Schema-on-Write vs Schema-on-Read vs Schemaless: Explained with Data Types

Ever wondered how data is organized, and why some databases need strict rules while others let you store anything? 🤔 In this video, I break down the fundamentals of data and schemas in simple terms, with easy examples. 📌 What you’ll learn: 🔹 Types of Data Structured Data: organized in rows and columns (SQL tables, spreadsheets) Semi-Structured Data: flexible but with some organization (JSON, XML, logs) Unstructured Data: no predefined format (images, videos, audio, text documents) 🔹 What is a Schema? The blueprint that defines how data is organized, stored, and validated. 🔹 Types of Schema Schema-on-Write (Fixed Schema): define the structure before storing data (e.g., relational databases, data warehouses) Schema-on-Read (Flexible Schema): apply the structure when reading the data (e.g., data lakes, Hadoop, Spark) Schemaless (No Schema): store data without a predefined structure (e.g., NoSQL databases like MongoDB) 🎯 Who is this video for? Students, beginners, data engineers, data analysts, and anyone preparing for data engineering or database interviews. 👍 If this helped you, like, share, and subscribe for more videos. #StructuredData #UnstructuredData #SemiStructuredData #Schema #SchemaOnRead #SchemaOnWrite #Database #SQL #NoSQL #DataAnalytics #DataScience #LearnDataEngineering #TechForBeginners #trendingvideo #viralvideo