Awesome

Data Quality Tools

The purpose of the dq tool is to make simple storing test results and visualisation of these in a BI dashboard.

Supported DWHs:

Installation:

Add to packages.yml file:

packages:
  - package: infinitelambda/dq_tools
    version: [">=1.8.0", "<1.9.0"]

(Optional) Configure schema in dbt_project.yml file:

models:
  dq_tools:
    # +database: DQ
    +schema: AUDIT
    
vars: # (TO BE DEPRECATED, currently used only in test macros)
  # dbt_dq_tool_database: DQ
  dbt_dq_tool_schema: AUDIT

Add on-run-end hook:

on-run-end:
  - '{{ dq_tools.store_test_results(results) }}'

Initialize dq-tools (new in v1.4+)
```
dbt run -s dq_tools 
```

Then, decide to save the test result in dbt command:

dbt test --vars '{dq_tools_enable_store_test_results: True}'
dbt build --vars '{dq_tools_enable_store_test_results: True}'

Alternatively, we can also enable this variable in <code>dbt_project.yml`</code>:

vars:
  # to store the test results in db table
  dq_tools_enable_store_test_results: True

See Installation Instructions in more details.

3 Functional layers

store dbt test results in a table
create mart for DQ test result
provide BI dashboard for visualisation

The idea behind the layer is that each layer can be changed, extended or replaced without or with minimal impact on the other 2.

Data Quality KPIs

There are 6 main KPIs will be produced as below:

Accuracy
Consistency
Completeness
Timeliness
Validity
Uniqueness

DataQualityKPIs

NOTE: It is possible that we can have custom KPI(s) as you go but it is NOT recommended as the existing modelling design will stick to the above 6 ones only.

models:
  - name: my_model
    columns:
      - name: my_column
        data_tests:
          - dq_tools.unique_where_db:
              kpi_category: MyKPI # not recommended

Quick Demo

STEP 1 - Installation:
- install dq tools package
- create dq log issue table following the documentation in the package.
- create metrics views
- set up looker dashboard

STEP 2 - define dbt data_tests: Define tests following the description in the package documentation.

models:
- name: dim_customers
  description: This table has basic information about a customer, as well as some derived facts based on a customer's orders
  data_tests:
    - dq_tools.equal_rowcount_where_db:
        compare_model: ref('stg_customers')
        where: customer_id > 50
        compare_model_where: customer_id > 50
  columns:
    - name: customer_id
      description: This is a unique identifier for a customer
      data_tests:
        - dq_tools.unique_where_db
        - dq_tools.not_null_where_db

STEP 3 - run the dbt test and check: Test results in the dq issue log table:

Data quality KPIs in looker:

Installation Instructions

Go to dbt Hub and register the package into your dbt packages.yml file:

packages:
  - package: infinitelambda/dq_tools
    version: [">=1.2.0", "<1.3.0"]

1. Create table DQ_ISSUE_LOG in the database

Since the version 1.3, the table dq_issue_log is made as dbt model, no more manual hook config :tada:.

It should be created automatically within your upstream dbt command. If not, all you should do that is running the command: dbt run -s dq_tools.

[!NOTE] If you already have a metricflow_time_spine model in your project, you will encounter a Compilation error when executing dbt run -s dq_tools. In this scenario, disable the metricflow_time_spine model by updating its configuration in your project (path: your_project/dbt_packages/dq_tools/models/04_metric/metricflow_time_spine).

<details> <summary>For dq-tools legacy version >=1.0,<1.3</summary>

A macro create_table_dq_issue_log (source) will create the log table in your database (Snowflake) / project (BigQuery).

Add on-run-start hook (required dbt >= 1.0.0):

on-run-start:
  - '{{ dq_tools.create_table_dq_issue_log() }}'

</details> <details> <summary>For dq-tools legacy version < 1.0, you can run it as an operation</summary>

dbt run-operation create_dq_issue_log

</details>

2. Configure your DQ schema with `dbt_dq_tool_schema` variable:

Value for variable dbt_dq_tool_schema: your_schema_name needs to be added to dbt_project.yml file in your project. And then, optionally add dbt_dq_tool_database: your_database_name which default value is target.schema in profiles.yml file

e.g.

vars:
  # (optional) to create db table in the schema named as AUDIT, default to `target.schema`
  dbt_dq_tool_schema: AUDIT
  # (optional) to create db table in the database named as DQ_TOOLS, default to `target.database`
  dbt_dq_tool_database: DQ_TOOLS

3. Decide to save test result to Data Warehouse table:

With `dq_tools_enable_store_test_results` variable:

Add the on-run-end hook to you project:

on-run-end:
  - '{{ dq_tools.store_test_results(results) }}'

Then, decide to save the test result in dbt command:

dbt test --vars '{dq_tools_enable_store_test_results: True}'
dbt build --vars '{dq_tools_enable_store_test_results: True}'

<details> <summary>Alternatively, we can also enable this variable in <code>dbt_project.yml`</code>:</summary>

vars:
  # to store the test results in db table
  dq_tools_enable_store_test_results: True

</details>

Pros & Cons:

Pros:
- Save both type of tests (singular and generic) result to log table
- Save test result from any test functions (outside of dq-tools ones)
Cons
- Only availabe on the latest version
- Singular Test: table_name / ref_table: cannot be captured
- Singular Test: no_of_records cannot be captured

<details> <summary>For dq_tools version < 1.0 (with legacy variable) </summary>

Enabling test result storing by `dbt_test_results_to_db` variable

Optionally, add dbt_test_results_to_db: False as a variable to your dbt_profile.yml file. Its default value is False meaning NOT to save test result.

e.g.

vars:
  # to store the test results in db table
  dbt_test_results_to_db: False

You can also specify the variable in dbt commands e.g.

dbt test -s your_model vars '{dbt_test_results_to_db: True}'
dbt build -s your_model vars '{dbt_test_results_to_db: True}'

Additionally, you MUST know that when you generate the doc or compile the code, this variable dbt_test_results_to_db is super important. If it's defined as True, it will run the test when generating the documentation or compiling. (indeed generating the doc compile the code first see: Generating project documentation).

So you should either pass the variable when generating the doc and compiling the code.

dbt docs generate --vars  'dbt_test_results_to_db: False'
dbt compile --vars  'dbt_test_results_to_db: False'

Either defined it to false in the default variables and defined the variables when running the test.

dbt test --vars  'dbt_test_results_to_db: False'

Pros & Cons:

Pros:
- Automatically save generic test result (if you used dq-tools functions)
Cons:
- Requires to create new test function(s) in advanced case(s) to adapt with current implementation of test result capturing approach
- Singular test functions is not documented (?)

</details>

4. Decide to enable building the downstream models of the log table

Since the version 1.4+, all models and metrics will be enabled by default.

<details> <summary>For dq-tools version <1.4</summary>

Enable it in dbt_project.yml file:

# dbt_project.yml
models:
  dq_tools:
    +enabled: true

metrics:
  dq_tools:
    +enabled: true

</details>

Macros

on-run-end Context

store_test_results (source)

This macro is used to parse results variable at the on-run-end context to achieve the test result nodes, and save them to the DQ_ISSUE_LOG table. If the model is materialized as ephemeral, this macro will insert the null value for aggregated fields related to tested model.

Usage:

# dbt_project.yml
on-run-end:
  - '{{ dq_tools.store_test_results(results) }}'

Besides, there are couple of private macros are used as a part of it aiming to extract/calculate things under (here)

Generic Tests

These tests are based on dbt_utils test. The test result will be stored in a database table and further analysis can be built on these.

Detailed informations will be stored such as check_timestamp, table_name, column_name, value, severity, no_of records etc.

not_null_where_db (source)

This test validates that there are no null values present in a column for a subset of rows by specifying a where clause.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Kpi_category option allows you to change the default category, which this test will fall into in the looker dq_mart dashboard. Accepted values are: [Accuracy, Consistency, Completeness, Timeliness, Validity, Uniqueness]. Any other value will fall into Other. Default option for this test is Completeness.

Usage:

version: 2

models:
  - name: my_model
    columns:
      - name: id
        data_tests:
          - dq_tools.not_null_where_db:
              where: "_deleted = false"
              severity_level: error
              kpi_category: Completeness

relationships_where_db (source)

This test validates the referential integrity between two relations (same as the core relationships schema test) with an added predicate to filter out some rows from the test. This is useful to exclude records such as test entities, rows created in the last X minutes/hours to account for temporary gaps due to ETL limitations, etc.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Usage:

version: 2

models:
  - name: model_name
    columns:
      - name: id
        data_tests:
          - dq_tools.relationships_where_db:
              to: ref('other_model_name')
              field: client_id
              from_condition: id <> '4ca448b8-24bf-4b88-96c6-b1609499c38b'
              severity_level: warn
              kpi_category: Consistency

unique_where_db (source)

This test validates that there are no duplicate values present in a field for a subset of rows by specifying a where clause.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Usage:

version: 2

models:
  - name: my_model
    columns:
      - name: id
        data_tests:
          - dq_tools.unique_where_db:
              where: "_deleted = false"
              severity_level: error
              kpi_category: Uniqueness

recency_db (source)

This schema test asserts that there is data in the referenced model at least as recent as the defined interval prior to the current timestamp.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Usage:

version: 2

models:
  - name: model_name
    data_tests:
      - dq_tools.recency_db:
          datepart: day
          field: created_at
          interval: 1
          severity_level: warn
          kpi_category: Timeliness

expression_is_true_db (source)

This schema test asserts that a valid sql expression is true for all records. This is useful when checking integrity across columns, for example, that a total is equal to the sum of its parts, or that at least one column is true.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Usage:

version: 2

models:
  - name: model_name
    data_tests:
      - dq_tools.expression_is_true_db:
          expression: "col_a + col_b = total"
          kpi_category: Validity

accepted_values_where_db (source)

This schema test asserts that all of the column values are within the list of accepted values provided. As with other schema tests, optional parameter where can be specified for testing just a subset of the column.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Usage:

version: 2

models:
  - name: model_name
    data_tests:
      - dq_tools.accepted_values_where_db:
          values: [value1, value2]
          severity_level: warn
          kpi_category: Accuracy

equal_rowcount_where_db (source)

This schema test asserts that count of rows in two relations is the same. Optional parameters where and compare_model_where can be specified for testing just a subset of base and compared relations respectively.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Usage:

version: 2

models:
  - name: model_name
    data_tests:
      - dq_tools.equal_rowcount_where_db:
          compare_model: some_other_model
          where: "_deleted = false"
          compare_model_where: "_deleted = false"
          severity_level: warn

equality_where_db (source)

This schema test asserts that two relations (or subset of their columns) are equal. Relations as a whole are considered if the parameter compare_columns is not provided. Optional parameters where and compare_model_where can be specified for testing just a subset of base and compared relations respectively.

All data quality issues are stored in the dq_issues_log table.

If not specified the default severity level is 'warn'. This option coresponds with dbts severity setting.

Usage:

version: 2

models:
  - name: model_name
    data_tests:
      - dq_tools.equality_where_db:
          compare_model: some_other_model
          compare_columns:
            - column1
            - column2
          where: "_deleted = false"
          compare_model_where: "_deleted = false"
          severity_level: warn

Contributing

If you've ever wanted to contribute to this tool, and a great cause, feel free to create your Pull Request, or submit a new issue via Bug Report / Feature Request 💖

Awesome

Data Quality Tools

3 Functional layers

Data Quality KPIs

Quick Demo

Installation Instructions

1. Create table DQ_ISSUE_LOG in the database

2. Configure your DQ schema with dbt_dq_tool_schema variable:

3. Decide to save test result to Data Warehouse table:

With dq_tools_enable_store_test_results variable:

Enabling test result storing by dbt_test_results_to_db variable

4. Decide to enable building the downstream models of the log table

Macros

on-run-end Context

store_test_results (source)

Generic Tests

not_null_where_db (source)

relationships_where_db (source)

unique_where_db (source)

recency_db (source)

expression_is_true_db (source)

accepted_values_where_db (source)

equal_rowcount_where_db (source)

equality_where_db (source)

Contributing

2. Configure your DQ schema with `dbt_dq_tool_schema` variable:

With `dq_tools_enable_store_test_results` variable:

Enabling test result storing by `dbt_test_results_to_db` variable