### Install MkDocs Material and Serve Docs

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/contributing.md

Install the necessary package for local documentation serving and then start the local development server. Docs are automatically pushed to the website via CI/CD.

```bash
pip install mkdocs-material
mkdocs serve
```

--------------------------------

### JSON Output Example

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/issues-in-log.md

This is an example of the JSON output format, suitable for programmatic consumption and automation.

```json
[
  {
    "test_name": "dbt_project_evaluator.marts.dag.is_empty_fct_model_fanout_",
    "results": [
      {"resource_name": "my_model", "num_dependents": 5}
    ]
  },
  {
    "test_name": "dbt_project_evaluator.marts.tests.is_empty_fct_missing_primary_key_tests_",
    "results": [
      {"resource_name": "stg_orders", "resource_type": "model"}
    ]
  }
]
```

--------------------------------

### Staging Directory Structure

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Example of a well-organized staging directory structure with subdirectories for each data source (e.g., 'braintree', 'stripe').

```bash
├── dbt_project.yml
└── models
    ├── marts
    └── staging
        ├── braintree
        └── stripe
```

--------------------------------

### Model Naming Convention Example

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Illustrates a dbt project structure where a model is nested in the 'marts' subdirectory and requires a prefix like 'fct_' or 'dim_'.

```bash
├── dbt_project.yml
└── models
    ├── marts
        └── model_8.sql
```

--------------------------------

### Configure Model Access for Exposures (YAML)

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/governance.md

Example YAML configuration showing how to set the 'access' property for dbt models. Ensure models that are depended upon by exposures are set to 'public'.

```yaml
models:
  - name: fct_model_6
    description: very important OKR reporting model
    access: public
    config:
      materialized: table
      contract:
        enforced: true
    columns:
      - name: id 
        description: the primary key of my OKR model
        data_type: integer
  - name: dim_model_7
    description: excellent model
    access: private
```

```yaml
models:
  - name: fct_model_6
    description: very important OKR reporting model
    access: public
    config:
      materialized: table
      contract:
        enforced: true
    columns:
      - name: id 
        description: the primary key of my OKR model
        data_type: integer
  - name: dim_model_7
    description: excellent model
    access: public
```

--------------------------------

### Filter JSON Output with `jq`

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/issues-in-log.md

Examples of using `jq` to filter and extract specific information from the JSON output.

```bash
# Get all test names
dbt build --select package:dbt_project_evaluator -q | jq '.[].test_name'
```

```bash
# Get results for a specific test
dbt build --select package:dbt_project_evaluator -q | jq '.[] | select(.test_name | contains("fanout"))'
```

```bash
# Count total violations
dbt build --select package:dbt_project_evaluator -q | jq '[.[].results | length] | add'
```

--------------------------------

### Model Directory Rule: Staging Model

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Shows an example of a staging model ('stg_model_3') incorrectly nested in 'source_1' instead of its correct parent source directory 'source_2'.

```bash
├── dbt_project.yml
└── models
    ├── marts
    └── staging
        └── source_1
            ├── stg_model_3.sql
```

--------------------------------

### Public Model Without Documentation

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/governance.md

Example of a public model missing model-level and column-level descriptions. This check highlights models that may be difficult for consumers to understand and use.

```yaml
# public model without documentation
models:
  - name: report_1
    access: public
    columns:
      - name: id


```

--------------------------------

### Public Model Without Contract Configuration

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/governance.md

Example of a public model definition missing the contract configuration. This check flags models that lack data type and column enforcement.

```yaml
# public model without a contract
models:
  - name: report_1
    description: very important OKR reporting model
    access: public


```

--------------------------------

### Override dbt Project Evaluator Variables

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/overriding-variables.md

Customize test and documentation coverage targets, and specify primary key test macros by overriding the default variables in your dbt_project.yml file. This example sets coverage targets to 75% and uses `dbt_constraints.test_primary_key` for primary key validation.

```yaml
vars:
  dbt_project_evaluator:
    documentation_coverage_target: 75
    test_coverage_target: 75
    primary_key_test_macros: [["dbt_constraints.test_primary_key"]]
    

```

--------------------------------

### Override chained_views_threshold Variable

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/overriding-variables.md

Adjust the threshold for unacceptable chain lengths of views. The default is 4, but this example sets it to 8.

```yaml
vars:
  dbt_project_evaluator:
    # set your chained views threshold to 8 instead of 4
    chained_views_threshold: 8
```

--------------------------------

### Disable dbt Project Evaluator Models in Production

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/ci-check.md

Optionally disable dbt Project Evaluator models in production environments by setting an environment variable. This example shows how to enable models only when the environment variable is set to 'true'.

```yaml
models:
  dbt_project_evaluator:
    +enabled: "{{ env_var('DBT_PROJECT_EVALUATOR_ENABLED', 'true') | lower == 'true' | as_bool }}"
```

--------------------------------

### Intermediate and Marts Model Directory Structure

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Shows the recommended directory structure for intermediate and mart models, ensuring files are nested closest to their model type folder.

```bash
├── dbt_project.yml
└── models
    └── marts
        ├── fct_model_6.sql
        └── intermediate
            └── int_model_5.sql
```

--------------------------------

### Run dbt Build with Custom Seed

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/exceptions.md

Execute this command to build the dbt Project Evaluator package along with your custom exception seed.

```bash
dbt build --select package:dbt_project_evaluator dbt_project_evaluator_exceptions
```

--------------------------------

### Configure Output Format and Quoting

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/issues-in-log.md

Customize the output format to 'table', 'csv', or 'json', and specify quoting characters for database and schema names.

```yaml
on-run-end:
  - "{{ dbt_project_evaluator.print_dbt_project_evaluator_issues(format='json', quote='"') }}"
```

--------------------------------

### Model Test Directory Structure

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Model tests and documentation should be located in a '.yml' file within the same subdirectory as the corresponding model, under 'models/'. This ensures tests are co-located with the models they validate.

```bash
├── dbt_project.yml
└── models
    └── marts
        ├── int_model_4.sql
    └── staging
        ├── staging.yml
```

```bash
├── dbt_project.yml
└── models
    └── marts
        ├── int_model_4.sql
        ├── marts.yml
    └── staging
        ├── staging.yml
```

--------------------------------

### Configure `use_native_agate_printing` in `dbt_project.yml`

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/overriding-variables.md

Set `use_native_agate_printing` to `false` to use a pure Jinja implementation for printing issues. This is necessary for environments where agate's native printing functions do not work, such as dbt Cloud CLI/IDE, or when using `format='csv'` with dbt Fusion.

```yaml
vars:
  dbt_project_evaluator:
    # use pure Jinja printing for dbt Cloud CLI, IDE, or Fusion with CSV format
    use_native_agate_printing: false
```

--------------------------------

### Document Public Model with Columns

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/governance.md

Add model-level and column-level descriptions to public models to improve usability for consumers. This is a stricter check than general model documentation.

```yaml
models:
  - name: report_1
    description: very important OKR reporting model
    access: public
    columns:
      - name: id 
        description: the primary key of my OKR model
```

--------------------------------

### Configure `on-run-end` Hook

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/issues-in-log.md

Add this line to your `dbt_project.yml` to automatically print evaluation issues to the logs after each run.

```yaml
on-run-end:
  - "{{ dbt_project_evaluator.print_dbt_project_evaluator_issues() }}"
```

--------------------------------

### Direct Join to Source Model

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/modeling.md

Identifies models that directly reference both another model and a source. This pattern is flagged to encourage using staging models as an intermediary.

```sql
SELECT
    *
FROM
    {{ ref('int_model_4') }}
WHERE
    {{ source('source_1', 'table_5') }}
    AND {{ ref('model_1') }}
```

--------------------------------

### Exclude Package and Paths

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/excluding-packages-and-paths.md

Combine `exclude_packages` and `exclude_paths_from_project` to exclude both a specific package and multiple paths within the project.

```yaml
vars:
  exclude_packages: ["upstream_package"]
  exclude_paths_from_project: ["models/legacy/", "/my_date_spine.sql"]
```

--------------------------------

### Enable Pure Jinja Printing for dbt Cloud

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/issues-in-log.md

Set this variable in `dbt_project.yml` to use a Jinja-based printing method, which is required for dbt Cloud CLI/IDE and for CSV output in dbt Fusion.

```yaml
vars:
  use_native_agate_printing: false
```

--------------------------------

### Calculate Documentation Coverage Percentage

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/documentation.md

This SQL model calculates the percentage of enabled models in a dbt project that have a configured description. It can warn if the coverage is below 100% and allows overriding the target threshold.

```sql
--8<-- "models/marts/documentation/fct_documentation_coverage.sql"
```

--------------------------------

### AWS Athena profiles.yml Configuration

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/integration_tests/README.md

This sample profiles.yml file is used for local Athena tests. Ensure environment variables are set.

```yaml
athena: # for local tests only
  type: athena
  s3_staging_dir: {{ env_var('ATHENA_S3_STAGING_DIR') }}
  s3_data_dir: {{ env_var('ATHENA_S3_DATA_DIR') }}
  s3_data_naming: schema_table_unique
  region_name: {{ env_var('ATHENA_REGION') }}
  schema: {{ env_var('ATHENA_SCHEMA') }}
  database: awsdatacatalog
  work_group: {{ env_var('ATHENA_WORKGROUP') }}
  num_retries: 2
  threads: 4
```

--------------------------------

### Run dbt Build Commands in CI

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/ci-check.md

Execute dbt build commands as part of your CI job. This includes building modified resources and then running the dbt Project Evaluator package, optionally including exception tests.

```bash
dbt build --select state:modified+ --exclude package:dbt_project_evaluator
dbt build --select package:dbt_project_evaluator
```

```bash
dbt build --select state:modified+ --exclude package:dbt_project_evaluator
dbt build --select package:dbt_project_evaluator dbt_project_evaluator_exceptions
```

--------------------------------

### Model Directory Rule: Marts Model

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Demonstrates a marts model ('dim_model_7') incorrectly nested within the 'intermediate' subdirectory instead of the 'marts' directory.

```bash
├── dbt_project.yml
└── models
    └── marts
        └── intermediate
            ├── dim_model_7.sql
```

--------------------------------

### Run with `--quiet` and `jq` for Clean JSON

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/issues-in-log.md

Combine dbt's `--quiet` flag with `jq` to suppress dbt logs and process the clean JSON output for automation tasks.

```bash
dbt build --select package:dbt_project_evaluator -q | jq '.'
```

--------------------------------

### Source Definition Directory Structure

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Source definitions should reside in a 'source.yml' file within a subdirectory named after the source, located under 'models/staging/'. This promotes organization when managing multiple data sources.

```bash
├── dbt_project.yml
└── models
    ├── marts
    └── staging
        └── source_1
            ├── source.yml
```

```bash
├── dbt_project.yml
└── models
    ├── marts
    └── staging
        ├── source_1
        └── source_2
            ├── source.yml
```

```bash
├── dbt_project.yml
└── models
    ├── marts
    └── staging
        ├── braintree
        └── stripe
```

--------------------------------

### List Undocumented Sources

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/documentation.md

This SQL model identifies and lists all dbt sources that do not have a description configured. It stresses the importance of source documentation for understanding data loading processes.

```sql
fct_undocumented_sources
```

```yaml
sources:
  - name: my_source
    description: This is the source description
    tables:
      - name: my_table
```

--------------------------------

### Set Environment Variables for AWS Athena

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/integration_tests/README.md

Configure these environment variables before running local integration tests with AWS Athena.

```bash
ATHENA_S3_STAGING_DIR=
ATHENA_S3_DATA_DIR=
ATHENA_REGION=
ATHENA_SCHEMA=
ATHENA_WORKGROUP=
```

--------------------------------

### Configure dbt_project.yml for Specific Adapters

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/README.md

This configuration is necessary for adapters like Databricks/Spark, DuckDB, Redshift, ClickHouse, and Fabric. It ensures the project can run across these adapters by managing macro overrides.

```yaml
# dbt_project.yml

dispatch:
  - macro_namespace: dbt
    search_order: ['dbt_project_evaluator', 'dbt']
```

--------------------------------

### Log Custom Rules with `is_empty`

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/issues-in-log.md

Apply the `dbt_project_evaluator.is_empty` data test to custom models to log their results.

```yaml
models:
  - name: my_custom_rule_model
    description: This is my custom project evaluator check 
    data_tests:
      - dbt_project_evaluator.is_empty
```

--------------------------------

### Model Directory Rule: Intermediate Model

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/structure.md

Illustrates an intermediate model ('int_model_4') incorrectly nested within the 'marts' subdirectory instead of the 'intermediate' subdirectory.

```bash
├── dbt_project.yml
└── models
    └── marts
        ├── int_model_4.sql
```

--------------------------------

### Duplicate Source Definitions

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/modeling.md

Highlights database objects that are mapped to by more than one dbt source node. This can lead to inaccurate lineage.

```yaml
sources:
  - name: source_1
    schema: real_schema
    database: real_database
    tables:
      - name: table_5
      - name: raw_table_5
        identifier: table_5
```

--------------------------------

### List Undocumented Source Tables

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/documentation.md

This SQL model lists all source tables in a dbt project that lack a configured description. It highlights the value of documenting source tables for project contributors.

```sql
fct_undocumented_source_tables
```

```yaml
sources:
  - name: my_source
    tables:
      - name: my_table
        description: This is the source table description
```

--------------------------------

### Configure Markdownlint for VSCode

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/contributing.md

Configure the markdownlint extension in VSCode to enforce specific Markdown formatting rules. This JSON snippet should be placed within your VSCode settings.

```json
{
    "markdownlint.config": {
        "ul-indent": {"indent": 4},
        "MD036": false,
        "MD046": false
    }
}
```

--------------------------------

### Customize Model Naming Conventions

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/overriding-variables.md

Define custom model types, folder names, and prefixes to align with your project's structure. Update `model_types`, and add new variables like `<model_type>_folder_name` and `<model_type>_prefixes` in your dbt_project.yml.

```yaml
vars:
  dbt_project_evaluator:
    model_types: ['staging', 'intermediate', 'marts', 'other', 'util']
    util_folder_name: 'util'
    util_prefixes: ['util_']
```

--------------------------------

### dbt_project.yml Configuration for Specific Adapters

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/index.md

This configuration is required for adapters like Databricks/Spark, DuckDB, Redshift, and Fabric. It ensures the package can run by overriding certain dbt core macros.

```yaml
dispatch:
  - macro_namespace: dbt
    search_order: ['dbt_project_evaluator', 'dbt']
```

--------------------------------

### Avoid Hard-Coded References in dbt Models

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/modeling.md

Use `ref` for models and `source` for raw data instead of direct relation references. This ensures dbt can manage dependencies and build order correctly.

```sql
with orders as (
    select * from my_db.my_schema.orders
),
customers as (
    select * from my_schema.customers
)
select
    orders.order_id,
    customers.name
from orders
left join customers on
  orders.customer_id = customers.id
```

```sql
with orders as (
    select * from {{ ref('orders') }}
),
customers as (
    select * from {{ ref('customers') }}
)
select
    orders.order_id,
    customers.name
from orders
left join customers on
  orders.customer_id = customers.id
```

--------------------------------

### List Undocumented Models

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/documentation.md

This SQL model identifies and lists all models within a dbt project that are missing a configured description. It emphasizes the importance of model documentation for downstream consumers.

```sql
fct_undocumented_models
```

--------------------------------

### Identify Unused Sources in dbt Project

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/modeling.md

This rule flags sources defined in your dbt project's YAML files that are not referenced by any models. This helps in cleaning up unused configurations.

```yaml
sources:
  - name: some_source
    database: raw
    tables:
      - name: table_1
      - name: table_2
      - name: table_3
      - name: table_4  # <-- remove this line
```

--------------------------------

### Define Exceptions in CSV Seed

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/exceptions.md

Use this CSV format to specify exceptions. The `id_to_exclude` can be a specific value or a `like` pattern.

```csv
fct_name,column_name,id_to_exclude,comment
fct_multiple_sources_joined,child,stg_%_unioned,Models called _unioned can union multiple sources
```

--------------------------------

### Downstream Model Dependent on Source

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/modeling.md

Detects marts or intermediate models that directly depend on a source node. This indicates a potential missing staging model.

```sql
SELECT
    *
FROM
    {{ source('source_1', 'table_5') }}
WHERE
    {{ ref('fct_model_9') }}
```

--------------------------------

### Identify Staging Models Dependent on Marts or Intermediate Models

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/modeling.md

This rule flags staging models that depend on intermediate or marts models, which violates dbt best practices. Staging models should only select from source nodes.

```sql
SELECT * FROM {{ ref('fct_staging_dependent_on_marts_or_intermediate') }}
```

--------------------------------

### Add Contract to Public Model

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/governance.md

Configure a public model with a contract to enforce data types and columns. This ensures downstream consumers receive consistent data.

```yaml
models:
  - name: report_1
    description: very important OKR reporting model
    access: public
    config:
      contract:
        enforced: true
    columns:
      - name: id 
        data_type: integer
```

--------------------------------

### Exclude Models/Sources in a Given Path

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/excluding-packages-and-paths.md

Use `exclude_paths_from_project` to exclude models or sources located within specific directories or files.

```yaml
vars:
  exclude_paths_from_project: ["models/legacy/"]
```

--------------------------------

### Exclude a Whole Package

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/excluding-packages-and-paths.md

Configure `exclude_packages` to prevent evaluation of models from a specified package.

```yaml
vars:
  exclude_packages: ["upstream_package"]
```

--------------------------------

### Identify Staging Models Dependent on Other Staging Models

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/rules/modeling.md

This rule identifies parent/child relationships where staging models depend on other staging models. This may indicate a naming issue or that the child model should reference a source.

```sql
SELECT * FROM {{ ref('fct_staging_dependent_on_staging') }}
```

--------------------------------

### Disable Default Exception Seed in dbt_project.yml

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/exceptions.md

To use a custom exception seed, disable the default one from the package by adding this configuration to your `dbt_project.yml` file.

```yaml
seeds:
  dbt_project_evaluator:
    dbt_project_evaluator_exceptions:
      +enabled: false
```

--------------------------------

### Override DAG Thresholds

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/overriding-variables.md

Adjust the `models_fanout_threshold` and `too_many_joins_threshold` to customize DAG complexity checks. Set these in your dbt_project.yml under the `vars` section for `dbt_project_evaluator`.

```yaml
vars:
  dbt_project_evaluator:
    models_fanout_threshold: 10
    too_many_joins_threshold: 6
```

--------------------------------

### Override Execution Variables

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/overriding-variables.md

Customize 'insert_batch_size' for batch insertion and 'max_depth_dag' to limit the maximum distance between nodes in DAG relationship calculations. The default for 'insert_batch_size' is 10000, and 'max_depth_dag' varies by adapter (9 for BigQuery, Spark, Fabric; -1 for others).

```yaml
vars:
  dbt_project_evaluator:
    # update the number of records inserted from the graph from 10,000 to 500 to reduce query size
    insert_batch_size: 500
    # set the maximum distance between nodes to 5
    max_depth_dag: 5
```

--------------------------------

### Override Test Severity in dbt_project.yml

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/ci-check.md

Configure the severity of dbt Project Evaluator tests using an environment variable. This allows you to set tests to 'error' in CI environments and 'warn' in others.

```yaml
data_tests:
  dbt_project_evaluator:
    +severity: "{{ env_var('DBT_PROJECT_EVALUATOR_SEVERITY', 'warn') }}"
```

--------------------------------

### Disable Specific Model Tests in dbt_project.yml

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/customization.md

To disable tests for a specific model, such as `fct_model_fanout`, set `+enabled: false` for that model within the `dag` configuration of the dbt Project Evaluator. This allows fine-grained control over which models are tested.

```yaml
models:
  dbt_project_evaluator:
    marts:
      dag:
        # disable single DAG model
        fct_model_fanout:
          +enabled: false
```

--------------------------------

### Disable Tests in dbt_project.yml

Source: https://github.com/dbt-labs/dbt-project-evaluator/blob/main/docs/customization/customization.md

To disable all tests from the dbt Project Evaluator package, set `+enabled: false` for the `tests` directory within the package's configuration. This is useful for completely deactivating the test coverage suite.

```yaml
models:
  dbt_project_evaluator:
    marts:
      tests:
        # disable entire test coverage suite
        +enabled: false
```

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