# dbt_project_evaluator
This package highlights areas of a dbt project that are misaligned with dbt Labs' best practices.
Specifically, this package tests for:
1. __[DAG Issues](#dag-issues)__ - your dbt DAG for modeling best practices
2. __[Testing](#testing)__ - your models for testing best practices
3. __[Documentation](#documentation)__ - your models for documentation best practices
3. __[Structure](#structure)__ - your dbt project for file structure and naming best practices
In addition to tests, this package creates the model `int_all_dag_relationships` which holds information about your DAG in a tabular format and can be queried using SQL in your Warehouse.
## ⚠️ NEW PACKAGE WARNING ⚠️
This package is in its early stages! It's very likely that you could encounter bugs, and functionality will be changing quickly as we gather feedback from end users. Please do not hesitate to create new issues in this repo for bug reports and/or feature requests, and we appreciate your patience as we continue to enhance this package!
## Using This Package
Installation Instructions
Check [dbt Hub](https://hub.getdbt.com/dbt-labs/dbt_project_evaluator/latest/) for the latest installation instructions, or [read the docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.
How It Works
This package will:
1. Parse your [graph](https://docs.getdbt.com/reference/dbt-jinja-functions/graph) object and write it into your warehouse as a series of models (see [models/marts/core](https://github.com/dbt-labs/dbt-project-evaluator/tree/main/models/marts/core))
2. Create another series of models that each represent one type of misalignment in your project (below you can find a full list of each misalignment and its accompanying model)
3. Test those models to alert you to the presence of the misalignment
Once you've installed the package, all you have to do is run a `dbt build --select package:dbt_project_evaluator`!
----
## Package Documentation
__[DAG Issues](#dag-issues)__
- [Direct Join to Source](#direct-join-to-source)
- [Downstream Models Dependent on Source](#downstream-models-dependent-on-source)
- [Model Fanout](#model-fanout)
- [Multiple Sources Joined](#multiple-sources-joined)
- [Rejoining of Upstream Concepts](#rejoining-of-upstream-concepts)
- [Root Models](#root-models)
- [Source Fanout](#source-fanout)
- [Staging Models Dependent on Downstream Models](#staging-models-dependent-on-downstream-models)
- [Staging Models Dependent on Other Staging Models](#staging-models-dependent-on-other-staging-models)
- [Unused Sources](#unused-sources)
__[Testing](#testing)__
- [Models without Primary Key Tests](#models-without-primary-key-tests)
- [Test Coverage](#test-coverage)
__[Documentation](#documentation)__
- [Documentation Coverage](#documentation-coverage)
- [Undocumented Models](#undocumented-models)
__[Structure](#structure)__
- [Model Naming Conventions](#model-naming-conventions)
- [Model Directories](#model-directories)
- [Source Directories](#model-directories)
- [Test Directories](#test-directories)
__[Querying the DAG with SQL](#querying-the-dag-with-sql)__
__[Limitations](#limitations)__
----
## DAG Issues
### Direct Join to Source
#### Model
`fct_direct_join_to_source` ([source](models/marts/dag/fct_direct_join_to_source.sql)) shows each parent/child relationship where a model has a reference to
both a model and a source.
#### Graph Example
`int_model_4` is pulling in both a model and a source.
#### Reason to Flag
We highly recommend having a one-to-one relationship between sources and their corresponding `staging` model, and not having any other model reading from the source. Those `staging` models are then the ones read from by the other downstream models.
This allows renaming your columns and doing minor transformation on your source data only once and being consistent
across all the models that will consume the source data.
#### How to Remediate
In our example, we would want to:
1. create a `staging` model for our source data if it doesn't exist already
2. and join this `staging` model to other ones to create our downstream transformation instead of using the source
After refactoring your downstream model to select from the staging layer, your DAG should look like this:
### Downstream Models Dependent on Source
#### Model
`fct_marts_or_intermediate_dependent_on_source` ([source](models/marts/dag/fct_marts_or_intermediate_dependent_on_source.sql)) shows each downstream model (`marts` or `intermediate`)
that depends directly on a source node.
#### Graph Example
`fct_model_9`, a marts model, builds from `source_1.table_5` a source.
#### Reason to Flag
We very strongly believe that a staging model is the atomic unit of data modeling. Each staging
model bears a one-to-one relationship with the source data table it represents. It has the same
granularity, but the columns have been renamed, recast, or usefully reconsidered into a consistent
format. With that in mind, if a `marts` or `intermediate` type model joins directly to a `{{ source() }}`
node, there likely is a missing model that needs to be added.
#### How to Remediate
Add the reference to the appropriate `staging` model to maintain an abstraction layer between your raw data
and your downstream data artifacts.
After refactoring your downstream model to select from the staging layer, your DAG should look like this:
### Model Fanout
#### Model
`fct_model_fanout` ([source](models/marts/dag/fct_model_fanout.sql)) shows all parents with more direct leaf children than the threshold for fanout
(determined by variable `models_fanout_threshold`, default 3)
#### Graph Example
`fct_model` has three direct leaf children.
#### Reason to Flag
This might indicate some transformations should move to the BI layer, or a common business transformations
should be moved upstream.
#### Exceptions
Some BI tools are better than others at joining and data exploration. For example, with Looker you could
end your DAG after marts (i.e. fcts & dims) and join those artifacts together (with a little know how
and setup time) to make your reports. For others, like Tableau, model fanouts might be more
beneficial, as this tool prefers big tables over joins, so predefining some reports is usually more performant.
#### How to Remediate
Queries and transformations can move around between dbt and the BI tool, so how do we try to stay
effortful in what we decide to put where?
You can think of dbt as our assembly line which produces expected outputs every time.
You can think of the BI layer as the place where we take the items produced from our assembly line to
customize them in order to meet our stakeholder's needs.
Your dbt project needs a defined end point! Until the metrics server comes to fruition, you cannot possibly
predefine every query or quandary your team might have. So decide as a team where that line is and maintain it.
### Multiple Sources Joined
#### Model
`fct_multiple_sources_joined` ([source](models/marts/dag/fct_multiple_sources_joined.sql)) shows each instance where a model references more than one source.
#### Graph Example
`model_1` references two source tables.
#### Reason to Flag
We very strongly believe that a staging model is the atomic unit of data modeling. Each staging
model bears a one-to-one relationship with the source data table it represents. It has the same
granularity, but the columns have been renamed, recast, or usefully reconsidered into a consistent
format. With that in mind, two `{{ source() }}` declarations in one staging model likely means we are
not being composable enough and there are individual building blocks which could be broken out into
their respective models.
#### Exceptions
NoSQL databases or heavily nested data sources often have so much info json packed into a table
that you need to break one raw data source into multiple base models.
Also, sometimes companies will have a bunch of [identical sources across systems](https://discourse.getdbt.com/t/unioning-identically-structured-data-sources/921) and you union them once before you stage them.
These make total sense, and you should keep them in your project. To continue to test your project, you can
count those instances, then add a [warn_if](https://docs.getdbt.com/reference/resource-configs/severity)
threshold to the test to account for the known examples.
#### How to Remediate
In this example specifically, those raw sources, `source_1.table_1` and `source_1.table_2` should each
have their own staging model (`stg_model_1` and `stg_model_2`), as transitional steps, which will
then be combined into a new `int_model_2`. Alternatively, you could keep `stg_model_2` and add
`base__` models as transitional steps.
To fix this, try out the [codegen](https://hub.getdbt.com/dbt-labs/codegen/latest/) package! With
this package you can dynamically generate the SQL for a staging (what they call base) model, which
you will use to populate `stg_model_1` and `stg_model_2` directly from the source data. Create a
new model `int_model_2`. Afterwards, within `int_model_2`, update your `{{ source() }}` macros to
`{{ ref() }}` macros and point them to your newly built staging models. If you had type casting,
field aliasing, or other simple improvements made in your original `stg_model_2` SQL, then attempt
to move that logic back to the new staging models instead. This will help colocate those
transformations and avoid duplicate code, so that all downstream models can leverage the same
set of transformations.
Post-refactor, your DAG should look like this:
or if you want to use base_ models and keep stg_model_2 as is:
### Rejoining of Upstream Concepts
#### Model
`fct_rejoining_of_upstream_concepts` ([source](models/marts/dag/fct_rejoining_of_upstream_concepts.sql)) contains all cases where one of the parent's direct children
is ALSO the direct child of ANOTHER one of the parent's direct children. Only includes cases
where the model "in between" the parent and child has NO other downstream dependencies.
#### Graph Example
`stg_model_1`, `int_model_4`, and `int_model_5` create a "loop" in the DAG. `int_model_4` has no other downstream dependencies other than `int_model_5`.
#### Reason to Flag
This could happen for a variety of reasons: Accidentally duplicating some business concepts in multiple
data flows, hesitance to touch (and break) someone else’s model, or perhaps trying to snowflake out
or modularize everything without awareness of what will help build time.
As a general rule, snowflaking out models in a thoughtful manner allows for concurrency, but in this
example nothing downstream can run until `int_model_4` finishes, so it is not saving any time in
parallel processing by being its own model. Since both `int_model_4` and `int_model_5` depend solely
on `stg_model_1`, there is likely a better way to write the SQL within one model (`int_model_5`) and
simplify the DAG, potentially at the expense of more rows of SQL within the model.
#### Exceptions
The one major exception to this would be when using a function from
[dbt_utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/) package, such as `star` or `get_column_values`,
(or similar functions / packages) that require a [relation](https://docs.getdbt.com/reference/dbt-classes#relation)
as an argument input. If the shape of the data in the output of `stg_model_1` is not the same as what you
need for the input to the function within `int_model_5`, then you will indeed need `int_model_4` to create
that relation, in which case, leave it.
#### How to Remediate
Barring jinja/macro/relation exceptions we mention directly above, to resolve this, simply bring the SQL contents from `int_model_4` into a CTE within `int_model_5`, and swap all `{{ ref('int_model_4') }}` references to the new CTE(s).
Post-refactor, your DAG should look like this:
### Root Models
#### Model
`fct_root_models` ([source](models/marts/dag/fct_root_models.sql)) shows each model with 0 direct parents, meaning that the model cannot be traced back to a declared source or model in the dbt project.
#### Graph Example
`model_4` has no direct parents
#### Reason to Flag
This likely means that the model (`model_4` below) contains raw table references, either to a raw data source, or another model in the project without using the `{{ source() }}` or `{{ ref() }}` functions, respectively. This means that dbt is unable to interpret the correct lineage of this model, and could result in mis-timed execution and/or circular references depending on the model’s upstream dependencies.
#### How to Remediate
Start by mapping any table references in the `FROM` clause of the model definition to the models or raw tables that they draw from, and replace those references with the `{{ ref() }}` if the dependency is another dbt model, or the `{{ source() }}` function if the table is a raw data source (this may require the declaration of a new source table). Then, visualize this model in the DAG, and refactor as appropriate according to best practices.
#### Exceptions
This behavior may be observed in the case of a manually defined reference table that does not have any dependencies. A good example of this is a `dim_calendar` table that is generated by the `{{ dbt_utils.date_spine() }}` macro — this SQL logic is completely self contained, and does not require any external data sources to execute.
### Source Fanout
#### Model
`fct_source_fanout` ([source](models/marts/dag/fct_source_fanout.sql)) shows each instance where a source is the direct parent of multiple resources in the DAG.
#### Graph Example
`source.table_1` has more than one direct child model.
#### Reason to Flag
Each source node should be referenced by a single model that performs basic operations, such as renaming, recasting, and other light transformations to maintain consistency through out the project. The role of this staging model is to mirror the raw data but align it with project conventions. The staging model should act as a source of truth and a buffer- any model which depends on the data from a given source should reference the cleaned data in the staging model as opposed to referencing the source directly. This approach keeps the code DRY (any light transformations that need to be done on the raw data are performed only once). Minimizing references to the raw data will also make it easier to update the project should the format of the raw data change.
#### How to Remediate
Create a staging model which references the source and cleans the raw data (e.g. renaming, recasting). Any models referencing the source directly should be refactored to point towards the staging model instead.
After refactoring the above example, the DAG would look something like this:
### Staging Models Dependent on Downstream Models
#### Model
`fct_staging_dependent_on_marts_or_intermediate` ([source](models/marts/dag/fct_staging_dependent_on_marts_or_intermediate.sql)) shows each staging model that depends on an intermediate or marts model, as defined by the naming conventions and folder paths specified in your project variables.
#### Graph Example
`stg_model_5`, a staging model, builds from `fct_model_9` a marts model.
#### Reason to Flag
This likely represents a misnamed file. According to dbt best practices, staging models should only
select from source nodes. Dependence on downstream models indicates that this model may need to be either
renamed, or reconfigured to only select from source nodes.
#### How to Remediate
Rename the file in the `child` column to use to appropriate prefix, or change the models lineage
by pointing the staging model to the appropriate `{{ source() }}`.
After updating the model to use the appropriate `{{ source() }}` function, your graph should look like this:
### Staging Models Dependent on Other Staging Models
#### Model
`fct_staging_dependent_on_staging` ([source](models/marts/dag/fct_staging_dependent_on_staging.sql)) shows each parent/child relationship where models in the staging layer are
dependent on each other.
#### Graph Example
`stg_model_2` is a parent of `stg_model_4`.
#### Reason to Flag
This may indicate a change in naming is necessary, or that the child model should instead reference a source.
#### How to Remediate
You should either change the model type of the `child` (maybe to an intermediate or marts model) or change the child's lineage instead reference the appropriate `{{ source() }}`.
In our example, we might realize that `stg_model_4` is _actually_ an intermediate model. We should move this file to the appropriate intermediate directory and update the file name to `int_model_4`.
### Unused Sources
#### Model
`fct_unused_sources` ([source](models/marts/dag/fct_unused_sources.sql)) shows each source with 0 children.
#### Graph Example
`source.table_4` isn't being referenced.
#### Reason to Flag
This represents either a source that you have defined in YML but never brought into a model or a
model that was deprecated and the corresponding rows in the source block of the YML file were
not deleted at the same time. This simply represents the buildup of cruft in the project that
doesn’t need to be there.
#### How to Remediate
Navigate to the `sources.yml` file (or whatever your company has called the file) that corresponds
to the unused source. Within the YML file, remove the unused table name, along with descriptions
or any other nested information.
```yaml
version: 2
sources:
- name: some_source
database: raw
tables:
- name: table_1
- name: table_2
- name: table_3
- name: table_4 # <-- remove this line
```
## Testing
### Models without Primary Key Tests
#### Model
`fct_missing_primary_key_tests` ([source](models/marts/tests/fct_missing_primary_key_tests.sql)) lists every model that does not meet the minimum testing requirement of testing primary keys. Any models that does not have both a `not_null` and `unique` test configured will be highlighted in this model.
#### Reason to Flag
Tests are assertions you make about your models and other resources in your dbt project (e.g. sources, seeds and snapshots). Defining tests is a great way to confirm that your code is working correctly, and helps prevent regressions when your code changes. Models without proper tests on their grain are a risk to the reliability and scalability of your project.
#### How to Remediate
Apply a [uniqueness test](https://docs.getdbt.com/reference/resource-properties/tests#unique) and a [not null test](https://docs.getdbt.com/reference/resource-properties/tests#not_null) to the column that represents the grain of your model in its schema entry. For models that are unique across a combination of columns, we recommend adding a surrogate key column to your model, then applying these tests to that new model. See the [`surrogate_key`](https://github.com/dbt-labs/dbt-utils#surrogate_key-source) macro from dbt_utils for more info!
Additional tests can be configured by applying a [generic test](https://docs.getdbt.com/docs/building-a-dbt-project/tests#generic-tests) in the model's `.yml` entry or by creating a [singular test](https://docs.getdbt.com/docs/building-a-dbt-project/tests#singular-tests)
in the `tests` directory of you project.
### Test Coverage
#### Model
`fct_test_coverage` ([source](models/marts/tests/fct_test_coverage.sql)) contains metrics pertaining to project-wide test coverage.
Specifically, this models measures:
1. `test_coverage_pct`: the percentage of your models that have minimum 1 test applied.
2. `test_to_model_ratio`: the ratio of the number of tests in your dbt project to the number of models in your dbt project
3. `marts_test_coverage_pct`: the percentage of your marts models that have minimum 1 test applied.
This model will raise a `warn` error on a `dbt build` or `dbt test` if the `test_coverage_pct` is less than 100%.
You can set your own threshold by overriding the `test_coverage_target` variable. [See overriding variables section.](#overriding-variables)
#### Reason to Flag
We recommend that every model in your dbt project has tests applied to ensure the accuracy of your data transformations.
#### How to Remediate
Apply a [generic test](https://docs.getdbt.com/docs/building-a-dbt-project/tests#generic-tests) in the model's `.yml` entry, or create a [singular test](https://docs.getdbt.com/docs/building-a-dbt-project/tests#singular-tests)
in the `tests` directory of you project.
As explained above, we recommend [at a minimum](https://www.getdbt.com/analytics-engineering/transformation/data-testing/#what-should-you-test), every model should have `not_null` and `unique` tests set up on a primary key.
## Documentation
### Documentation Coverage
#### Model
`fct_documentation_coverage` ([source](models/marts/documentation/fct_documentation_coverage.sql)) calculates the percent of enabled models in the project that have
a configured description.
This model will raise a `warn` error on a `dbt build` or `dbt test` if the `documentation_coverage_pct` is less than 100%.
You can set your own threshold by overriding the `test_coverage_target` variable. [See overriding variables section.](#overriding-variables)
#### Reason to Flag
Good documentation for your dbt models will help downstream consumers discover and understand the datasets which you curate for them.
The documentation for your project includes model code, a DAG of your project, any tests you've added to a column, and more.
#### How to Remediate
Apply a text [description](https://docs.getdbt.com/docs/building-a-dbt-project/documentation#related-documentation) in the model's `.yml` entry, or create a [docs block](https://docs.getdbt.com/docs/building-a-dbt-project/documentation#using-docs-blocks) in a markdown file, and use the `{{ doc() }}`
function in the model's `.yml` entry.
Tip: We recommend that every model in your dbt project has at minimum a model-level description. This ensures that each model's purpose is clear to other developers and stakeholders when viewing the dbt docs site.
### Undocumented Models
#### Model
`fct_undocumented_models` ([source](models/marts/documentation/fct_undocumented_models.sql)) lists every model with no description configured.
#### Reason to Flag
Good documentation for your dbt models will help downstream consumers discover and understand the datasets which you curate for them.
The documentation for your project includes model code, a DAG of your project, any tests you've added to a column, and more.
#### How to Remediate
Apply a text [description](https://docs.getdbt.com/docs/building-a-dbt-project/documentation) in the model's `.yml` entry, or create a [docs block](https://docs.getdbt.com/docs/building-a-dbt-project/documentation#using-docs-blocks) in a markdown file, and use the `{{ doc() }}`
function in the model's `.yml` entry.
Tip: We recommend that every model in your dbt project has at minimum a model-level description. This ensures that each model's purpose is clear to other developers and stakeholders when viewing the dbt docs site. Missing documentation should be addressed first for marts models, then for the rest of your project, to ensure that stakeholders in the organization can understand the data which is surfaced to them.
## Structure
### Model Naming Conventions
#### Model
`fct_model_naming_conventions` ([source](models/marts/structure/fct_model_naming_conventions.sql)) shows all cases where a model does NOT have the appropriate prefix.
#### Reason to Flag
Without appropriate naming conventions, a user querying the data warehouse might incorrectly assume the model type of a given relation. In order to explicitly name
the model type in the data warehouse, we recommend appropriately prefixing your models in dbt.
| Model Type | Appropriate Prefixes |
| ------------ | -------------------- |
| Staging | `stg_` |
| Intermediate | `int_` |
| Marts | `fct_` or `dim_` |
| Other | `rpt_` |
#### How to Remediate
For each model flagged, ensure the model type is defined and the model name is prefixed appropriately.
#### Example
Consider `model_8` which is nested in the `marts` subdirectory:
```
├── dbt_project.yml
└── models
├── marts
└── model_8.sql
```
This model should be renamed to either `fct_model_8` or `dim_model_8`.
### Model Directories
#### Model
`fct_model_directories` ([source](models/marts/structure/fct_model_directories.sql)) shows all cases where a model is NOT in the appropriate subdirectory:
- For staging models: The files should be nested in the staging folder of a subfolder that matches their source parent's name.
- For non-staging models: The files should be nested closest to the folder name that matches their model type.
#### Reason to Flag
Because we often work with multiple data sources, in our staging directory, we create one subdirectory per source.
```
├── dbt_project.yml
└── models
├── marts
└── staging
├── braintree
└── stripe
```
Each staging directory contains:
- One staging model for each raw source table
- One .yml file which contains source definitions, tests, and documentation (see [Source Directories](#source-directories))
- One .yml file which contains tests & documentation for models in the same directory (see [Test Directories](#test-directories))
This provides for clear repository organization, so that analytics engineers can quickly and easily find the information they need.
We might create additional folders for intermediate models but each file should always be nested closest to the folder name that matches their model type.
```
├── dbt_project.yml
└── models
└── marts
└── fct_model_6.sql
└── intermediate
└── int_model_5.sql
```
#### How to Remediate
For each resource flagged, move the file from the `current_file_path` to `change_file_path_to`.
#### Example
Consider `stg_model_3` which is a staging model for `source_2.table_3`:
But, `stg_model_3.sql` is inappropriately nested in the subdirectory `source_1`:
```
├── dbt_project.yml
└── models
├── marts
└── staging
└── source_1
├── stg_model_3.sql
```
This file should be moved into the subdirectory `source_2`:
```
├── dbt_project.yml
└── models
├── marts
└── staging
├── source_1
└── source_2
├── stg_model_3.sql
```
Consider `dim_model_7` which is a marts model but is inappropriately nested closest to the subdirectory `intermediate`:
```
├── dbt_project.yml
└── models
└── marts
└── intermediate
├── dim_model_7.sql
```
This file should be moved closest to the subdirectory `marts`:
```
├── dbt_project.yml
└── models
└── marts
├── dim_model_7.sql
```
Consider `int_model_4` which is an intermediate model but is inappropriately nested closest to the subdirectory `marts`:
```
├── dbt_project.yml
└── models
└── marts
├── int_model_4.sql
```
This file should be moved closest to the subdirectory `intermediate`:
```
├── dbt_project.yml
└── models
└── marts
└── intermediate
├── int_model_4.sql
```
## Structure
### Model Naming Conventions
#### Model
`fct_model_naming_conventions` ([source](models/marts/structure/fct_model_naming_conventions.sql)) shows all cases where a model does NOT have the appropriate prefix.
#### Reason to Flag
Without appropriate naming conventions, a user querying the data warehouse might incorrectly assume the model type of a given relation. In order to explicitly name
the model type in the data warehouse, we recommend appropriately prefixing your models in dbt.
| Model Type | Appropriate Prefixes |
| ------------ | -------------------- |
| Staging | `stg_` |
| Intermediate | `int_` |
| Marts | `fct_` or `dim_` |
| Other | `rpt_` |
#### How to Remediate
For each model flagged, ensure the model type is defined and the model name is prefixed appropriately.
#### Example
Consider `model_8` which is nested in the `marts` subdirectory:
```
├── dbt_project.yml
└── models
├── marts
└── model_8.sql
```
This model should be renamed to either `fct_model_8` or `dim_model_8`.
### Source Directories
#### Model
`fct_source_directories` ([source](models/marts/structure/fct_source_directories.sql)) shows all cases where a source definition is NOT in the appropriate subdirectory:
#### Reason to Flag
Because we often work with multiple data sources, in our staging directory, we create one subdirectory per source.
```
├── dbt_project.yml
└── models
├── marts
└── staging
├── braintree
└── stripe
```
Each staging directory contains:
- One staging model for each raw source table (see [Model Directories](#source-directories))
- One .yml file which contains source definitions, tests, and documentation
- One .yml file which contains tests & documentation for models in the same directory (see [Test Directories](#test-directories))
This provides for clear repository organization, so that analytics engineers can quickly and easily find the information they need.
#### How to Remediate
For each source flagged, move the file from the `current_file_path` to `change_file_path_to`.
#### Example
Consider `source_2.table_3` which is a `source_2` source but it had been defined inappropriately in a `source.yml` file nested in the subdirectory `source_1`:
```
├── dbt_project.yml
└── models
├── marts
└── staging
└── source_1
├── source.yml
```
This definition should be moved into a `source.yml` file nested in the subdirectory `source_2`:
```
├── dbt_project.yml
└── models
├── marts
└── staging
├── source_1
└── source_2
├── source.yml
```
### Test Directories
#### Model
`fct_test_directories` ([source](models/marts/structure/fct_test_directories.sql)) shows all cases where model tests are NOT in the same subdirectory as the corresponding model.
#### Reason to Flag
Each subdirectory in `models/` should contain one .yml file that includes the tests and documentation for all models within the given subdirectory. Keeping your repository organized in this way ensures that folks can quickly access the information they need.
#### How to Remediate
Move flagged tests from the yml file under `current_test_directory` to the yml file under `change_test_directory_to` (create a new yml file if one does not exist).
#### Example
`int_model_4` is located within `marts/`. However, tests for `int_model_4` are configured in `staging/staging.yml`:
```
├── dbt_project.yml
└── models
└── marts
├── int_model_4.sql
└── staging
├── staging.yml
```
A new yml file should be created in `marts/` which contains all tests and documentation for `int_model_4`, and for the rest of the models in located in the `marts/` directory:
```
├── dbt_project.yml
└── models
└── marts
├── int_model_4.sql
├── marts.yml
└── staging
├── staging.yml
```
-----
## Customization
### Disabling models
If there is a particular model or set of models that you *do not want this package to execute*, you can
disable these models as you would any other model in your `dbt_project.yml` file
```yml
# dbt_project.yml
models:
dbt_project_evaluator:
tests:
# disable entire test coverage suite
+enabled: false
dag:
# disable single DAG model
fct_model_fanout:
+enabled: false
```
### Overriding Variables
Currently, this package uses different variables to adapt the models to your objectives and naming conventions. They can all be updated directly in `dbt_project.yml`
- tests and docs coverage variables
- `test_coverage_pct` can be updated to set a test coverage percentage (default 100% coverage)
- `documentation_coverage_pct` can be updated to set a documentation coverage percentage (default 100% coverage)
```yml
# dbt_project.yml
# set your test and doc coverage to 75% instead
vars:
dbt_project_evaluator:
documentation_coverage_target: 75
test_coverage_target: 75
```
- naming conventions variables
- the `model_types` variable is used to configure the different layers of your dbt Project. In conjunction with the variables `_folder_name` and `_prefixes`, it allows the package to check if models in the different layers are in the correct folders and have a correct prefix in their name. The default model types are the ones we recommend in our [dbt Labs Style Guide](https://github.com/dbt-labs/corp/blob/main/dbt_style_guide.md). If your model types are different, you can update this variable and create new variables for `_folder_name` and/or `_prefixes`
- all the `_folder_name` variables are used to parameterize the name of the folders for the model types of your DAG. Each variable must be a string.
- all the `_prefixes` variables are used to parameterize the prefixes of your models for the model types of your DAG. Each parameter contains the list of prefixes that are allowed according to your naming conventions.
- warehouse specific variables
- `max_depth_bigquery` is only referred to with BigQuery as the Warehouse and is used to limit the number of nested CTEs when computing the DAG end to end. Changing this number to a higher one might prevent the package from running properly on BigQuery
----
## Querying the DAG with SQL
The model `int_all_dag_relationships` ([source](models/marts/core/int_all_dag_relationships.sql)), created with the package, lists all the dbt nodes (models, exposures, sources, metrics, seeds, snapshots) along with all their dependencies (including indirect ones) and the path between them.
Building additional models and snapshots on top of this model could allow:
- creating a dashboard that provides
- a list of all the sources used by a given exposure
- a list of all the exposures or metrics using a given source
- the dependencies between different models
- building metrics/KPIs on top of a dbt project
- evolution of the number of models over time
- evolution of the number of metrics and exposures over time
- getting insights on potential refactoring work
- looking at the longest "chains" of models in a project
- reviewing models with many/few direct dependents
- identifying potential bottlenecks
----
## Limitations
### BigQuery
BigQuery current support for recursive CTEs is limited.
For BigQuery, the model `int_all_dag_relationships` needs to be created by looping CTEs instead. The number of loops is defaulted to 9, which means that dependencies between models of more than 9 levels of separation won't show in the model `int_all_dag_relationships` but tests on the DAG will still be correct. With a number of loops higher than 9 BigQuery sometimes raises an error saying the query is too complex.
----
## Contributing
If you'd like to add models to flag new areas, please update this README and add an integration test
([more details here](https://github.com/dbt-labs/pro-serv-dag-auditing/tree/main/integration_tests#adding-an-integration-test)).