Lectures

Find out why data normalization is needed in databases, what problems it solves, when denormalization might actually be useful, and what the normalization process looks like with real-world examples. This lecture will help you get the basics of organizing data to make working with it more efficient and reliable.

First Normal Form (1NF) is a fundamental principle of relational database design that requires all values in a table to be atomic, rows to be unique, and the structure to be uniform. In this material, you'll learn how to spot and fix 1NF violations, see practical examples, and get tips on how to organize your data the right way.

In this article, you'll find out what the Second Normal Form (2NF) is in relational databases, how to spot and fix partial dependencies, and you'll see hands-on examples of table normalization to boost the quality and flexibility of your data storage.

Third Normal Form (3NF) is a key step in designing relational databases that helps you get rid of transitive dependencies and data duplication. In this material, you'll learn how to spot 3NF violations, how to fix them with hands-on examples, and why sticking to this form makes your data cleaner and more reliable.

On this page, you'll find out what data denormalization is, when and why to use it, plus you'll check out some practical examples and dig into the possible consequences of this approach. This stuff is super useful for devs and analysts working with big data volumes and gnarly queries.