Like in SQL, both operations succeeded together.


๐Ÿ”ฅ Test Failure Scenario in MongoDB

Simulate a failure by modifying the second update with an invalid field:

db.accounts.updateOne(
  { _id: 999 },
  { $inc: { balance: 200 } },
  { session }
);

➡️ MongoDB will throw an error, and the first update will be rolled back automatically when abortTransaction() is called.


๐Ÿง  Final Thoughts

Operation SQL MongoDB
Begin Transaction START TRANSACTION session.startTransaction()
Commit COMMIT session.commitTransaction()
Rollback ROLLBACK session.abortTransaction()
Atomicity Built-in Requires session/replica set
Use Case High integrity systems (e.g. banks) NoSQL with transactional guarantees (MongoDB 4.0+)

Part 11: Database Security Basics

๐Ÿš€ In the next part of this tutorial series, we’ll shift focus to Database Security Basics. You'll learn about user roles, privileges, authentication, and how to protect your data from unauthorized access—an essential step for any real-world application.


๐Ÿ‘ If you found this tutorial helpful, consider following to the blog or sharing it with a friend or teammate learning databases.


๐Ÿ’ฌ Got questions or a better approach? Drop a comment below—I’d love to hear your thoughts!


๐Ÿ” Ready for more? Check out the next post on Database Security →

Full Practical Solution | SQL JOINs vs MongoDB Referencing

 Here’s the full solution to the Practice Assignment on SQL JOINs vs MongoDB Referencing, including expected outputs and explanations.


Solution: SQL JOINs vs MongoDB Referencing


๐Ÿ”น Part A: SQL Solution


1. Tables Created

CREATE TABLE Authors (
  AuthorID INT PRIMARY KEY,
  Name VARCHAR(100)
);

CREATE TABLE Books (
  BookID INT PRIMARY KEY,
  Title VARCHAR(150),
  AuthorID INT,
  FOREIGN KEY (AuthorID) REFERENCES Authors(AuthorID)
);

2. Sample Data Inserted

INSERT INTO Authors (AuthorID, Name) VALUES
(1, 'George Orwell'),
(2, 'J.K. Rowling');

INSERT INTO Books (BookID, Title, AuthorID) VALUES
(101, '1984', 1),
(102, 'Animal Farm', 1),
(103, 'Harry Potter', 2);

3. Query Using JOIN

SELECT Books.Title, Authors.Name
FROM Books
JOIN Authors ON Books.AuthorID = Authors.AuthorID;

๐Ÿ“ค Expected Output:

Title Name
1984 George Orwell
Animal Farm George Orwell
Harry Potter J.K. Rowling

๐Ÿ” This result shows book titles along with the author's name using a JOIN between Books and Authors.


๐Ÿ”น Part B: MongoDB Solution

Note: MongoDB doesn’t support traditional SQL-style JOINs. Instead, you can simulate them using referencing (multiple queries) or the $lookup operator in aggregation pipelines.

1. Insert Documents

Authors Collection:

db.authors.insertMany([
  { _id: 1, name: "George Orwell" },
  { _id: 2, name: "J.K. Rowling" }
]);

Books Collection:

db.books.insertMany([
  { title: "1984", author_id: 1 },
  { title: "Animal Farm", author_id: 1 },
  { title: "Harry Potter", author_id: 2 }
]);

2. Simulating a JOIN

Step 1: Find Author Document

const author = db.authors.findOne({ name: "George Orwell" });

๐Ÿ” Expected Output:

{
  "_id": 1,
  "name": "George Orwell"
}

Step 2: Find Books by Author ID

db.books.find({ author_id: author._id });

๐Ÿ” Expected Output:

[
  { "title": "1984", "author_id": 1 },
  { "title": "Animal Farm", "author_id": 1 }
]

๐Ÿ“ To match SQL output, you'd display:

Title Author Name
1984 George Orwell
Animal Farm George Orwell

๐Ÿ”ธ Optional: Embedded Model Query

If books were stored like this:

db.books.insertOne({
  title: "1984",
  author: {
    id: 1,
    name: "George Orwell"
  }
});

Then your query:

db.books.find({ "author.name": "George Orwell" });

๐Ÿ” Would return documents with both book title and embedded author name directly, simulating a denormalized JOIN.


✅ Summary

Concept SQL (JOIN) MongoDB (Referencing)
How data is linked Foreign key + JOIN Manual field referencing (or embedding)
Queries One SQL JOIN query Two MongoDB queries (or use $lookup)
Output Title + Author from joined tables Title + Author via multiple reads or embed
Query Complexity Moderate (JOIN syntax) Simple (but more steps)

Note: MongoDB’s $lookup can perform JOIN-like operations in a single aggregation query, but it's more complex and typically used for reporting or complex analytics.


  • Move to Part 10: Transactions and Consistency

SQL Joins vs NoSQL Referencing: Understanding Data Relationships Easily

๐Ÿ”ท Part 9: Relationships in SQL vs Referencing in NoSQL


This is a key concept that helps readers understand how to link data across tables (SQL) or documents (NoSQL), which is essential for real-world applications like e-commerce, school systems, or social networks.


๐Ÿ“ Introduction

Most databases store related data: customers and orders, students and classes, or products and reviews.

  • In SQL, we use foreign keys and JOINs to link tables.

  • In NoSQL (like MongoDB), we use manual referencing or embedding to connect documents.

Understanding these relationship methods is essential for building scalable and maintainable database designs.


๐Ÿ”ธ 1. Relationships in SQL (Using Joins)

SQL supports:

  • One-to-One

  • One-to-Many

  • Many-to-Many
    …through foreign keys and JOINs.


✅ Example: Students and Classes (One-to-Many)


๐Ÿ“ Tables:

CREATE TABLE Students (
  StudentID INT PRIMARY KEY,
  Name VARCHAR(100)
);

CREATE TABLE Classes (
  ClassID INT PRIMARY KEY,
  StudentID INT,
  Subject VARCHAR(50),
  FOREIGN KEY (StudentID) REFERENCES Students(StudentID)
);

๐Ÿ” Fetching Related Data:

SELECT Students.Name, Classes.Subject
FROM Students
JOIN Classes ON Students.StudentID = Classes.StudentID;

➡️ This JOIN connects each student to their class records.


๐Ÿ”น 2. Relationships in MongoDB (Referencing)

In NoSQL, there are two ways to handle related data:

✅ A. Manual Referencing (Normalized approach)

// students collection
{
  _id: ObjectId("stu101"),
  name: "Aisha Khan"
}

// classes collection
{
  student_id: ObjectId("stu101"),
  subject: "Math"
}

To retrieve, you need two queries:

const student = db.students.findOne({ name: "Aisha Khan" });
const classes = db.classes.find({ student_id: student._id });

✅ B. Embedding (Denormalized approach)

{
  name: "Aisha Khan",
  classes: [
    { subject: "Math" },
    { subject: "Science" }
  ]
}

➡️ This is faster for reads, but harder to update if the same class is shared across students.


๐Ÿ”„ Comparison Table: SQL Joins vs NoSQL Referencing


Feature SQL (Joins) MongoDB (Referencing/Embedding)
Relationship type Foreign keys & JOINs Manual references or embedded documents
Querying Single JOIN query Multiple queries or nested documents
Read performance May be slower due to joins Fast if embedded
Update complexity Centralized & simple More complex with embedded structures
Data duplication Minimal Possible with embedding

๐Ÿง  Best Practices


✅ Use SQL when:

  • Data relationships are complex and frequently queried together

  • You want strong referential integrity

✅ Use NoSQL referencing when:

  • You need flexibility or horizontal scaling

  • Data isn’t always accessed together


๐Ÿ“Œ Quick Summary

  • SQL uses foreign keys and JOINs for linking tables.
  • NoSQL uses manual referencing or embedding to relate documents.
  • Joins are powerful but may affect performance in large datasets.
  • Embedding is fast but risks data duplication and update complexity.


✅ What’s Next?

In Part 10, we’ll dive into Transactions and Consistency — how SQL ensures ACID compliance, and how NoSQL handles consistency across distributed systems.


    Click Next for:
  •  practice assignment comparing SQL JOINs and MongoDB referencing

Normalization vs Denormalization: SQL vs MongoDB Practice task Solution

Solution: Normalization vs Denormalization Practice


This tutorial provides the complete solution to the earlier practice task comparing normalization in SQL and denormalization in MongoDB. If you haven't attempted the exercise, click here to try it first.

Here's the full solution to the Normalization vs Denormalization practice exercise using both SQL (normalized) and MongoDB (denormalized) approaches.


๐Ÿ”น Task A: Normalized Design (SQL Style)


1. Table Structure

Authors Table

CREATE TABLE Authors (
  AuthorID INT PRIMARY KEY,
  AuthorName VARCHAR(100),
  Email VARCHAR(100)
);

Books Table

CREATE TABLE Books (
  BookID INT PRIMARY KEY,
  Title VARCHAR(150),
  AuthorID INT,
  FOREIGN KEY (AuthorID) REFERENCES Authors(AuthorID)
);


2. Sample Data Insertion

INSERT INTO Authors (AuthorID, AuthorName, Email) VALUES
(1, 'Jane Austen', 'jane.austen@example.com'),
(2, 'Mark Twain', 'mark.twain@example.com');

INSERT INTO Books (BookID, Title, AuthorID) VALUES
(101, 'Pride and Prejudice', 1),
(102, 'Emma', 1),
(103, 'Adventures of Tom Sawyer', 2);


3. SQL Query to Join Data

SELECT Books.Title, Authors.AuthorName
FROM Books
JOIN Authors ON Books.AuthorID = Authors.AuthorID;

๐Ÿ” Expected Output:


+----------------------------+-------------+
| Title                      | AuthorName  |
+----------------------------+-------------+
| Pride and Prejudice        | Jane Austen |
| Emma                       | Jane Austen |
| Adventures of Tom Sawyer   | Mark Twain  |
+----------------------------+-------------+


๐Ÿ”น Task B: Denormalized Design (MongoDB Style)


1. Book Documents with Embedded Author Info

db.books.insertMany([
  {
    book_id: 101,
    title: "Pride and Prejudice",
    author: {
      author_id: 1,
      name: "Jane Austen",
      email: "jane.austen@example.com"
    }
  },
  {
    book_id: 102,
    title: "Emma",
    author: {
      author_id: 1,
      name: "Jane Austen",
      email: "jane.austen@example.com"
    }
  },
  {
    book_id: 103,
    title: "Adventures of Tom Sawyer",
    author: {
      author_id: 2,
      name: "Mark Twain",
      email: "mark.twain@example.com"
    }
  }
]);


2. MongoDB Query to Find Books by Author Name

db.books.find({ "author.name": "Jane Austen" }, { title: 1, _id: 0 });

๐Ÿ” Expected Output:


[
  { title: "Pride and Prejudice" },
  { title: "Emma" }
]

๐Ÿ”ธ Bonus Challenge: Updating Author Email


๐Ÿง  In Normalized (SQL):

  • You update once in the Authors table:

UPDATE Authors
SET Email = 'new.jane.austen@example.com'
WHERE AuthorID = 1;

Change is reflected across all books automatically (since books reference the author ID).


๐Ÿง  In Denormalized (MongoDB):

  • You must update all documents containing the embedded author data:

db.books.updateMany(
  { "author.author_id": 1 },
  { $set: { "author.email": "new.jane.austen@example.com" } }
);

❗ Requires multiple updates — more effort, but often better read performance.

๐Ÿ”— Want a deeper look at MongoDB's schema design strategies? Check out our MongoDB Schema Design Guide.


✅ Summary: What You’ve Learned


Concept SQL MongoDB (NoSQL)
Data Structure Normalized into separate tables Denormalized with embedded docs
Redundancy Low High
Update Effort One update in Authors table Multiple document updates
Read Simplicity Needs JOIN Direct query, no joins needed

๐Ÿ That’s it! You now understand the core differences between normalized and denormalized database design using SQL and MongoDB. Got questions or want more practice? Let us know in the comments or check out more tutorials in our Learning Series.


Practice Exercise on SQL Normalization vs NoSQL Denormalization (With Sample Data)

 

๐Ÿงช Practice Exercise: Normalization vs Denormalization


๐ŸŽฏ Objective:

Understand how to design data using normalization (SQL-style) and denormalization (NoSQL-style).


๐Ÿ”น Scenario:

You are managing a simple Library Database with Books and Authors.


๐Ÿ“Œ Task A: Normalize the Data (SQL style)

  1. Create tables for Books and Authors.

  2. Each book should store a reference (AuthorID) to the author.

  3. Insert sample data for 2 authors and 3 books (some books by the same author).

  4. Write an SQL query to fetch each book’s title with its author’s name.


๐Ÿ“Œ Task B: Denormalize the Data (NoSQL style)

  1. Create a MongoDB collection called books.

  2. Store author details embedded inside each book document.

  3. Insert the same sample data for 2 authors and 3 books as above.

  4. Write a MongoDB query to find all books by a specific author’s name.


๐Ÿ”น Bonus Challenge

  • Consider the scenario when an author’s information (e.g., email) changes.

  • Explain briefly how this update would differ in normalized vs denormalized models.


๐Ÿ“ Sample Data to Use


AuthorID AuthorName Email
1 Jane Austen jane.austen@example.com
2 Mark Twain mark.twain@example.com

BookID Title AuthorID
101 Pride and Prejudice 1
102 Emma 1
103 Adventures of Tom Sawyer 2


๐Ÿ’ก Hint: Use a JOIN to connect books and authors in SQL.



✍️ Tried the exercise? Share your SQL or MongoDB approach in the comments below.


Next: solution for this exercise 


Normalization vs Denormalization in Databases: SQL vs NoSQL Explained Simply


๐Ÿ”ท Part 7: Normalization vs Denormalization – Understanding Data Structure in SQL and NoSQL


This part will help beginners and pros understand how data is structured differently in these databases(SQL and NoSQL) and impacts performance, flexibility, and maintenance.


๐Ÿ“ Introduction

Data organization is a cornerstone of database efficiency in both SQL and NoSQL systems. Two essential techniques for structuring data are Normalization and Denormalization. Techniques like normalization (used in relational databases) and denormalization (common in document-based NoSQL databases like MongoDB) affect performance, scalability, and data integrity.

  • Normalization is commonly used in SQL databases to reduce data redundancy by organizing data into related tables.

  • Denormalization is often preferred in NoSQL databases like MongoDB, where embedding data improves read performance at the cost of some duplication.

In this post, we’ll break down these concepts, explain their pros and cons, and provide examples to make it crystal clear.


๐Ÿ”ธ 1. What is Normalization in SQL Databases?

Normalization is the process of structuring a relational database so that:

  • Data is stored in multiple related tables

  • Each table contains data about one type of entity

  • Redundancy is minimized

  • Integrity and consistency are ensured


๐Ÿ“ Example: Students and Courses

  • Students Table: Stores student details

  • Courses Table: Stores course details

  • Enrollments Table: Links students to courses (many-to-many relationship)

This normalized structure in SQL avoids repeating course information for every student, ensuring data integrity and reducing redundancy.


๐Ÿ”ธ 2. What is Denormalization?

Denormalization is the process of intentionally introducing redundancy by:

  • Combining related data into single documents or tables

  • Embedding data to optimize read performance

  • Simplifying queries by reducing joins


๐Ÿ“ Example: MongoDB Student Document

Here is a denormalized NoSQL document structure example using MongoDB.

Instead of separate collections, a student document contains embedded courses and marks:

{
  "student_id": 101,
  "name": "Aisha Khan",
  "class": "10A",
  "courses": [
    { "course_id": 301, "title": "Math", "score": 85 },
    { "course_id": 302, "title": "Science", "score": 90 }
  ]
}


๐Ÿ”ธ 3. Pros and Cons

Aspect Normalization (SQL) Denormalization (NoSQL)
Data Redundancy Low High (intentional duplication)
Query Complexity More complex (joins needed) Simple (embedded data, fewer joins)
Data Consistency Easier to maintain More challenging to keep consistent
Performance Good for writes, complex reads Optimized for reads, slower writes
Flexibility Schema-based, less flexible Schema-less, highly flexible

๐Ÿ”ธ 4. When to Use Which?

  • Use Normalization (SQL):
    When data integrity is crucial, and you expect complex queries involving relationships.

  • Use Denormalization (NoSQL):
    When performance on reads is critical, and you want flexible, evolving schemas.


๐Ÿง  Summary

Understanding the difference between normalization in SQL and denormalization in NoSQL helps you choose the right database structure and design models that balance performance and consistency for your project. Choosing between normalization and denormalization depends on your project needs—whether you prioritize performance or data integrity.

If you have not gone through previous tutorial read: Part-6: CRUD Operations in SQL vs NoSQL – A Beginner's Guide


Task for you:

    Try normalizing a sample dataset and share your experience.

    Leave a comment below if you have used either in your projects.



✅ What’s Next?

In Part 8, we shall explore Indexing and Query Optimization to speed up your database performance.



  • Practice exercises for normalization and denormalization


CRUD Operations in SQL vs NoSQL Explained with Simple Examples

๐Ÿ”ท Part 6: CRUD Operations in SQL vs NoSQL – A Beginner's Guide


This post will:

  • ✅ Compare how to CreateReadUpdate, and Delete data in both systems

  • ✅ Use simple, realistic examples

  • ✅ Help beginners understand key syntax differences


๐Ÿ“ 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.


๐Ÿ”ธ Assumed Data Structure

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

  • StudentID

  • Name

  • Class

  • Marks (embedded in NoSQL)


๐Ÿ“Œ 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 }
  ]
});

๐Ÿ“Œ 2. READ – Retrieving Data


✅ SQL

SELECT * FROM Students WHERE StudentID = 1;

✅ NoSQL

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

๐Ÿ“Œ 3. UPDATE – Changing Data


✅ SQL

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

✅ NoSQL

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

๐Ÿ“Œ 4. DELETE – Removing Data


✅ SQL

DELETE FROM Students
WHERE StudentID = 1;

✅ NoSQL

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

๐Ÿ“Š 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({ ... })

๐Ÿง  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

✅ What’s Next?

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


NoSQL Databases Explained: Beginner's Guide to Flexible Data Storage (With Examples)


๐Ÿ”ท Part 5: Introduction to NoSQL Databases – A Flexible Alternative to Relational Models


๐Ÿ“ Introduction

So far, we’ve learned how relational databases work with structured tables, rows, and columns. But what if your data doesn’t fit neatly into tables?

Welcome to NoSQL — a more flexible way to store and manage data. Whether you're building apps with real-time feeds, handling massive data from sensors, or creating dynamic content, NoSQL databases offer a powerful solution.


๐Ÿ”น What is NoSQL?

NoSQL stands for “Not Only SQL.”
It refers to a group of databases that store and retrieve data in ways other than traditional tabular formats (used by relational databases).

NoSQL is often used for:

  • Unstructured or semi-structured data

  • High-speed, high-volume applications

  • Scalable systems like social networks, IoT platforms, or real-time analytics


๐Ÿงฐ Types of NoSQL Databases

  1. Document-Based – Stores data as JSON-like documents (e.g., MongoDB)

  2. Key-Value Stores – Stores data as simple key-value pairs (e.g., Redis)

  3. Column-Family Stores – Similar to tables but with flexible columns (e.g., Cassandra)

  4. Graph Databases – Designed to represent complex relationships (e.g., Neo4j)


๐Ÿ” Document-Based NoSQL Example (MongoDB)

Here’s how a student record might look in a NoSQL document store:

{
  "student_id": 1,
  "name": "Aisha",
  "class": "10A",
  "marks": [
    { "subject": "Math", "score": 85 },
    { "subject": "English", "score": 88 }
  ]
}

๐Ÿ”„ Compare this to the relational model where marks are in a separate table. In NoSQL, all related data can be stored together in one document.


๐Ÿ†š NoSQL vs SQL – Key Differences

Feature SQL (Relational) NoSQL (Non-Relational)
Structure Fixed tables and schemas Flexible, schema-less
Data Format Rows and columns JSON, key-value, graphs, etc.
Relationships Supports JOINs Embeds or references data
Scalability Vertical (scale-up) Horizontal (scale-out)
Best For Structured, consistent data Dynamic, varied, big data

๐Ÿ“š Real-Life Analogy

Imagine SQL is like organizing books in a library, where every book must follow a strict format (title, author, ISBN).

NoSQL is like a digital folder, where each file (document) can have different details — one may have a title and summary, another may have a title, author, and image — and that’s perfectly okay.


๐Ÿง  When to Use NoSQL?

Use NoSQL when:

  • Data structure is not fixed

  • You're dealing with lots of rapidly changing data

  • You need fast performance and easy scalability

  • Relationships between data are simple or embedded


๐Ÿง  Recap

  • NoSQL databases offer flexibility and speed for non-tabular data

  • They store data in formats like documents or key-value pairs

  • Great for modern apps, real-time systems, and unstructured data

  • Popular tools: MongoDB, Redis, Cassandra, Firebase


✅ What’s Next?

In Part 6, we’ll perform real-world CRUD operations in both SQL and NoSQL — showing how to Create, Read, Update, and Delete data with easy examples.


Practice Set: MongoDB Document Modeling Practice


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