From the course: Designing Highly Scalable and Highly Available SQL Databases

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Denormalizing for analytical queries

Denormalizing for analytical queries

From the course: Designing Highly Scalable and Highly Available SQL Databases

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Denormalizing for analytical queries

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- [Instructor] In earlier videos we talked about how the data model we choose can influence performance, especially in a scalable database. And at that point, we were looking at event sourcing and CQRS. I want to look again at this issue of how the changes or differences in data models can affect performance. And in particular, I want to talk about normalization and denormalization. Now, traditionally in relational databases, one of the things we do is we go through a process called normalization, and we do this to avoid data anomalies. So what we don't want is to run queries that give us the wrong answer and that can happen if we don't follow certain rules or if we don't follow certain rules and are careful not to institute other measures to avoid data anomalies. Now, there are multiple levels of normalization. Probably the most most commonly used is third normal form. And I won't go into all the details about…

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