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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

What is MongoDB?

MongoDB is a NoSQL database that is designed to store, manage, and retrieve large amounts of unstructured or semi-structured data. Unlike traditional relational databases (RDBMS), which use tables and rows, MongoDB stores data in a more flexible format called documents. These documents are JSON-like and can contain nested data structures, arrays, and other complex data types. MongoDB is highly scalable, fast, and can handle large amounts of data in modern applications, especially those that need to handle dynamic or complex data models.

MongoDB is an open-source, document-oriented database that falls under the broader category of NoSQL databases. It is widely used in applications where flexibility and scalability are important, such as real-time analytics, social media platforms, mobile apps, and content management systems.

Key Characteristics of MongoDB

Document-Oriented Storage:

  • Data in MongoDB is stored in documents rather than tables. A document is a BSON (Binary JSON) object, which is a binary-encoded version of JSON. It can contain complex, nested data structures (arrays, sub-documents) that make MongoDB particularly suited for handling unstructured or semi-structured data.

Schema-less (Flexible Schema):

  • MongoDB does not require a predefined schema. Each document in a collection can have different fields and data types, which allows for easy adaptation to changing requirements over time. This contrasts with traditional relational databases, where the schema must be strictly defined before data can be stored.

Scalability:

  • MongoDB is designed for horizontal scaling, meaning it can handle large-scale applications by distributing data across multiple machines or servers. This is accomplished through a technique called sharding, which breaks up large datasets into smaller chunks that can be distributed across various servers.

High Availability:

  • MongoDB supports replication, which allows data to be copied across multiple servers in a replica set. If one server goes down, another can take over, ensuring high availability and fault tolerance.

Rich Querying:

  • MongoDB supports a wide variety of query types, including CRUD operations (Create, Read, Update, Delete), text search, range queries, aggregation queries, and geospatial queries. This makes it highly flexible for different kinds of applications.

Aggregation Framework:

  • MongoDB has a powerful aggregation framework that allows for complex data processing, transformations, and analytics. You can group, filter, and sort data in a variety of ways, similar to SQL’s GROUP BY, HAVING, and JOIN operations, but with more flexibility and efficiency.

Indexes:

  • MongoDB supports the creation of indexes on any field or combination of fields to improve the performance of queries. Indexing helps speed up search operations by allowing the database to quickly locate documents based on indexed fields.

Basic MongoDB Concepts

Database:

  • A container for collections. Each MongoDB instance can have multiple databases.

Collection:

  • A group of MongoDB documents. It is similar to a table in a relational database, but collections are not constrained by any fixed schema. A collection can store documents of different shapes and sizes.

Document:

  • A record in MongoDB, similar to a row in a relational database, but represented as a BSON (Binary JSON) object. Documents can contain various data types such as strings, numbers, arrays, and nested documents.

BSON:

  • Binary JSON is the format in which data is stored in MongoDB. BSON is an extended version of JSON, designed to be both human-readable and efficient for storage and retrieval. It supports additional data types like Date, ObjectId, and Binary Data.

Replica Set:

  • A group of MongoDB servers that maintain the same data. Replica sets provide data redundancy, high availability, and automatic failover in case of server failure.

Sharding:

  • The process of distributing data across multiple servers, or shards, to handle large datasets and high throughput. Each shard contains a subset of the data, and together, they provide a horizontally scalable architecture.

Conclusion

MongoDB is a powerful and flexible NoSQL database that is perfect for modern applications that deal with large amounts of unstructured or semi-structured data. Its document-oriented model, schema flexibility, and scalability make it highly adaptable for use in a wide variety of use cases, from web and mobile apps to real

Why MongoDB?
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