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  • Writer's pictureWilson Vaquen


Updated: Apr 26, 2022

In this post we will talk about dbt_dataquality, a new dbt package developed by us at Divergent Insights and contributed to the dbt community

This dbt package helps you to

If you are a visual learner feel free to checkout our Loom video

Also, here's a live demo dashboard built on Power BI that shows you the value of the dbt_dataquality package


  • This package is compatible with dbt 1.0.0 and later

  • This packages uses Snowflake as the backend for reporting (contributions to support other backend engines are welcomed)

  • Snowflake credentials with the right level of access to create/destroy and configure the following objects:

  • Database (optional)

  • Schema (optional)

  • Internal stage (recommended but optional). Alternatively, you can use an external stage

  • Table

Source Code

At Divergent Insights, we love Open Source and the source code of this package is available in GitHub and the package is licensed under Apache License 2.0

High Level Architecture

Architecture Overview

As per the high-level architecture diagram, these are the different functionalities that this package provides:

1. (Optional) Creation of Snowflake resources to store and make available dbt logging information

  • Create a database (optional) - this is only provided for convenience and very unlikely to be required by you

  • Creates a schema (optional)

  • Creates an internal stage (optional)

  • Creates a variant-column staging table

2. Loads dbt logging information on an internal stage

  • This is achieved via a set of dbt macros together leveraging Snowflake PUT command

3. Copies dbt logging information into a Snowflake table

  • This is achieved via a set of dbt macros together leveraging Sowflake COPY command

4. Creating and populating simple dbt models to report on dbt source freshness and dbt tests

  • Raw logging data is modelled downstream and contextualised for reporting purposes

5. Bonus - it provides a ready-to-go Power BI dashboard built on top the dbt models created by the package to showcase all features


Package Configuration

Optionally, set any relevant variables in your dbt_project.yml

    dbt_dataquality_database: my_database # optional, default is target.database
    dbt_dataquality_schema: my_schema # optional, default is target.schema
    dbt_dataquality_table: my_table # optional, default is 'stg_dbt_dataquality'
    dbt_dataquality_stage: my_internal_stage | my_external_stage, default is 'dbt_dataquality'),
    dbt_dataquality_target_path: my_dbt_target_directory # optional, default is 'target'

Important: when using an external stage you need to set the parameter load_from_internal_stage to False on the load_log_ macros. See below for more details

Resources Creation

Use the macro "create_resources" to create the backend resources required by the package

  • If you have the right permissions, you should be able to run this macro to create all resources required by the dbt_dataquality package

  • For example, a successful run of "dbt run-operation create_resources" will give you the schema, table and staging tables required by the package

If you are in a complex environment with stringent permissions, you can run the macro in "dry mode" which will give you the SQL required by the macro. Once you have the SQL you can copy and paste and run manually the parts of the query that make sense

  • For example, "dbt run-operation create_resources --args '{dry_run:True}'"

Also, keep in mind that the "create_resources" macro creates an internal stage by default. If you are wanting to load log files via an external stage then you can disable the creation of the internal stage

  • For example, "dbt run-operation create_resources --args '{internal_stage:False}'"

Generating some log files

Optionally, do a regular run of dbt source freshness or dbt test on your local project to generate some logging files

  • For example "dbt run" or "dbt test"

Loading log files - Internal Stage

Use the load macros provided by the dbt_quality package to load the dbt logging information that's required

  • Use the macro "load_log_sources" to load sources.json and manifest.json files

  • Use the macro "load_log_tests" to load run_results.json and manifest.json files

Note that the "load_log_sources" and "load_log_tests" macros automatically upload the relevant log and manifest files

  • For example, the macro "load_log_sources" loads sources.json and manifest.json and the macro "load_log_tests" loads the files run_results.json and manifest.json

Loading log files - External Stage

To load data from an external stage, you must:

  • Workout on your own how to create, configure and load the data to the external stage

  • In this case, when running the "create_resources" macro set the parameter "create_internal_stage" to "False"

  • For example: "dbt run-operation create_resources --args '{create_internal_stage: False}'"

  • Set the package variable "dbt_dataquality_stage: my_external_stage" (as described at the beginning of the Usage section)

  • When running the "load_log_sources" and "load_log_tests" macros set the parameter "load_from_internal_stage" to "False"

  • For example: "dbt run-operation load_log_sources --args '{load_from_internal_stage: False}'"

Create and populate downstream models

  • Use "dbt run --select dbt_quality.sources" to load source freshness logs

  • Use "dbt run --select dbt_quality.tests" to load tests logs

Data Quality Attributes

This package supports capturing and reporting on Data Quality Attributes. This is a very popular feature!

To use this functionality just follow these simple steps:

Add tests to your models

Just add tests to your models following the standard dbt testing process

Tip: you may want to use some tests from the awesome dbt package dbt-expectations

Tag your tests

Tag any tests that you want to report on with your preferred data quality attributes

To keep things simple at Divergent Insights we use the ISO/IEC 25012:2008 standard to report on data quality (refer to the image below)

You can read more about ISO 25012 here; however, here's a summary of the key Data Quality Attributes defined by the standard:

  • Accuracy: the degree to which data has attributes that correctly represent the true value of the intended attribute of a concept or event in a specific context of use

  • Completeness: the degree to which subject data associated with an entity has values for all expected attributes and related entity instances in a specific context of use

  • Consistency: the degree to which data has attributes that are free from contradiction and are coherent with other data in a specific context of use. It can be either or both among data regarding one entity and across similar data for comparable entities

  • Credibility: the degree to which data has attributes that are regarded as true and believable by users in a specific context of use. Credibility includes the concept of authenticity (the truthfulness of origins, attributions, commitments)

  • Currentness / Timeliness: the degree to which data has attributes that are of the right age in a specific context of use.

Please note that

  • Tags MUST be prefixed with "dq:", for example "dq:accuracy" or "dq:timeliness"

  • Any tag prefixed with "dq:" will be automatically detected and reported on by the package

  • In our case, we use four tags aligned to ISO 25012: "dq:accuracy", "dq:completeness", "dq:consistency" and "dq:timeliness" (we don't use "credibility" due to obvious reasons)

  • If you add two or more "dq:" tags, only the first tag sorted alphabetically is processed

Here's an example on how to tag your models to use this functionality

version: 2

  - name: my_table1
      - dbt_expectations.expect_table_column_count_to_be_between:
          min_value: 1
          max_value: 4
      - name: col1
          - unique:
              tags: ['dq:consistency']
          - not_null:
              tags: ['dq:consistency']
      - name: col2
          - not_null:
              tags: ['dq:timeliness','mytag']

  - name: my_table2
      - name: col1
          - unique:
              tags: ['dq:accuracy','other-tag']
          - not_null
      - name: col2
          - not_null:
              tags: ['dq:completeness']

Usage Summary

Here's all the steps put together:

dbt run-operation create_resources

dbt source freshness
dbt run-operation load_log_sources
dbt run --select dbt_dataquality.sources

dbt test
dbt run-operation load_log_tests
dbt run --select dbt_dataquality.tests

# Optionally, the dbt_dataquality package uses incremental models so don't forget to use the option `--full-refresh` to rebuild them
# For example
dbt run --full-refresh --select dbt_dataquality.sources
dbt run --full-refresh --select dbt_dataquality.tests

Dashboarding Data Quality Information

  • The models created will allow you to dome some simple but powerful reporting on your data quality (see images below)

  • This package includes a nice and simple Power BI sample dashboard to get you going!

Live Demo Dashboard

Sources Overview Dashboard

Tests Overview Dashboard

Data Quality Attributes


  • Adding testing suite

  • Adding more complex downstream metrics on Data Quality Coverage

  • When the time is right, adding support for old and new [dbt artifacts schema versions](, currently on v3 is supported


This work is licensed under the Apache License 2.0

This is a permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.

About Divergent Insights

We are your data team as a service! We are a consulting and professional services partner of dbt Labs and Fivetran

We are a passionate and eclectic team determined to supercharge the most ambitious and innovative data solutions in Australia and New Zealand. We provide consultancy and professional services that give you the technical expertise, hands-on experience and thought leadership to deliver innovative data solutions.

We follow the ideology of "everything as code" and provide innovative data solutions that apply classic and modern software engineering principles to the data space such as Continuous Integration and Infrastructure as Code.

Let's chat https;//

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