The dbt-bigquery transformer uses SQL to transform data stored in your warehouse.

Getting Started #

Prerequisites #

If you haven't already, follow the initial steps of the Getting Started guide:

  1. Install Meltano
  2. Create your Meltano project

Installation and configuration #

  1. Add the dbt-bigquery transformer to your project using meltano add :

    meltano add transformer dbt-bigquery
  2. Configure the settings below using meltano config .

Next steps #

Follow the remaining steps of the Getting Started guide:

  1. Transform loaded data for analysis
If you run into any issues, learn how to get help.

Settings #

Settings for dbt itself can be configured through dbt_project.yml as usual, which can be found at transform/dbt_project.yml in your Meltano project. dbt also has adapter-specific documentation for BigQuery.

The settings for transformer dbt-bigquery that are known to Meltano are documented below. To quickly find the setting you're looking for, use the Table of Contents at the top of the page.

Project Directory (project_dir) #

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set project_dir <project_dir>

export DBT_BIGQUERY_PROJECT_DIR=<project_dir>

Profiles Directory (profiles_dir) #

  • Environment variable: DBT_PROFILES_DIR, alias: DBT_BIGQUERY_PROFILES_DIR
  • Default: $MELTANO_PROJECT_ROOT/transform/profiles/bigquery

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set profiles_dir <profiles_dir>

export DBT_PROFILES_DIR=<profiles_dir>

Authentication Method (auth_method) #

The auth method to use. One of: “oauth”, “oauth-secrets”, or “service-account”

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set auth_method <auth_method>

export DBT_BIGQUERY_AUTH_METHOD=<auth_method>

Project (project) #

The BigQuery project ID.

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set project <project>

export DBT_BIGQUERY_PROJECT=<project>

dataset #

The dataset to use.

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set dataset <dataset>

export DBT_BIGQUERY_DATASET=<dataset>

Refresh Token (refresh_token) #

The refresh token, if authenticating via oauth-secrets method.

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set refresh_token <refresh_token>

export DBT_BIGQUERY_REFRESH_TOKEN=<refresh_token>

Client ID (client_id) #

The client id to use, if authenticating via oauth-secrets method.

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set client_id <client_id>

export DBT_BIGQUERY_CLIENT_ID=<client_id>

Client Secret (client_secret) #

The client secret to use, if authenticating via oauth-secrets method.

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set client_secret <client_secret>

export DBT_BIGQUERY_CLIENT_SECRET=<client_secret>

Token URI (token_uri) #

The token redirect URI

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set token_uri <token_uri>

export DBT_BIGQUERY_TOKEN_URI=<token_uri>

keyfile #

The path to the keyfile.json` to use, if authenticating via service-account method.

How to use #

Manage this setting using meltano config or an environment variable:

meltano config dbt-bigquery set keyfile <keyfile>

export DBT_BIGQUERY_KEYFILE=<keyfile>

Commands #

The dbt-bigquery transformer supports the following commands that can be used with meltano invoke :

clean #

  • Equivalent to: dbt clean

Delete all folders in the clean-targets list (usually the dbt_modules and target directories.)

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:clean [additional arguments...]

compile #

  • Equivalent to: dbt compile

Generates executable SQL from source model, test, and analysis files. Compiled SQL files are written to the target/ directory.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:compile [additional arguments...]

deps #

  • Equivalent to: dbt deps

Pull the most recent version of the dependencies listed in packages.yml

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:deps [additional arguments...]

run #

  • Equivalent to: dbt run

Compile SQL and execute against the current target database.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:run [additional arguments...]

seed #

  • Equivalent to: dbt seed

Load data from csv files into your data warehouse.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:seed [additional arguments...]

snapshot #

  • Equivalent to: dbt snapshot

Execute snapshots defined in your project.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:snapshot [additional arguments...]

test #

  • Equivalent to: dbt test

Runs tests on data in deployed models.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:test [additional arguments...]

freshness #

  • Equivalent to: dbt source freshness

Check the freshness of your source data.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:freshness [additional arguments...]

build #

  • Equivalent to: dbt build

Will run your models, tests, snapshots and seeds in DAG order.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:build [additional arguments...]

docs-generate #

  • Equivalent to: dbt docs generate

Generate documentation for your project.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:docs-generate [additional arguments...]

docs-serve #

  • Equivalent to: dbt docs serve

Serve documentation for your project. Make sure you ran docs-generate first.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:docs-serve [additional arguments...]

debug #

  • Equivalent to: dbt debug

Debug your DBT project and warehouse connection.

How to use #

Run this command using meltano invoke:

meltano invoke dbt-bigquery:debug [additional arguments...]

Looking for help? #

If you're having trouble getting the dbt-bigquery transformer to work, look for an existing issue in its repository, file a new issue, or join the Meltano Slack community and ask for help in the #plugins-general channel.

Found an issue on this page? #

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