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Advanced MongoDB

Master MongoDB at an advanced level by diving deep into features like sharding, replication, data partitioning, performance tuning, and enterprise-level security. This course is designed for learners who want to work with large-scale applications and solve complex data management challenges using MongoDB.

5.0 Rating Advanced Course 12 Weeks

About this Course

This advanced-level MongoDB course is designed for those who have a strong understanding of basic and intermediate MongoDB concepts. The course focuses on optimizing MongoDB for performance, scaling databases, sharding, and implementing robust security practices for enterprise environments. You'll work on complex queries, improve data relationships, and handle large-scale databases effectively.

Course Objective

In this course, you will gain advanced skills to scale and secure MongoDB environments. You'll explore sharding, replication, performance optimization, and security measures required for large-scale enterprise applications. By the end of this course, you'll be able to implement high-availability databases and work with complex data models in MongoDB.

Skills you'll gain

Sharding and Replication Scaling and Partitioning MongoDB Advanced Security and Authentication Performance Optimization for Large Databases

Syllabus

  • Introduction to NoSQL and MongoDB
  • Installing MongoDB and Setting Up Environment
  • Basic MongoDB Commands (start, stop, status)
  • Overview of Collections and Documents
  • Introduction to BSON and Data Types
Practice Task: Set up MongoDB and create a sample collection with basic documents.

  • Inserting Documents into Collections
  • Querying Documents with Find
  • Updating Documents in MongoDB
  • Deleting Documents from a Collection
  • Understanding MongoDB Cursors
Practice Task: Perform CRUD operations on a sample collection.

  • Introduction to Data Modeling Concepts in MongoDB
  • Embedding vs. Referencing Data
  • Using Indexes to Improve Query Performance
  • Understanding MongoDB Index Types
  • Creating and Dropping Indexes
Practice Task: Model a collection with embedded documents and apply indexing.

  • Setting Up Project Environment
  • Designing Database Schema for Application
  • Implementing CRUD Operations in the Application
  • Using Indexes for Optimized Queries
  • Testing and Debugging MongoDB Application
Project: Build a basic application (e.g., a to-do list, a blog, or a product catalog) using MongoDB to store and retrieve data.

  • Advanced Query Operators and Expressions
  • Aggregation Framework: Pipelines and Stages
  • Working with Aggregations for Complex Data Analysis
  • Using `$match`, `$group`, `$sort`, and `$limit` stages
Practice Task: Create queries that analyze and summarize data using aggregation techniques.

  • Understanding and Creating Indexes
  • Index Types: Single, Compound, and Text Indexes
  • Optimizing Query Performance with Indexing
  • Using the `explain` Command to Analyze Query Plans
Practice Task: Implement various indexes to improve performance on complex queries.

  • Data Relationships in MongoDB: One-to-One, One-to-Many, Many-to-Many
  • Choosing Between Embedded Documents and References
  • Working with `$lookup` for Data Joins
  • Best Practices for Structuring Data Relationships
Practice Task: Design data models with both embedded and referenced relationships.

  • Develop a data model for a sample application
  • Apply advanced queries and aggregations to analyze data
  • Implement indexing for performance optimization
  • Optimize the data structure with embedded documents and references
Project Goal: Design and implement a MongoDB database optimized for complex queries, data relationships, and performance.

  • Understand complex data structures in MongoDB
  • Embed vs reference data models
  • Design one-to-many and many-to-many relationships
  • Model hierarchical data effectively
  • Sharding and partitioning for large datasets
Project Goal: Implement complex data models and optimize MongoDB for large datasets and real-world applications.

  • Learn advanced indexing techniques (compound, geospatial, etc.)
  • Optimize query performance using `explain` function
  • Improve aggregation pipeline performance
  • Implement in-memory storage and optimize I/O operations
Project Goal: Optimize MongoDB queries, indexing, and database performance for large-scale applications.

  • Implement role-based access control (RBAC)
  • Configure authentication methods (LDAP, x.509, etc.)
  • Encrypt data at rest and in transit
  • Automate backup strategies and disaster recovery
Project Goal: Ensure the security of MongoDB databases and implement robust backup and recovery strategies.

  • Build an enterprise-level application using MongoDB
  • Implement advanced MongoDB features like sharding, replication, and indexing
  • Optimize the data model for performance and scalability
  • Complete a project based on real-world use cases, ensuring high availability and security
Project Goal: Apply advanced MongoDB concepts to build a fully optimized, secure, and scalable enterprise application.

About the Instructor

Shalini Baskaran

Founder & CEO

4.98 Instructor rating

40 Students

Shalini Baskaran, is the CEO and Curriculum Director at Savvy Axiss. With a deep background in computer science, she has designed numerous courses to build strong foundations for aspiring programmers. Known for her clear, practical teaching style, she excels at making complex topics accessible. She is passionate about empowering students with real-world skills, and she looks forward to guiding you through this foundational journey in programming.

Fee ₹ 3999

₹ 2999

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Duration

12 Weeks

Class Time

1.5 Hours

Enrolled

250 students

Language

English / Tamil

Skill Level

Advanced

Schedule

Monday to Friday

Certificate

Yes

SoftSkill Trainning

Free

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