Dbt cloud target error but not in Dbt core

Help
1

Hi, i’m new in dbt .
I want to run different models with different targets at dbt cloud. I saw the command :
" dbt run --select model.sql --target dev "
This command line works in dbt core, but when I tried to do it at dbt cloud , it doesn’t run my model in the target called dev, it takes the default one.

I have tried doing dbt debug --target dev to see the connection but also got errors in dbt cloud (but no at dbt core):

“No matching handler found for rpc method None (which=debug)”

I want to work at dbt cloud because it easier for me to set jobs, so how can I fix it?

Thanks!

2

Hello,
Modify the value of target in the dbt cloud environments… when you create a new env the target is set to a value equal to default… you can change it to dev
To handle multiple targets in dbt models you need to use jinja macros

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3

First, thank you for your answer but as I said, I´m a new developer on dbt and I´d like to understand better about environments… from what you said, or at least, from what I understood, we can only define one schema target per environment? That is, if I need to work with different taget schemas do I have to create an environment for each schema? On the other hand to work with different targets in the same environment I could use jinja macros… but how can I do this? I looked in the documentation and didn’t find anything about this. Is there some variable environment or similar? I´m sorry if my questions are too basic…

4

I´ve found a way to solve my problem… first I deleted macro get_custom_schema from macro´s directory. Then I create three different outputs for my target in my profile.yml:

outputs:
    stg:
      type: bigquery
      method: oauth
      project: dw-mkt-stn
      dataset: stage
      threads: 4
      timeout_seconds: 300
      location: US 
      priority: interactive
      retries: 1

    raw:
      type: bigquery
      method: oauth
      project: dw-mkt-stn
      dataset: raw
      threads: 4
      timeout_seconds: 300
      location: US 
      priority: interactive
      retries: 1

    prd:
      type: bigquery
      method: oauth
      project: dw-mkt-stn
      dataset: analytics
      threads: 4
      timeout_seconds: 300
      location: US 
      priority: interactive
      retries: 1

I ´ve created 3 main models: marts, staging and raw. When I run the models from marts, I just pass --target prd. In the case of staging, just add the --target stg parameter to the dbt run. But in the case of raw, I have different datasets with “Raw” as the dataset prefix… like: raw_googleads, raw_facebookads and so on.
In this case, for each submodel created below the Raw model, I define the schema:schema_name and then the dbt understands that I am providing a custom_schema and creates the dataset using the target_name that I defined: Raw, adding the schema_name defined in schema.

I’m sure this might not be the best solution but it’s the one I managed to find and I’m sharing it with you guys. If anyone wants more details or wants to collaborate with other solutions, feel free to comment here.

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