Modelling and Transformation Orchestrator

Type	Pre-Set
Image	$DATAOPS_TRANSFORM_RUNNER_IMAGE

The Modeling and Transformation (MATE) orchestrator is a pre-set orchestrator. It is responsible for taking the models in the /dataops/modelling directory and executing them in a Snowflake Data Warehouse by compiling them to SQL and then running the resultant SQL statements.

Multiple operations are possible within the Modelling and Transformation Engine (MATE). To trigger the selected operation within MATE, set the parameter TRANSFORM_ACTION to one of the supported values.

Usage

You must always use the Modelling and Transformation orchestrator with the DataOps Reference Project, providing, among others, the .modelling_and_transformation_base job.

pipelines/includes/local_includes/mate_jobs/build_all_models.yml

"Build all Models":
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  stage: "Data Transformation"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: RUN
  script:
    - /dataops
   icon: ${TRANSFORM_ICON}

Supported parameters

note

When you want to apply a transformation action to all jobs in a pipeline, you must define the relevant parameter at the project level, i.e., in the pipelines/includes/config/variables.yml file. If you set the parameter in the job definition, it will only apply to the specific job.

Parameter	Required/Default	Description
TRANSFORM_ACTION	REQUIRED	Must be one of BUILD, RUN, COMPILE, SNAPSHOT, DOCS, TEST, OPERATION, RENDER, or SEED
DATABASE_PROFILE	REQUIRED. Defaults to snowflake_operations	Which dbt profile to use from profiles.template.yml
TRANSFORM_PROJECT_PATH	REQUIRED. Defaults to $CI_PROJECT_DIR/templates/modelling	The directory in the project structure where the base of the Modelling and Transformation project is located
TRANSFORM_OPERATION_NAME	REQUIRED—if TRANSFORM_ACTION is set to OPERATION	The macro/operation to be executed
DATABASE_TARGET	REQUIRED. Defaults to other	Which dbt profile target to use from profiles.template.yml
DATAOPS_FEATURE_MATE_DBT_VERSION	Optional. Defaults to 1.4	Specifies the dbt version with which to run MATE. See Switching dbt version for more information.
TRANSFORM_DEBUG_TIMING	Optional. Defaults to blank	If set, it saves performance profiling information to a file timing.log in the TRANSFORM_PROJECT_PATH directory. To view the report use the tool snakeviz by using the command snakeviz timing.log
TRANSFORM_EXTRA_PARAMS_AFTER	Optional. Defaults to blank	Additional command-line arguments to be added to the end of the transform orchestrator process
TRANSFORM_EXTRA_PARAMS_BEFORE	Optional. Defaults to blank	Additional command-line arguments to be added to the beginning of the transform orchestrator process
TRANSFORM_VARS	Optional. Defaults to blank	Key-value pairs to be added when running models. See the section Adding/Overriding variables to dbt at run-time for usage.
TRANSFORM_FORCE_DEPS	Optional. Defaults to blank	Decides whether to force a refresh of external transformation libraries before execution. Note: When using additional packages in your project, and not only in a few selected jobs in your pipeline, you must define this parameter at the project level, i.e., in the variables: section at pipelines/includes/config/variables.yml. Everything you define in this file will be available to all jobs.
TRANSFORM_MODEL_INDIRECT_SELECTION_MODE	Optional. Defaults to blank	dbt indirectly selects all tests if they touch any resource you select directly, and this is called "eager" indirect selection. If you don't want to include tests selected indirectly, set the value of this variable to cautious. See Exclude tests selected indirectly for more information.
TRANSFORM_MODEL_SELECTOR	Optional. Defaults to blank	If set, it specifies what model to run in the project following the defined model name or the name of the model-level tag, e.g., 'person' or 'tag:curation'
TRANSFORM_YAML_SELECTOR	Optional. Defaults to blank	If set, it executes models following what is defined in resource selectors in YAML. See the Resource selectors in YAML for more information.
TRANSFORM_OPERATION_ARGS	Optional. Defaults to {}	The YAML string representing the macro arguments, e.g. {arg1: value1, arg2: 345}
TRANSFORM_PARTIAL_PARSE	Optional. Defaults to blank	If set, it disables partial parsing in the project. See the section on partial parsing for more information
FULL_REFRESH	Optional. Defaults to blank	If set, it will force incremental models to be fully refreshed
DATAOPS_REMOVE_RENDERED_TEMPLATES	Optional. Defaults to blank	If set, the system will remove any templates found after processing. This allows files of the format <modelname>.template.yml to be used without creating extra models in the project. Note: Make sure to set this variable in the variables.yml file in the project settings and not within your job.

The following topics bear reference to and expand on several of these supported parameters:

Using TRANSFORM_ACTION: BUILD

Prerequisite (dbt 1.4 or later)

MATE with dbt adds a new TRANSFORM_ACTION: BUILD, which runs the following four commands in a single job:

• RUN
• TEST
• SNAPSHOT
• SEED

It follows the Directed Acyclic Graph (DAG) order for selected resources or the entire project. The BUILD action allows you to combine jobs spread across multiple stages into one.

select dbt version

Make sure you have set the dbt version with which to use MATE. See Switching dbt versions.

The following example shows how to run BUILD for all models, snapshots, and seeds tagged with finance:

pipelines/includes/local_includes/mate_jobs/build_tagged_models.yml

"Build all Models":
  extends:
    - .modeling_and_transformation_base
    - .agent_tag
  stage: "Data Transformation"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: BUILD
    TRANSFORM_MODEL_SELECTOR: tag:finance
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

Testing reporting

The test reporting feature is broken further down into the following categories:

• Enable test reporting
• Test states
• Test report control

1. Enable test reporting

The Transform orchestrator generates a test report when running a TEST job. To surface this report into the DataOps platform, the job must include an artifact around it, as in lines 11 to 14 in the following example:

pipelines/includes/local_includes/mate_jobs/my_test_job.yml

"My Test Job":
  extends:
    - .modeling_and_transformation_base
    - .agent_tag
  stage: "Transformation Testing"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: TEST
  script:
    - /dataops
  artifacts:
    when: always
    reports:
      junit: $CI_PROJECT_DIR/report.xml
  icon: ${TRANSFORM_ICON}

Once the job has run successfully, the job will add the test results to the pipeline's Tests tab.

2. Test states

It is possible to categorize the test result or state test in one of the following ways:

State	Description	Example
PASS	The test executed and passed	The test is successful. The target fulfilled its specified condition, such as the correct number of rows present in a database table
FAIL	The test executed and failed	The test failed. The target did not meet its specified condition, such as the incorrect number of rows present in a table
ERROR	Error while executing a test	An error occurred while executing a test, such as an invalid column/table name specified in a table/dataset
SKIPPED	The test skipped due to an unfulfilled condition	The test did not execute because of an unfilled condition in a dataset/table

3. Test report control

The following parameters control the generation of test reports:

Parameter	Required/Optional	Description
JSON_PATH	Optional. Defaults to $TRANSFORM_PROJECT_PATH/target/run_results.json	The path to the JSON result generated by the Transform orchestrator
REPORT_NAME	Optional. Defaults to report.xml	The generated report name
REPORT_DIR	Optional. Defaults to $CI_PROJECT_DIR	The path where the generated report is saved. Note: This directory must already exist before these tests run
TREAT_TEST_ERRORS_AS_FAILED	Optional. Defaults to FALSE	If enabled, it reports a test error as FAIL. See Test state for more information
TREAT_TEST_WARNS_AS_FAILED	Optional. Defaults to FALSE	If enabled, it reports a test warning as FAIL. See Test state for more information

Partial parse

Partial parsing can improve the performance characteristics of DataOps pipeline runs by limiting the number of files a pipeline must parse every time it runs. Here, "parsing" means reading files in a project from disk and capturing ref() and config() method calls. These method calls are used to determine the following:

• The shape of the dbt DAG (Direct Acyclic Graph)
• The supplied resource configurations

There is no need to re-parse these files if partial parsing is enabled and the files are unchanged between job requests. The Transform orchestrator can use the parsed representation from the last job request. However, if a file has changed between invocations, then the orchestrator will re-parse the file and update the parsed node cache accordingly.

TRANSFORM_PARTIAL_PARSE is enabled by default. To disable this feature, set its value to 1.

To utilize partial parsing in a DataOps project, enable caching in the .modeling_and_transformation_base job by overriding the settings from the reference project base job by creating a definition in your project as follows:

pipelines/includes/local_includes/mate_jobs/base_job_modelling_and_transformation.yml

.modeling_and_transformation_base:
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    DATAOPS_TEMPLATES_DIR: /tmp/local_config
    DATAOPS_SECONDARY_TEMPLATES_DIR: $CI_PROJECT_DIR/dataops/modeling
  cache:
    key: $CI_PIPELINE_ID
    paths:
      - dataops/modeling/target/
  icon: ${TRANSFORM_ICON}

Switching dbt version

Prerequisite (dbt 1.4 or later)

You can select the dbt version you want to use to run MATE actions using the variable DATAOPS_FEATURE_MATE_DBT_VERSION. This variable automatically supports the latest dbt minor release. The supported versions include bundled packages as well.

Currently, by default, MATE uses the dbt version 1.0. We highly recommend selecting a later version, as 1.0 is no longer supported by dbt Labs. To select a later, supported version of dbt, specify the version in the parameter DATAOPS_FEATURE_MATE_DBT_VERSION, e.g. 1.4.

Installing specific or unsupported dbt versions at runtime is also possible. Then you can set their values in the variable to use them with MATE. See the below topic for more details.

Using a specific or unsupported version

You can specify a dbt version currently not supported by DataOps, such as 1.3, or a specific version of dbt-core, such as 1.4.3, in the variable DATAOPS_FEATURE_MATE_DBT_VERSION.

If you set an unsupported version, MATE will attempt to install the required dbt-core packages for the specified value. Use at your own risk.

Bundled Packages

Bundled packages such as dataops, dbtvault, etc., are unavailable if an unsupported version of dbt is specified.

In such case, you must set the required package in the file dataops/modelling/packages.yml and enable the variable TRANSFORM_FORCE_DEPS. Read more about this package installation at Installing additional packages.

Use the following formats to install new dbt versions:

Value	Installed dbt-core Version	Installed dbt-snowflake Version
1.3	Highest available version for 1.3 (>=1.3 and <1.4)	Highest available version for 1.3 (>=1.3 and <1.4)
1.4.3	1.4.3	Highest available version for 1.4 (>=1.4 and <1.5)

Installation of Pre-release version

Switching dbt version in MATE does not support installing pre-release versions of dbt.

Defining a custom profiles.template.yml

You can customize profiles.template.yml in the /dataops/profiles project directory to be used by the MATE orchestrator. If no profiles.template.yml is defined in /dataops/profiles, then a default one will be used.

note

The rendered file /dataops/profiles/profiles.yml will be removed after the /dataops script call to prevent exposing credentials. You can set the parameter PREVENT_KEY_PAIR_CLEANUP to 1 to prevent the removal. Doing this helps avoid cleaning up key-pair files (if in use).

caution

The /dataops/profiles/profiles.yml contains all the details required to connect to your Snowflake account, including sensitive information like credentials.

One can set up DATABASE_PROFILE and DATABASE_TARGET to execute macro with higher privilege. See Running a macro using the SOLE Admin role

The default profiles.template.yml

The default profiles.template.yml is provided below. It defines the default DATABASE_PROFILE named snowflake_operations and the default DATABASE_TARGET named other.

/dataops/profiles/profiles.template.yml

snowflake_operations:
  target: other
  outputs:
    other:
      type: snowflake
      account: {{ SNOWFLAKE.ACCOUNT }}
      user: {{ SNOWFLAKE.TRANSFORM.USERNAME }}
      role: {{ SNOWFLAKE.TRANSFORM.ROLE }}
      {% if env.DATAOPS_SNOWFLAKE_AUTH %}
        {% if env.DATAOPS_SNOWFLAKE_AUTH == "KEY_PAIR" %}
      private_key_path: {{ env.DATAOPS_SNOWFLAKE_KEY_PAIR_PATH }}
          {% if env.DATAOPS_SNOWFLAKE_PASSPHRASE %}
      private_key_passphrase: {{ env.DATAOPS_SNOWFLAKE_PASSPHRASE }}
          {% endif %}
        {% else %}
      password: {{ SNOWFLAKE.TRANSFORM.PASSWORD }}
        {% endif %}
      {% else %}
      password: {{ SNOWFLAKE.TRANSFORM.PASSWORD }}
      {% endif %}
      database: {{ env.DATABASE }}
      schema: BASESCHEMA
      warehouse: {{ SNOWFLAKE.TRANSFORM.WAREHOUSE }}
      threads: {{ SNOWFLAKE.TRANSFORM.THREADS }}
      client_session_keep_alive: False
    qa:
      type: snowflake
      account: {{ SNOWFLAKE.ACCOUNT }}
      user: {{ SNOWFLAKE.TRANSFORM.USERNAME }}
      role: {{ SNOWFLAKE.TRANSFORM.ROLE }}
      {% if env.DATAOPS_SNOWFLAKE_AUTH %}
        {% if env.DATAOPS_SNOWFLAKE_AUTH == "KEY_PAIR" %}
      private_key_path: {{ env.DATAOPS_SNOWFLAKE_KEY_PAIR_PATH }}
          {% if env.DATAOPS_SNOWFLAKE_PASSPHRASE %}
      private_key_passphrase: {{ env.DATAOPS_SNOWFLAKE_PASSPHRASE }}
          {% endif %}
        {% else %}
      password: {{ SNOWFLAKE.TRANSFORM.PASSWORD }}
        {% endif %}
      {% else %}
      password: {{ SNOWFLAKE.TRANSFORM.PASSWORD }}
      {% endif %}
      database: {{ env.DATABASE }}
      schema: BASESCHEMA
      warehouse: {{ SNOWFLAKE.TRANSFORM.WAREHOUSE }}
      threads: {{ SNOWFLAKE.TRANSFORM.THREADS }}
      client_session_keep_alive: False
    prod:
      type: snowflake
      account: {{ SNOWFLAKE.ACCOUNT }}
      user: {{ SNOWFLAKE.TRANSFORM.USERNAME }}
      role: {{ SNOWFLAKE.TRANSFORM.ROLE }}
      {% if env.DATAOPS_SNOWFLAKE_AUTH %}
        {% if env.DATAOPS_SNOWFLAKE_AUTH == "KEY_PAIR" %}
      private_key_path: {{ env.DATAOPS_SNOWFLAKE_KEY_PAIR_PATH }}
          {% if env.DATAOPS_SNOWFLAKE_PASSPHRASE %}
      private_key_passphrase: {{ env.DATAOPS_SNOWFLAKE_PASSPHRASE }}
          {% endif %}
        {% else %}
      password: {{ SNOWFLAKE.TRANSFORM.PASSWORD }}
        {% endif %}
      {% else %}
      password: {{ SNOWFLAKE.TRANSFORM.PASSWORD }}
      {% endif %}
      database: {{ env.DATABASE }}
      schema: BASESCHEMA
      warehouse: {{ SNOWFLAKE.TRANSFORM.WAREHOUSE }}
      threads: {{ SNOWFLAKE.TRANSFORM.THREADS }}
      client_session_keep_alive: False

snowflake_ingestion:
  outputs:
    default:
      type: snowflake
      account: {{ SNOWFLAKE.ACCOUNT }}
      user: {{ SNOWFLAKE.INGESTION.USERNAME }}
      role: {{ SNOWFLAKE.INGESTION.ROLE }}
      {% if env.DATAOPS_SNOWFLAKE_AUTH %}
        {% if env.DATAOPS_SNOWFLAKE_AUTH == "KEY_PAIR" %}
      private_key_path: {{ env.DATAOPS_SNOWFLAKE_KEY_PAIR_PATH }}
          {% if env.DATAOPS_SNOWFLAKE_PASSPHRASE %}
      private_key_passphrase: {{ env.DATAOPS_SNOWFLAKE_PASSPHRASE }}
          {% endif %}
        {% else %}
      password: {{ SNOWFLAKE.INGESTION.PASSWORD }}
        {% endif %}
      {% else %}
      password: {{ SNOWFLAKE.INGESTION.PASSWORD }}
      {% endif %}
      database: {{ DATABASE }}
      schema: BASESCHEMA
      warehouse: {{ SNOWFLAKE.INGESTION.WAREHOUSE }}
      threads: {{ SNOWFLAKE.INGESTION.THREADS }}
      client_session_keep_alive: False


snowflake_master:
  outputs:
    default:
      type: snowflake
      account: {{ SNOWFLAKE.ACCOUNT }}
      user: {{ SNOWFLAKE.MASTER.USERNAME }}
      role: {{ SNOWFLAKE.MASTER.ROLE }}
      {% if env.DATAOPS_SNOWFLAKE_AUTH %}
        {% if env.DATAOPS_SNOWFLAKE_AUTH == "KEY_PAIR" %}
      private_key_path: {{ env.DATAOPS_SNOWFLAKE_KEY_PAIR_PATH }}
          {% if env.DATAOPS_SNOWFLAKE_PASSPHRASE %}
      private_key_passphrase: {{ env.DATAOPS_SNOWFLAKE_PASSPHRASE }}
          {% endif %}
        {% else %}
      password: {{ SNOWFLAKE.MASTER.PASSWORD }}
        {% endif %}
      {% else %}
      password: {{ SNOWFLAKE.MASTER.PASSWORD }}
      {% endif %}
      database: {{ env.DATABASE }}
      schema: BASESCHEMA
      warehouse: {{ SNOWFLAKE.TRANSFORM.WAREHOUSE }}
      threads: {{ SNOWFLAKE.TRANSFORM.THREADS }}
      client_session_keep_alive: False

config:
  send_anonymous_usage_stats: False

Migrating to the /dataops/profiles directory

If you had customized the profiles.template.yml in the DataOps Runner host directory /app, you need to copy the content to the new project git directory /dataops/profiles. To leverage key pair authentication, start from the default profiles.template.yml and merge your modifications. In most cases, using the default file is sufficient.

If no profile file named profiles.template.yml is present in the project directory /dataops/profiles then the default will be used.

Authentication

Key pair authentication

MATE orchestrator supports using key pair authentication. To know more and how to configure it, see Key Pair Authentication.

Example jobs

The examples below extend the base job .modeling_and_transformation_base to simplify the MATE job definition. See the reference project base job for all details about these examples.

For ease of reading, the examples below are summarized as follows:

1. Build All Models

2. Build a Directory of Models

3. Build Tagged Models

4. Test all Models

5. Build Models by Running a Macro

6. Run a Macro using the SOLE Admin Role

7. Build Selected Models

8. [Test Indirect selection Models][test-indirect-selection-models]

1. Build all models

Build all the models in your project:

pipelines/includes/local_includes/mate_jobs/build_all_models.yml

"Build all Models":
  extends:
    - .modeling_and_transformation_base
    - .agent_tag
  stage: "Data Transformation"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: RUN
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

2. Build a directory of models

Build all the models in the divisions/finance directory:

pipelines/includes/local_includes/mate_jobs/build_all_models.yml

"Build all Models":
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  stage: "Data Transformation"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: RUN
    TRANSFORM_MODEL_SELECTOR: models/divisions/finance
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

3. Build tagged models

Build all the models tagged finance:

pipelines/includes/local_includes/mate_jobs/build_all_models.yml

"Build all Models":
  extends:
    - .modeling_and_transformation_base
    - .agent_tag
  stage: "Data Transformation"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: RUN
    TRANSFORM_MODEL_SELECTOR: tag:finance
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

4. Test all models

The TRANSFORM_MODEL_SELECTOR variable works the same way with TEST as it does with RUN.

pipelines/includes/local_includes/mate_jobs/test_all_models.yml

"Test all Models":
  extends:
    - .modeling_and_transformation_base
    - .agent_tag
  stage: "Transformation Testing"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: TEST
  script:
    - /dataops
  artifacts:
    when: always
    reports:
      junit: $CI_PROJECT_DIR/report.xml
  icon: ${TESTING_ICON}

5. Build models by running a macro

Rather than building/testing all or part of the MATE models, a MATE job can also execute a standalone macro as its primary operation.

pipelines/includes/local_includes/mate_jobs/my_mate_job.yml

Run My Macro:
  extends:
    - .modeling_and_transformation_base
    - .agent_tag
  stage: "Additional Configuration"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: OPERATION
    TRANSFORM_OPERATION_NAME: my_macro
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

6. Run a macro using the SOLE Admin role

Setting DATABASE_PROFILE and DATABASE_TARGET to the values snowflake_master and default, respectively (as per the example below) will execute the macro using the higher privileges that SOLE uses.

pipelines/includes/local_includes/mate_jobs/my_mate_job.yml

"Run My Macro as Admin":
  extends:
    - .modeling_and_transformation_base
    - .agent_tag
  stage: "Additional Configuration"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: OPERATION
    TRANSFORM_OPERATION_NAME: my_macro
    DATABASE_PROFILE: snowflake_master
    DATABASE_TARGET: default
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

7. Resource selectors in YAML

Prerequisite (dbt 1.4 or later)

Build models according to what you set in the resource selector in YAML, which allows you to write resource selector configurations in a human-readable and reusable format:

Full YAML

dataops/modelling/selectors.yml

selectors:
  - name: business_vault_tests
    description: "Non-incremental Snowplow models that power nightly exports"
    definition:
      union:
        - method: tag
          value: datavault_business_vault
        - exclude:
            - union:
                - method: tag
                  value: datavault_raw
                - method: tag
                  value: datavault_stage

CLI-style

dataops/modelling/selectors.yml

selectors:
  - name: business_vault_tests
    description: "Non-incremental Snowplow models that power nightly exports"
    definition:
      union:
        - "tag:datavault_business_vault"
        - exclude:
            - union:
                - "tag:datavault_raw"
                - "tag:datavault_stage"

Set the name of the selector in TRANSFORM_YAML_SELECTOR:

pipelines/includes/local_includes/mate_jobs/my_mate_job.yml

Run My Macro:
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  stage: "Additional Configuration"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: RUN
    TRANSFORM_YAML_SELECTOR: business_vault_tests
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

8. Exclude tests selected indirectly

You can select tests directly by methods and operators that capture one of the tests' attributes, names, properties, tags, etc. But dbt also indirectly selects all tests that touch any resource you select. See the below example for more information.

pipelines/includes/local_includes/mate_jobs/test_direct_selection_model.yml

"Test Direct Selection Model":
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  stage: "Transformation Testing"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: TEST
    TRANSFORM_MODEL_SELECTOR: tag:direct
    TRANSFORM_MODEL_INDIRECT_SELECTION_MODE: cautious
  script:
    - /dataops
  artifacts:
    when: always
    reports:
      junit: $CI_PROJECT_DIR/report.xml
  icon: ${TESTING_ICON}

Test against a direct tagged parent model:

dataops/modelling/models/example1/direct_selection_model.yml

version: 2
models:
  - name: direct_selection_model
    description: "dbt model"
    columns:
      - name: id
        description: "The primary key for this table"
        tests:
          - unique

Test against an indirect tagged child model (referenced from the direct tagged model):

While testing a direct tagged model, if you set the value of the TRANSFORM_MODEL_INDIRECT_SELECTION_MODE variable to "cautious" in the pipeline, it will not include the below test for the child model.

dataops/modelling/models/example2/indirect_selection_model.yml

version: 2
models:
  - name: indirect_selection_model
    tests:
      - dataops.same_rows_as:
          compare_model: ref('direct_selection_model')
    description: >
      Some very interesting stuff

For more details about indirect selection, see test selection examples.

9. Adding/Overriding variables to dbt at runtime

Prerequisite (dbt 1.4 or later)

You can specify variables with a dynamic or run-time value using the parameters TRANSFORM_VARS. If set, the value is added to dbt command-line arguments as --vars.

TRANSFORM_VARS as string

pipelines/includes/local_includes/mate_jobs/my_mate_job.yml

Run My Macro:
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  stage: "Additional Configuration"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: RUN
    TRANSFORM_VARS: "{ var_one: Value one, var_two: 2 }"
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}

TRANSFORM_VARS as dictionary

pipelines/includes/local_includes/mate_jobs/my_mate_job.yml

Run My Macro:
  extends:
    - .modelling_and_transformation_base
    - .agent_tag
  stage: "Additional Configuration"
  image: $DATAOPS_TRANSFORM_RUNNER_IMAGE
  variables:
    TRANSFORM_ACTION: RUN
    TRANSFORM_VARS: >-
      { var_one: Value one, var_two: 2 }
  script:
    - /dataops
  icon: ${TRANSFORM_ICON}