Day 76 of Learning MERN Stack

Hello Dev Community! 👋

It is officially Day 76 of my 100-day full-stack engineering streak! For the past several weeks, I have been heavily immersed in NoSQL databases, using MongoDB documents to back my full-stack clones. Today, I decided to broaden my database engineering skill set by taking a deep dive into Relational Databases (SQL)! 📊⚡

Stepping out of flexible JSON-like structures and adjusting to rigid, highly optimized tables is an essential step for any well-rounded backend developer.

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🧠 What I Learned Today: SQL vs. NoSQL

Before writing code, I mapped out the core architectural differences between the two paradigms:

Feature	NoSQL (e.g., MongoDB)	SQL (e.g., MySQL / PostgreSQL)
Data Model	Flexible, schema-less collections & documents.	Strict table-based structures with rows & columns.
Relationships	Typically nested embedded sub-documents or references.	Explicit Relational Mapping via Primary & Foreign Keys.
Scaling	Horizontally scalable (distributed sharding across nodes).	Vertically scalable (requires increasing horsepower on one machine).
Transactions	Great for high-write, unstructured or dynamic data shapes.	Strict ACID compliance, making it excellent for financial or tabular data.

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🛠️ Analyzing My First Query Block on Day 76

As showcased in "Screenshot (174).png", I configured an entire relational lifecycle inside an independent database script:

1. Database Provisioning & Focus Selection

I initialized the data cluster safely using standard syntax constraints to ensure execution safety and loaded the working context into the active engine:

sql
CREATE DATABASE IF NOT EXISTS XYZ_Company;
USE XYZ_Company;CREATE TABLE employee_info (
    id INT PRIMARY KEY,
    name VARCHAR(30),
    SALARY INT
);