Migrating EMR Data from MySQL to MongoDB Business Case Electronic Medical Record (EMR) systems are traditionally built using relational databases. While relational models are powerful for enforcing data integrity and consistency, they may not be optimal for real-time analytics, flexible schema evolution, or scaling to massive read-intensive operations common in modern healthcare applications. Task is to redesign the existing EMR system, currently implemented in MySQL, for a NoSQL environment using MongoDB. This shift is intended to support a next-generation patient portal that enables rapid data access, flexible record updates, and integration with external apps (e.g., wearables, mobile health tools). Learning Objectives Differentiate Database Models: Understand the structural differences between relational databases (MySQL) and document-oriented databases (MongoDB), including data organization, relationships, and flexibility. Practice NoSQL Schema Design: Apply schema design principles in MongoDB using real-world healthcare data, focusing on effective modeling of patient, visit, provider, and diagnosis entities. Evaluate Embedding vs. Referencing: Analyze and decide when to use embedded documents versus references in MongoDB collections, based on data access patterns, relationship cardinality, and performance considerations. Develop a Python-Based ETL Pipeline: Create an Extract, Transform, Load (ETL) script using Python with PyMySQL and pymongo to migrate normalized