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[supple oxide]

<@&987246399047479336> please have a look, thanks.

Here is an AI assisted attempt to answer your question 🤖. Maybe it helps! In any case, a human is on the way 👍. To continue talking to the AI, you can use </chatgpt:1108714622413963314>.

aving data for the old fields?

When your data model evolves and you add new fields or refactor existing structures, you need to address the issue of old data not having data for the new fields and new data having data for the old fields. Here are a few approaches to handle this situation:

1. Default Values: Assign default values to the new fields when they are introduced. This ensures that even if old data doesn't have values for these fields, they will still have a default value assigned. This approach allows you to maintain consistency in your data model.

2. Backfilling: If possible, you can perform a backfill operation on your existing data to populate the new fields with relevant values. This can be done by running scripts or batch processes that iterate through your old data and fill in the missing information based on certain rules or logic.

3. Null or Empty Values: If assigning default values is not appropriate for your use case, you can allow null or empty values for the new fields in your data model. This means that old records will have null or empty values for these fields, while new records will have actual values.

4. Data Migration: In some cases, it might be necessary to migrate your old data to a completely new structure that aligns with the updated data model. This involves transforming and transferring all existing records to the new structure while ensuring no loss of information.

5. Versioning: Consider implementing versioning in your data model so that different versions of your schema can coexist. This allows you to handle both old and new versions of your data without compromising integrity.

6. Documentation and Communication: It's crucial to document any changes made to the data model and communicate them effectively within your organization. This ensures that everyone understands how to handle old and new data appropriately.

Ultimately, the approach you choose depends on factors such as the nature of your application, impact on existing systems, time constraints, and the importance of maintaining consistency in your data. It's essential to carefully plan and execute the evolution of your data model to minimize any disruptions and ensure a smooth transition.

[lethal goblet]

Usually that means a new data flow. So either it's optional, and treated as such, or as part of the deploy process my data gets patched.

[supple oxide]

@balmy olive

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[balmy olive]

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