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What is the difference between combining and separating tables? #2092
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Hi @limhasic 👋 I'm a bit confused about exactly what you're asking -- do you mind clarifying a bit further? I understood your question to be -- "Why keep tables laid out in a multi-table pattern when I can just combine them into a single table and use SDV instead that way?" If this is incorrect, let me know! Here's the relevant key differences: Single Table: Works best when you have a single identifier column (e.g. Multi Table: Supports cases where you have multiple identifier / id columns in your data that have a relational link between them. With Multi Table, you can specify the relationships between identifier columns and SDV will learn to model them more effectively. For example, SDV will maintain referential integrity when generating synthetic data (e.g. the combinations of |
Hi @limhasic, To add to this, we always recommend you to use with data that is the closest to its original source. The more you modify the data (splitting, joining, etc.), the more logic/dependencies you will be introducing into your dataset. As a result, it becomes much more difficult for SDV synthesizers to learn this out-of-the-box, because they must reverse-engineer all the changes that were introduced. Hope that helps, and as @srinify mentioned, it would be helpful if you can provide an example to help us clarify the question further. Thanks. |
Hi @limhasic we hope our answers were helpful! It's been 2 weeks since we've heard from you and our general posture is to close out issues with no response after 2 weeks! If you have more questions, feel free to open more issues! |
After multi-table synthesis and joining all tables, existing single table synthesis
What is the difference between combining and separating tables?
Could you please explain it well?
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