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  • MongoDB – (No-SQL)

MongoDB – (No-SQL)

Curriculum

  • 10 Sections
  • 31 Lessons
  • 10 Weeks
Expand all sectionsCollapse all sections
  • Introduction to MongoDB
    MongoDB is a NoSQL database that is designed for handling large volumes of unstructured or semi-structured data. Unlike traditional relational databases (RDBMS) that use tables and rows to organize data, MongoDB stores data in a flexible document-oriented format using JSON-like documents (BSON - Binary JSON). This makes it highly scalable, flexible, and performant for applications that need to handle varying types of data with complex structures.
    5
    • 1.1
      What is MongoDB?
    • 1.2
      Why MongoDB?
    • 1.3
      When to use MongoDB?
    • 1.4
      Key Features of MongoDB
    • 1.5
      Installing MongoDB
  • MongoDB Basic Operations
    MongoDB provides a rich set of basic operations for interacting with the database, including creating, reading, updating, and deleting data (often abbreviated as CRUD operations). Below are the basic operations that you can perform with MongoDB.
    2
    • 2.0
      Database and Collection Basics
    • 2.1
      CRUD Operations
  • Advanced Querying Techniques
    MongoDB offers a rich set of querying capabilities, and as you work with larger datasets and more complex application requirements, you’ll often need to use advanced querying techniques. These techniques help you optimize performance, execute sophisticated queries, and leverage MongoDB’s powerful indexing and aggregation features.
    4
    • 3.1
      Query Filters and Operators
    • 3.2
      Advanced Querying
    • 3.3
      Sorting and Limiting Results
    • 3.4
      Aggregation Framework
  • Data Modeling and Schema Design
    Data modeling and schema design are critical when using MongoDB (or any NoSQL database) to ensure efficient data storage, fast queries, and scalability. Unlike relational databases, MongoDB is schema-less, which means you are not required to define a fixed schema upfront. However, making the right design decisions from the beginning is essential for maintaining performance and avoid complications as your data grows.
    4
    • 4.1
      Data Modeling
    • 4.2
      Document Structure
    • 4.3
      Schema Design Patterns
    • 4.4
      MongoDB and Relationships
  • Indexing and Performance Optimization
    In MongoDB, indexing is a critical part of performance optimization. Without proper indexes, MongoDB has to scan every document in a collection to satisfy queries, which can be very inefficient for large datasets. Indexes are used to quickly locate data without scanning every document, making reads faster and more efficient.
    3
    • 5.0
      Creating Indexes
    • 5.1
      Using Text Search
    • 5.2
      Performance Optimization
  • Integrating MongoDB with a Web Application (Node.js)
    Integrating MongoDB with a web application built using Node.js is a common and powerful combination for building scalable and efficient web apps. MongoDB’s flexibility with JSON-like data and Node.js's asynchronous event-driven architecture work well together. In this guide, I'll walk you through the steps for integrating MongoDB with a Node.js web application, covering the essentials of setting up the connection, performing CRUD operations, and using popular libraries.
    3
    • 6.0
      Setting Up MongoDB with Node.js
    • 6.1
      CRUD Operations with Mongoose
    • 6.2
      Error Handling and Validation
  • Security in MongoDB
    Security is an essential aspect when working with MongoDB, especially when handling sensitive data in production environments. MongoDB provides a variety of security features to help protect your data against unauthorized access, injection attacks, and other vulnerabilities. Here’s a guide on securing MongoDB and your Node.js application when interacting with MongoDB.
    2
    • 7.0
      Authentication and Authorization
    • 7.1
      Data Encryption
  • Working with MongoDB in Production
    3
    • 8.0
      MongoDB Backup and Restore
    • 8.1
      MongoDB Scaling and Sharding
    • 8.2
      MongoDB Replication
  • Deploying and Monitoring MongoDB
    Working with MongoDB in a production environment requires careful planning, attention to detail, and best practices to ensure optimal performance, security, reliability, and scalability.
    3
    • 9.0
      Deploying MongoDB to Production
    • 9.1
      Monitoring and Management
    • 9.2
      Summary for MongoDB deployment on Production
  • Building a Web App with MongoDB (Final Project)
    Demo Project (OneStopShop)
    2
    • 10.0
      Building the Application
    • 10.1
      Final Project Features

When to use MongoDB?

MongoDB is best suited for use cases where:

  1. Flexible or Evolving Schema:
    • If the structure of your data may change over time (e.g., adding new fields, removing others), MongoDB allows you to store this data without having to modify a schema or perform data migrations.
  2. High Volume of Data:
    • If your application needs to handle large amounts of data or scale as the application grows, MongoDB’s horizontal scaling capabilities (through sharding and replication) ensure that your data can be distributed across multiple servers and regions.
  3. Unstructured or Semi-Structured Data:
    • MongoDB is well-suited for storing unstructured or semi-structured data such as logs, social media posts, user profiles, product catalogs, or multimedia content.
  4. Real-Time Analytics:
    • MongoDB is optimized for real-time data processing, making it ideal for use cases like real-time analytics, business intelligence, and systems that require up-to-the-minute data insights.
  5. Content Management and Cataloging:
    • For storing content like articles, videos, images, or catalogs of products, MongoDB’s flexible document structure allows for varying metadata and nested content types.

Common Use Cases for MongoDB

  • Web Applications: MongoDB is commonly used in modern web development frameworks such as the MERN stack (MongoDB, Express, React, Node.js). It is ideal for storing user data, session information, and application content.
  • Real-Time Analytics and Reporting: MongoDB can store and process high volumes of data, making it an excellent choice for real-time dashboards, log analysis, and monitoring systems.
  • Mobile Applications: MongoDB is well-suited for mobile apps that need to store and sync data on the cloud, offering offline support and scalability for global mobile applications.
  • Content Management Systems (CMS): Its schema-less nature is well-suited for managing diverse types of content, including text, multimedia, and metadata.
  • Internet of Things (IoT): MongoDB’s ability to handle large amounts of rapidly changing, unstructured data makes it a popular choice for IoT applications, where sensor data needs to be captured and processed in real time.
  • Gaming: MongoDB is used in gaming applications to store user profiles, game progress, leaderboards, and other dynamic data that is constantly changing.

Basic MongoDB Operations

  1. Insert Data:db.users.insertOne({ name: "John Doe", age: 29, email: "john@example.com" });
  2. Find Data:db.users.find({ age: { $gte: 18 } });
  3. Update Data:db.users.updateOne({ name: "John Doe" }, { $set: { age: 30 } });
  4. Delete Data:db.users.deleteOne({ name: "John Doe" });
  5. Create an Index:db.users.createIndex({ email: 1 });
  6. Aggregation Query:db.orders.aggregate([
     { $match: { status: "Shipped" } },
     { $group: { _id: "$customer_id", totalAmount: { $sum: "$amount" } } },
     { $sort: { totalAmount: -1 }
     } ]);
Why MongoDB?
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Key Features of MongoDB
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