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Coding | Notes | Jobs on Instagram: "Choosing between SQL and NoSQL can shape how scalable, fast, and reliable your application becomes. Both databases solve different problems — knowing when to use which is a core backend skill. Here’s a simple breakdown to understand the difference: 🔹 SQL (Relational Databases) Relational model with tables (rows & columns) Predefined and rigid schema Vertically scalable (scale up) Best for structured data Supports complex queries & joins ACID compliant (stron
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Ajay Dangi on Instagram: "SQL gives structure. NoSQL gives scale. Backend needs both. . . SQL (Relational Databases) Data Model: Tables (Rows & Columns) Schema: Fixed / Predefined schema Query Language: SQL (SELECT, JOIN, GROUP BY) Transactions: Strong ACID compliance Scaling: Vertical scaling (scale-up) Best For: ✔ Banking systems ✔ Payment & finance apps ✔ ERP / CRM systems Examples: MySQL, PostgreSQL, Oracle, SQL Server NoSQL (Non-Relational Databases) Data Model: Document, Key-Value, Column,
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TechWithCP on Instagram: "Choosing between SQL and NoSQL is NOT about hype… It’s about HOW your data behaves and HOW your system scales. 👇 🛠 When to choose SQL (Relational DBs like PostgreSQL, MySQL): ✔ Data has clear relationships ✔ You need ACID transactions (banking, finance, orders) ✔ Complex queries with JOINS ✔ Strict schema & consistency 🔐 Best for: Banking, E-commerce orders, Inventory systems 🗃 When to choose NoSQL (MongoDB, DynamoDB, Cassandra): ✔ Data changes frequently or structu
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Instagramtechwithcp
Choosing between SQL and NoSQL? Learn when to use relational databases vs document/key-value stores based on consistency, schema changes, joins, scaling, and query needs. #SQL #NoSQL #Database #Backend #SystemDesign #DevOps #DataEngineering #SoftwareEngineering
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Bhavesh vaswani on Instagram: "Let’s break this classic system design debate step-by-step 👇 In big systems, it’s rarely SQL vs NoSQL — it’s SQL and NoSQL, working together. 1️⃣ SQL Databases (Relational) SQL stores structured data in tables with defined relationships. They ensure ACID properties — accuracy, consistency, and integrity in every transaction. When to Use: ✅ Data has relationships (users–orders–payments). ✅ When consistency are more important than speed. Examples: Banking apps → tra
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