Mongodb: Fundamentals To Advanced With Pymongo

A hands-on guide to mastering MongoDB and building Vector Databases

What you'll learn

Beginner Commands in MongoDB: Learn basic CRUD operations using MongoDB Shell and Compass.

Setting Up MongoDB Atlas: Configure and manage a cloud-hosted MongoDB database on MongoDB Atlas

Connecting Python with MongoDB Atlas: Use PyMongo to connect your Python applications to MongoDB Atlas

Advanced CRUD Operations: Perform complex operations like updating multiple documents, using filters, and conditional queries.

Indexing and Aggregation: Learn how to create indexes and build efficient aggregation pipelines to handle large datasets.

Introduction to Vector Databases: Understand vector embeddings and their role in AI applications like similarity search.

Requirements

Basic knowledge of Python (even beginner-level proficiency is sufficient).

A laptop or desktop with at least 8GB RAM and a stable internet connection.

Familiarity with basic computer operations and interest in databases.

Description

Embark on a journey to master modern databases with our comprehensive course on MongoDB and Vector Databases. Whether you’re a beginner or an enthusiast eager to explore advanced database techniques, this course is structured to guide you step by step.We start with the basics of MongoDB by introducing beginner-friendly commands using the MongoDB Shell and Compass. You’ll set up a local MongoDB database on your desktop, learn how to create and manage databases, and perform fundamental operations like CRUD (Create, Read, Update, Delete). We’ll also explore indexing and aggregation pipelines to build a solid foundation.Once you’re comfortable with the basics, we move to the cloud by configuring and working with MongoDB Atlas, a powerful cloud-hosted database solution. You’ll learn to create and secure a cloud database and connect it to your applications. Using PyMongo, the Python driver for MongoDB, you’ll automate database tasks and implement advanced features like indexing, aggregation, and schema design—all while working seamlessly with your cloud database.Finally, we take a deep dive into Vector Databases, where you’ll learn how to harness vector embeddings for AI-powered applications such as similarity search, recommendation systems, and semantic search. You’ll integrate vector search with MongoDB, combining traditional database management with cutting-edge AI technologies.Through hands-on projects and real-world examples, you’ll gain the skills to deploy scalable and intelligent database solutions, ensuring you’re ready to tackle challenges in modern data-driven industries.

Overview

Section 1: Introduction

Lecture 1 What is MongoDB ? Difference between SQL and NoSQL

Section 2: Crash Course on MongoDB and NoSQL

Lecture 2 Resources

Lecture 3 Create Database with Mongosh and Compass

Lecture 4 Insert Documents in Collection with Mongosh and Compass

Lecture 5 Different data types in document values

Lecture 6 Limit, Sort Operations

Lecture 7 Find | Comparison operators

Lecture 8 Find | Logical Operators

Lecture 9 Find | Element Query Operators (Exists, Type)

Lecture 10 Replace Document

Lecture 11 Update Document

Lecture 12 Update Document Part 2

Lecture 13 Update Document Part 3

Lecture 14 Create Index

Section 3: MongoDB Atlas Setting up

Lecture 15 Setup MongoDB DB Atlas

Section 4: PyMongo

Lecture 16 Jupyter Notebook files and Codes download here

Lecture 17 Pre-requisite to install Python, Pymongo and Dependencies

Lecture 18 Connect to MongoDB (Local version) using PyMongo

Lecture 19 Connect to MongoDB Atlas using PyMongo

Lecture 20 Use YAML file to connect with MongoDB Atlas using PyMongo

Lecture 21 PyMongo - Create Database, Collection, Document in MongoDB Atlas

Lecture 22 PyMongo - Print All Documents

Lecture 23 PyMongo - Insert Many Documents into a Collection

Lecture 24 PyMongo - Limit, Sort and Skip methods

Lecture 25 PyMongo - Querying and Projection (find) with comparison operators

Lecture 26 PyMongo - Querying with comparison operators part 2

Lecture 27 PyMongo - Querying, more comparison operators part 3

Lecture 28 PyMongo - Logical Operator [$and]

Lecture 29 PyMongo - Logical Operators [$or, $not, $nor]

Lecture 30 PyMongo Warning - Dont skip this.

Lecture 31 PyMongo - Element Query Operators ($exists and $type)

Lecture 32 PyMongo - Array Query Operator [$all]

Lecture 33 PyMongo - Aarray Query Operators [$elemMatch, $size]

Lecture 34 PyMongo- Update Document method ($set)

Lecture 35 PyMongo - Update Document method (UpdateMany, $unset)

Lecture 36 How to insert csv file records as documents in Collection for MongoDB

Section 5: PyMongo - Aggregate Pipeline

Lecture 37 What is Aggregate Pipeline

Lecture 38 Aggregate stage - $match

Lecture 39 More examples on $match

Lecture 40 Aggregate stage - $group

Lecture 41 More examples on $group

Section 6: Knowledge Check

Section 7: Bonus

Lecture 42 Bonus Lecture

Beginners who want to start their journey in database management and modern data technologies.,Python developers looking to enhance their skills by integrating databases into applications.,AI and Data Enthusiasts interested in vector search and building AI-driven solutions.,Students and professionals aiming to work with scalable cloud databases like MongoDB Atlas.,Small business owners and hobbyists seeking to manage their data efficiently.