MongoDB: Syllabus

Mastering MongoDB: NoSQL Database Design and Optimization with Python

Introduction#

MongoDB is a flexible, high-performance NoSQL database widely used for modern application development. This book takes a hands-on approach to MongoDB, focusing on practical implementation, performance tuning, and real-world integrations with Python. Covering 90% practical implementation, this book ensures readers gain expertise in MongoDB for building scalable, production-ready applications.

Module 1: Introduction to NoSQL and MongoDB#

• Understanding relational vs. NoSQL databases
• Key features and advantages of MongoDB
• Setting up MongoDB locally and in the cloud
• Using MongoDB Atlas for managed database services

Module 2: MongoDB Data Modeling and Schema Design#

• Understanding document-oriented data storage
• Designing efficient MongoDB schemas
• Embedding vs. referencing documents
• Best practices for handling relationships in NoSQL

Module 3: CRUD Operations in MongoDB with Python#

• Connecting to MongoDB using PyMongo
• Performing Create, Read, Update, Delete (CRUD) operations
• Using MongoDB transactions for atomic operations
• Handling data consistency and validation

Module 4: Indexing and Query Optimization#

• Understanding different types of indexes in MongoDB
• Creating and managing compound and text indexes
• Query optimization strategies for high-performance reads
• Analyzing query execution plans with .explain()

Module 5: MongoDB Aggregation Framework#

• Introduction to the aggregation pipeline
• Using $match, $group, $sort, and $project
• Implementing complex data transformations
• Optimizing aggregation queries for performance

Module 6: Transactions and Data Integrity#

• ACID compliance in MongoDB transactions
• Implementing multi-document transactions
• Handling concurrency and rollback scenarios
• Best practices for data consistency in NoSQL

Module 7: Replication and High Availability#

• Understanding MongoDB replica sets
• Configuring automatic failover and redundancy
• Read preferences and write concerns in replication
• Monitoring replication lag and performance tuning

Module 8: Sharding for Horizontal Scaling#

• Understanding when and why to shard data
• Setting up a sharded cluster in MongoDB
• Choosing shard keys for optimal performance
• Managing sharded clusters and balancing data

Module 9: MongoDB Security Best Practices#

• Implementing authentication and authorization (RBAC)
• Securing data with TLS/SSL encryption
• Preventing NoSQL injection attacks
• Auditing and logging database activities

Module 10: MongoDB and Python Integration#

• Working with MongoDB and Flask for web applications
• Using MongoEngine for Object-Document Mapping (ODM)
• Connecting MongoDB with FastAPI for modern APIs
• Performing batch operations with Python scripts

Module 11: Caching and Performance Tuning#

• Using Redis as a caching layer for MongoDB
• Profiling slow queries with MongoDB Performance Advisor
• Implementing write concerns for efficient data storage
• Load balancing MongoDB for high-performance applications

Module 12: Real-Time Analytics and Data Processing#

• Streaming data into MongoDB with Apache Kafka
• Performing real-time analytics with MongoDB Change Streams
• Querying time-series data efficiently
• Using MongoDB for event-driven applications

Module 13: Deploying MongoDB in Production#

• Best practices for deploying MongoDB on AWS, Azure, and GCP
• Running MongoDB in Docker and Kubernetes environments
• Automating backups and disaster recovery planning
• Implementing CI/CD pipelines for database deployments

Hands-On Projects#

Project 1: Building a NoSQL-Powered Blog Platform#

• Implement a blog engine with user authentication
• Store and query blog posts using MongoDB
• Optimize indexing for fast content retrieval

Project 2: E-Commerce Order Management System#

• Design a schema for product catalogs and orders
• Implement transactions for order processing
• Use MongoDB aggregation for revenue reports

Project 3: Real-Time Chat Application with MongoDB and Python#

• Store and retrieve chat messages in real time
• Implement MongoDB Change Streams for live updates
• Optimize performance with indexing and caching

Project 4: AI-Powered Recommendation System#

• Store user interaction data in MongoDB
• Use aggregation and Python ML models for recommendations
• Optimize data storage for fast query execution

Project 5: Deploying a High-Availability MongoDB Cluster#

• Set up a replica set with automated failover
• Implement sharding for horizontal scalability
• Secure the database with RBAC and encryption

References#

• MongoDB Official Documentation
• PyMongo Documentation
• MongoDB Aggregation Framework
• MongoDB Security Best Practices