Sometimes the data you receive from a school is not quite in the format you need in order to bring it into your system. At scale, this can be a significant problem and lead to spaghetti code in your codebase. Our goal is to help you get your data into the necessary format before it hits your system.
This is where data transformations come in.
Data transformations allow you to make adjustments to the school data you receive before it is brought into your system. This can include things like:
- Removing special characters from person or class names.
- Filling in default values where data is missing.
- Merging classes or students together programmatically.
- Removing stale or inactive data.
While general data filtering is technically a data transformation, we address it in a separate document called Sharing Rules.
Managing Transformations
Each integration has its own set of transformations that can be applied to the data. These transformations are applied in the order they are listed, from top to bottom. This allows you to achieve complex results by chaining together multiple simpler transformations.
Types of Transformations
There are three types of transformations available in the system: Basic, AI, and Custom. Each type of transformation has its own use case and level of complexity. You can learn more about the different types of transformations here.