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Hi,
You may want to use same strategy as the one used in this blog post. They create schema for other reasons than yours. But, the method they use remains applicable to your use case.
They provide code samples that you can reuser and adapt to your situation.
Best,
Didier
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Thank you Didier,
Just to be clear I understand your recommendation here:
Based on what I see in that post, the strategy is: write data to ensure schema. They have a python script that is run to populate some data first (
run.sh
).In other words, if I want to handle this in my pipeline, I would also have a step in my deployment to production, either before or after deployment, that would trigger a dummy insertion of data, to ensure the schema has been created?
My problem is that not all changes I introduce, immediately should have an effect in production, as not all the measures we are collecting are always readily available in production. However, as I explained previously, absence of data should be supported by the application, as absence of data is just an empty resultset and my application can handle that scenario.
So, is the recommendation here:
Is that correct?