FAQ

You can use the disablement vars or disable the entire package in your dbt_project.yml. It’s possible to configure to disable with a condition like specific env.

Here are examples:

Disable specific hooks (the recommended method) -

dbt_project.yml

vars:
  disable_run_results: "{{ target.name not in ['prod','analytics'] }}"
  disable_tests_results: "{{ target.name != 'prod' }}"
  disable_dbt_artifacts_autoupload: "{{ target.name != 'prod' }}"
  disable_dbt_invocation_autoupload: "{{ target.name != 'prod' }}"

Disable the whole package (Elementary tests won’t work) -

dbt_project.yml

models:
  elementary:
    +schema: "elementary"
    +enabled: "{{ target.name in ['prod','analytics'] }}"

Elementary only needs you to run the models once after you install, and on upgrades of minor versions (like 0.7.X -> 0.8.X). On such upgrades we make schema changes, so we need you to rebuild the tables.

For excluding the elementary models from your runs we suggest 2 options:

Use the selector --exclude elementary when you run dbt run.
Set a var that disables the models by default. On version upgrades you could pass it as true.

Here is how you implement option 2:

on your dbt_project.yml add:

dbt_project.yml

models:
  elementary:
    +schema: elementary
    edr:
      +enabled: "{{ var('enable_elementary_models', false) }}"

When you upgrade elementary run:

dbt run --select elementary --vars {enable_elementary_models: true}

The short answer is yes. We recommend that Elementary models will have their own schema, but it is not mandatory. You can change the schema name by using dbt custom schema configuration on your dbt_project.yml. In short, the default dbt generate_schema_name macro concatenate the value provided in schema configuration key to the target schema, as in: target_schema_custom_schema.

If you want a different behaviour, like configuring a full name for the Elementary schema, you can override the default generate_schema_name macro with your logic. Before you do that, make sure that there isn’t already a macro named generate_schema_name.sql in your project.

Here is a macro you can use that would search for a config under meta named schema_name. If it exists, that would be the schema name. If not - the original dbt logic would be followed:

generate_schema_name.sql

{% macro generate_schema_name(custom_schema_name, node) -%}

    {%- set default_schema = target.schema -%}
    {% set config_meta = node.config.get('meta') %}

    {% if config_meta and config_meta is mapping %}
        {% set schema_name = config_meta.get('schema_name') %}
        {% if schema_name and schema_name is string %}
            {{ return(schema_name) }}
        {% endif %}

    {%- elif custom_schema_name is none -%}

        {{ default_schema }}

    {%- else -%}

        {{ default_schema }}_{{ custom_schema_name | trim }}

    {%- endif -%}

{%- endmacro %}

If you implement this macro and want to name the Elementary schema elementary_data_observability:

dbt_project.yml

models:
  elementary:
    +meta:
      schema_name: "elementary_data_observability"

The Elementary package creates various models to store information about collected dbt artifacts and test results.

To avoid mixing with your existing models, we recommend configuring a dedicated schema for the Elementary models using the dbt custom schema option. Here is an example configuration, that creates a schema with the suffix ‘_elementary’ for elementary models:

dbt_project.yml

models:
  elementary:
    +schema: elementary

Custom / Singular tests are supported by Elementary.

Alerts: Full support.
Report: Only tests that ref one model are presented in the report under the relevant model.

You can add configuration to your custom tests with a config block:

custom_test.sql

{{ config(
    tags=["Tag1","Tag2"]
    meta={
        "description": "This is a description",
        "owner": "Maayan Salom"
    }
) }}

<test query here>

Elementary’s incremental models aren’t truncated by the standard full refresh flag. This is because generally you wouldn’t want those models to truncate

To run full-refresh, use the elementary_full_refresh var like this:

dbt run --select elementary --vars '{"elementary_full_refresh": "true"}

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