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dbt (data build tool) enables analytics engineers to transform data in their warehouses by simply writing select statements. dbt handles turning these select statements into tables and views. dbt does the T in ELT (Extract, Load, Transform) processes – it doesn’t extract or load data, but it’s extremely good at transforming data that’s already loaded into your warehouse.
please use the Raw JSON API to manually initiate a run using cURL or graphical API tool such as Postman. POST
the following request to the DevLake API Endpoint.
[ [ { "plugin": "dbt", "options": { "projectPath": "/Users/abeizn/demoapp", "projectName": "demoapp", "projectTarget": "dev", "selectedModels": ["my_first_dbt_model","my_second_dbt_model"], "projectVars": { "demokey1": "demovalue1", "demokey2": "demovalue2" } } } ] ]
projectPath
: the absolute path of the dbt project. (required)projectName
: the name of the dbt project. (required)projectTarget
: this is the default target your dbt project will use. (optional)selectedModels
: a model is a select statement. Models are defined in .sql files, and typically in your models directory. (required) And selectedModels accepts one or more arguments. Each argument can be one of:vars
: dbt provides a mechanism variables to provide data to models for compilation. (optional) example: select * from events where event_type = ‘{{ var(“event_type”) }}’ this sql in your model, you need set parameters “vars”: “{event_type: real_value}”