SQL vs. NoSQL databases: What's the difference?

Описание: Curious about the difference between a relational vs non-relational database? In this video, we break down what is a NoSQL database, what is a SQL database, and explain the NoSQL pros and cons you need to know when choosing the right database for your project.

You'll learn how SQL databases use structured tables with predefined schemas, support strong relationships between data, and offer ACID compliance for consistency—making them ideal for banking, e-commerce, and customer management.
On the other hand, NoSQL databases offer flexibility, faster performance, and scalability for handling unstructured data like text, images, and videos—perfect for real-time applications, IoT, and big data analytics.

We’ll also cover examples like MySQL, PostgreSQL, MongoDB, and graph databases to help you understand where each option shines.

🕐 Timeline:
0:00 - 0:34 Introduction to databases: SQL vs NoSQL
0:34 - 1:24 What is a SQL database? (Relational databases explained)
1:24 - 1:57 What is a NoSQL database? (Non-relational databases explained)
1:57 - 2:26 Types of NoSQL databases (document, key-value, column, graph)
2:26 - 3:06 NoSQL pros and cons compared to SQL
3:06 - 3:26 When to choose SQL or NoSQL for your project
3:26 - 3:46 Conclusion

Key Topics Covered:
What is a SQL database
What is a NoSQL database
NoSQL pros and cons compared to SQL databases
When to use SQL databases and when NoSQL is the better choice
Practical examples of SQL and NoSQL in real-world applications (e-commerce, social media, analytics)

Important Notes:
SQL databases are ideal for structured data, consistency, and complex queries (e.g., banking, e-commerce)
NoSQL databases offer flexibility, horizontal scalability, and fast performance for unstructured or rapidly changing data
Choose SQL when you need strong data relationships, reliable transactions, and ACID compliance.
Choose NoSQL for big data, real-time apps, IoT, or when working with varied and unstructured data types
Be aware: NoSQL trades some consistency for speed and may require more manual data management and optimization
SQL databases have more mature tools and documentation, which can make development and troubleshooting easier

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