Mastering MongoDB: From NoSQL Foundations to Advanced Aggregation and Vector Search
Master MongoDB from scratch. Learn document-based NoSQL database design, CRUD operations, indexing, and aggregation pipelines. You will also explore modern vector database concepts, including vector embeddings and similarity search, to prepare you for building AI-powered applications.
Course Overview
Welcome to this comprehensive course designed to take you from a complete beginner to a confident database developer. You will master MongoDB, the leading NoSQL database, and gain essential knowledge of modern Vector Databases used in generative AI and LLM applications.
What You'll Learn
- NoSQL Fundamentals: Understand how document databases differ from traditional relational databases.
- Core CRUD Operations: Master inserting, querying, updating, and deleting documents.
- Data Modeling & Indexing: Learn to design schemas, work with embedded documents, and optimize performance.
- Aggregation Pipelines: Build complex data processing pipelines for analytics.
- Vector Databases: Understand how unstructured data is converted into a vector embedding and queried in a high-dimensional space using distance metrics for similarity search.
Prerequisites
- Basic understanding of computer programming concepts.
- Familiarity with general database concepts is helpful but not required.
- No prior machine learning experience is needed to understand the vector database concepts.
Target Audience
This course is built for beginner to intermediate developers, backend engineers, data scientists, and AI enthusiasts who want to build scalable applications using modern database technologies.
Tools & Technologies
- MongoDB (Compass & Shell)
- Vector Databases
- LLMs (for generating embeddings)
Course Content
Introduction to NoSQL and MongoDB Fundamentals
Introduction to NoSQL Databases and MongoDB
Understanding JSON, BSON, and Document-Based Storage
Setting Up MongoDB and Connecting via MongoDB Compass
Core CRUD Operations and Querying
Inserting Documents with insertOne and insertMany
Querying Documents and Using Comparison Operators
Updating Documents with Field and Array Operators
Deleting Documents and Collections Safely
Data Modeling, Relationships, and Indexing
Document Modeling: Embedded vs. Referenced Patterns
Designing One-to-Many and Many-to-Many Relationships
Optimizing Query Performance with Indexing
Mastering the Aggregation Pipeline
Filtering and Reshaping Data with $match and $project
Grouping and Accumulating Data with $group
Advanced Aggregation: $unwind and $lookup
Vector Databases and Semantic Search for AI
Understanding Vector Embeddings and Semantic Search Foundations
Implementing Vector Search with MongoDB Atlas
This course includes
- 8h 3m on-demand video
- 15 lessons across 5 chapters
- 5 interactive quizzes
- Access on mobile and desktop
- Source playlist on YouTube
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