Data Modeling

This is the final installment of our schema series. We’ve seen the Star Express and the Snowflake District. Now, it’s time for the ultimate showdown. How does Conductor Mickey decide which tracks to lay down for the “Invisible City”? In the previous posts, we explored two very different ways to organize a subway station: The Star: Fast, flat, and simple. The Snowflake: Detailed, organized, and space-saving. But in the real world of Data Engineering, you can’t always have both. You have to choose your priority: Speed or Storage? ...

In our Invisible City, as the population grows and the subway map expands, Conductor Mickey sometimes finds that a simple Star Hub isn’t enough. When the “Station” information becomes a giant, messy pile of repeating words, it’s time to move to the Snowflake Schema. If the Star Schema is the “Express Line,” the Snowflake is the “Highly Detailed District.” What is a Snowflake Schema? In the previous post, we saw how the Star Schema keeps things simple. But what happens when your data starts repeating itself? What if you have 10 million rows, and you’ve written “United States” and “North America” 10 million times? ...

In our digital subway city, some stations are “Hubs.” They sit right in the middle, and every train line passes through them. In data engineering, if you don’t organize your stations, your subway maps will look like a plate of spaghetti. To keep things clean, we use Dimensional Modeling. The most famous layout for speed is the Star Schema. What is a Star Schema? The Star Schema is designed for one thing: Speed. It organizes your data into two distinct types of citizens: Facts and Dimensions. ...