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MongoDB

MongoDB

Duration
45 Hours

Course Description

        MongoDB is a popular, open-source NoSQL database that stores data in flexible, JSON-like documents within collections. It's known for its scalability and ability to handle large, distributed datasets, making it suitable for modern applications requiring high performance and rapid development. Unlike relational databases, MongoDB uses a dynamic schema, allowing for flexible data models and easier adaptation to changing application needs. 

Course Outline For MongoDB

Core modules

1. Introduction to NoSQL and MongoDB

  • Understanding different types of databases and the need for NoSQL databases.
  • Exploring the benefits of NoSQL databases compared to traditional relational databases.
  • An overview of MongoDB's architecture, design goals, and its role in modern applications.
  • Understanding MongoDB's core components like collections, documents, and key/value pairs.
  • Introduction to JSON and BSON data formats and their importance in MongoDB.
  • Setting up the MongoDB environment, including installation and configuring essential tools like the Mongo Shell and MongoDB Compass. 

2. CRUD Operations

  • Mastering the fundamental Create, Read, Update, and Delete (CRUD) operations in MongoDB using the Mongo Query Language.
  • Learning how to insert documents, query data using the find() and findOne() methods, and modify or delete documents.
  • Understanding concepts like Querying with Comparison and Logical Operators, Array Operators, and Array Projections. 

3. Data modeling

  • Understanding data modeling concepts and different approaches in MongoDB, such as embedding and referencing documents.
  • Modeling relationships between documents and creating efficient data models for specific application contexts.
  • Exploring techniques for designing schemas that cater to dynamic data demands. 

4. Indexing and aggregation

  • Understanding the importance of indexing for performance optimization and efficient query execution.
  • Learning about different types of indexes, such as single-field, compound, and multikey indexes.
  • Mastering the Aggregation Framework to process, transform, and analyze data for business intelligence and reporting needs.
  • Working with aggregation pipeline stages like $match, $group, $sort, and $project. 

5. Advanced topics

  • Replication and High Availability: Setting up and managing replica sets to ensure data redundancy, high availability, and automatic failover.
  • Sharding and Scalability: Understanding the concepts of sharding and horizontal scaling to handle large volumes of data and traffic.
  • Security: Implementing security best practices, including authentication, authorization, and role-based access control, to protect data.
  • Performance Tuning and Optimization: Utilizing tools like the Mongo Profiler and explain() plans to diagnose and resolve performance bottlenecks.
  • Backup and Recovery: Implementing comprehensive backup strategies and recovering data using tools like mongodump and mongorestore.
  • MongoDB in the Cloud: Working with MongoDB Atlas, a fully managed cloud database service, and integrating with other cloud services.
  • Integration with Programming Languages and Frameworks: Connecting and integrating MongoDB with popular programming languages like Node.js, Java, Python, and frameworks like Spring Data or Mongoose. 
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