A data build tool
› pip install dbtCreate a dbt_project.yml file in the root of your models directory. Also create a profiles.yml file in the ~/.dbt directory. If this directory doesn't exist, you should create it. The
dbt_project.yml file should be checked in to your models repository, so be sure that it does not contain any database
credentials! Make sure that all of your private information is stored in the ~/.dbt/profiles.yml configuration file.
# configure dbt file paths (relative to dbt_project.yml)
source-paths: ["models"] # paths with source code to compile
target-path: "target" # path for compiled code
clean-targets: ["target"] # directories removed by the clean task
test-paths: ["test"] # where to store test results
# default paramaters the apply to _all_ models (unless overriden below)
model-defaults:
enabled: true # enable all models by default
materialized: false # If true, create tables. If false, create views
# custom configurations for each model. Unspecified models will use the model-defaults information above.
models:
pardot: # assuming pardot is listed in the models/ directory
enabled: false # disable all pardot models except where overriden
pardot_emails: # override the configs for the pardot_emails model
enabled: true # enable this specific model
materialized: true # create a table instead of a viewuser: # you can have multiple profiles for different projects
outputs:
my-redshift: # uniquely named, you can have different targets in a profile
type: redshift # only type supported
host: localhost # any IP or fqdn
port: 5439
user: my_user
pass: password
dbname: dev
schema: my_model_schema # the schema to create models in (eg. analyst_collective)
run-target: my-redshift # which target to run sql againstdbt compile to generate runnable SQL from model files
dbt run to run model files on the current run-target database
dbt clean to clear compiled files
From the root directory of this repository, run:
› python setup.py developto install a development version of dbt.