Module 4: Introduction to MongoDB

 Lesson 13: MongoDB Introduction

 

 

 Objective:

Introduction to MongoDB as a NoSQL database for MERN stack.


MongoDB, a prominent NoSQL database, offers flexibility and scalability for modern web applications in the MERN (MongoDB, Express.js, React, Node.js) stack. Let's dive into its fundamentals and advantages:


 Basics of NoSQL databases

- NoSQL: Stands for "Not Only SQL," diverging from traditional relational databases (SQL) by offering a more flexible data model.

- Types: NoSQL databases include document-based (like MongoDB), key-value stores, column-oriented, and graph databases.


 Advantages of MongoDB

- Schema flexibility: No predefined schema allows for easy modification and addition of fields without downtime.

- Scalability: Horizontal scaling and sharding enable handling large volumes of data and high traffic.

- Querying: Powerful querying capabilities with support for complex queries.


 Comparison with SQL databases

- Structure: SQL databases use tables and rows, while MongoDB uses collections and documents.

- Schema: SQL databases have a fixed schema, whereas MongoDB has a dynamic schema.

- Scaling: MongoDB provides better scalability for large-scale applications.


 Lesson 14: Environment Setup of MongoDB

 

 

 Objective:

Learn how to set up and configure the MongoDB environment.


 Installation and configuration

- Installation: Download and install MongoDB locally on your machine or set it up through package managers.

- Configuration: Adjust settings such as data directory, port, and authentication.


 Using MongoDB Atlas for cloud-based setup

- MongoDB Atlas: A cloud-based platform that manages MongoDB databases, offering scalability, security, and automated backups.

- Setup: Create an account, set up clusters, and manage databases in the cloud.


 Connecting MongoDB with Node.js

- MongoDB drivers: Use MongoDB Node.js drivers like `mongodb` or `mongoose` to connect Node.js applications with MongoDB databases.

- Connection: Establish connections, perform CRUD operations, and handle data in Node.js.


 Lesson 15: Database Structure

 

 

 Objective:

Understand the structure and organization of databases in MongoDB.


 Collections and documents

- Collections: Equivalent to tables in SQL databases, hold documents.

- Documents: JSON-like data structures containing key-value pairs, forming the basic unit of data in MongoDB.


 CRUD operations (Create, Read, Update, Delete)

- Create: `insertOne`, `insertMany` to add data.

- Read: `find`, `findOne` for retrieving data.

- Update: `updateOne`, `updateMany` for modifying data.

- Delete: `deleteOne`, `deleteMany` for removing data.


 Indexing and querying in MongoDB

- Indexes: Enhance query performance by indexing fields for quicker retrieval.

- Querying: Utilize operators like `$eq`, `$ne`, `$in`, etc., to perform queries on data.


 Lesson 16: MongoDB Data Types

 

 

 Objective:

Explore data types supported by MongoDB.


 BSON data types

- BSON: Binary JSON, the format in which MongoDB stores data, includes various data types like string, integer, double, object, array, binary data, etc.

- Examples:

  - String: `"Hello"`

  - Integer: `42`

  - Object: `{ key: "value" }`


 Handling different types of data in MongoDB

- Data modeling: Considerations for embedding documents, referencing documents, and designing schemas based on application needs.

- Geospatial data: MongoDB supports geospatial queries, allowing the storage and querying of geospatial data.


 Data modeling considerations

- Normalization vs. denormalization: Deciding between normalized and denormalized data structures based on read vs. write performance requirements.

- Document structure: Designing documents to optimize query performance and data retrieval.


Understanding MongoDB's structure, its querying capabilities, and data modeling techniques empowers developers to create efficient, scalable, and well-structured databases for their MERN stack applications.


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