Best of MongoDB — September 2024

1. 1
   Video

   YouTube·2y

   MERN Stack Full Course - ECommerce App with Admin Panel : React, Redux Toolkit, Tailwind, MongoDB

   This post provides a comprehensive guide for building an industry-level e-commerce application using the MERN stack, including React, Redux Toolkit, Node.js, Express, and MongoDB. The app features a separate admin panel for managing products and orders, supports PayPal payment integration, and includes product listing and checkout functionalities. The post also covers setting up the project structure, configuring the server and database, and integrating frontend and backend technologies.

   111

   1
2. 2
   Article

   freeCodeCamp·2y

   How to Write Cleaner Code Using Mongoose Schemas

   Learn to write cleaner and more maintainable code in your NodeJS applications using Mongoose schemas. Key concepts include discriminators for creating multiple models with shared schemas, statics for reusable model-level methods, methods for custom instance-level functions, and hooks for adding custom behavior during different document lifecycle stages. These features help organize your data and streamline your database operations.

   93

   2
3. 3
   Video

   freeCodeCamp·2y

   FARM Stack Course – Full Stack Development with FastAPI, React MongoDB

   The FARM stack is a modern web development stack that combines FastAPI for the backend, React for the front end, and MongoDB for the database. This course covers each component, their integration using Docker, and steps to build a full-featured to-do application. FastAPI provides a high-performance Python framework for APIs, React enables efficient UI creation, and MongoDB offers flexible, document-oriented data storage. The tutorial emphasizes setting up the development environment, creating API endpoints, building a responsive UI, and managing databases, culminating in a Dockerized application deployment.

   85
4. 4
   Article

   Community Picks·2y

   Building an Advanced RAG System With Self-Querying Retrieval

   Learn how to build an advanced Retrieval Augmented Generation (RAG) system that leverages self-querying retrieval to improve search relevance. This tutorial covers extracting metadata filters from natural language queries, combining metadata filtering with vector search, and generating structured outputs using LLMs. The guide focuses on developing an investment assistant to answer financial questions using MongoDB as the vector store and LangGraph for orchestration.

   37
5. 5
   Article

   The New Stack·2y

   Introduction to Payload, a Headless CMS and App Framework

   Payload is a headless CMS and application framework designed to separate content storage from presentation, allowing developers to use any frontend they prefer. It supports granular access control and collections management through code. Currently, Payload is undergoing significant changes towards version 3, making its documentation temporarily inconsistent. The CMS supports both REST and reusable component architecture, providing developers with flexibility in managing content and roles.

   24
6. 6
   Article

   Community Picks·2y

   🗄️🚀 SQL vs NoSQL: Viability and Comparison

   SQL databases like PostgreSQL offer rigid, table-based schemas and follow ACID principles, making them ideal for structured data and applications requiring consistent transactions. NoSQL databases like MongoDB and ElasticSearch provide flexible schemas, are horizontally scalable, and are suited for unstructured data, web apps, IoT, and real-time text searching. PostgreSQL is trusted in corporate sectors for reliability, MongoDB is favored by startups for scalability, and ElasticSearch is essential for real-time observability and log analysis.

   23
7. 7
   Article

   Baeldung·2y

   Building a RAG App Using MongoDB and Spring AI

   Learn how to build a Retrieval-Augmented Generation (RAG) Wiki application using MongoDB and Spring AI. The tutorial details setting up MongoDB Atlas Vector Search for storing documents, adding necessary dependencies, and configuring the application to save and retrieve documents based on context. The application leverages a vector store for similarity search and utilizes LLM for generating responses, making it suitable for developing chatbots, automated wikis, and search engines.

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