Qualtrics Transformation dbt Package (Docs)

What does this dbt package do?

This package models Qualtrics data from Fivetran's connector. It uses data in the format described by this ERD and builds off the output of our Qualtrics source package.

The main focus of the package is to transform the core object tables into analytics-ready models, including:

• A Response breakdown model which consolidates all survey responses joined with users, questions, and survey details.
• Overview models for Surveys, Contacts, Directories, and Distributions which help to understand the nuances of each and how they affect key survey aggregations.
• A daily breakdown model which provides a high level view of a variety of Qualtrics account metrics at a daily level.

The following table provides a detailed list of all tables materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

How do I use the dbt package?

Step 1: Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran Qualtrics connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL destination.

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following (or a variation of the following) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Step 2: Install the package

Include the following qualtrics package version in your packages.yml file:

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/qualtrics
    version: [">=0.2.0", "<0.3.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the qualtrics_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Step 3: Define database and schema variables

Single connector

By default, this package runs using your destination and the qualtrics schema. If this is not where your qualtrics data is (for example, if your qualtrics schema is named qualtrics_fivetran), add the following configuration to your root dbt_project.yml file:

# dbt_project.yml

vars:
    qualtrics_database: your_database_name
    qualtrics_schema: your_schema_name

Union multiple connectors

If you have multiple Qualtrics connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the qualtrics_union_schemas OR qualtrics_union_databases variables (cannot do both) in your root dbt_project.yml file:

# dbt_project.yml

vars:
    qualtrics_union_schemas: ['qualtrics_usa','qualtrics_canada'] # use this if the data is in different schemas/datasets of the same database/project
    qualtrics_union_databases: ['qualtrics_usa','qualtrics_canada'] # use this if the data is in different databases/projects but uses the same schema name

NOTE: The native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.

To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.

Step 4: Enable Research Core Contacts API

By default, this package does not bring in data from the Qualtrics Research Core Contacts Endpoint, as this API is set to be deprecated by Qualtrics. However, if you would like the package to bring in Core contacts and mailing lists in addition to XM Directory data, add the following configuration to your dbt_project.yml:

vars:
    qualtrics__using_core_contacts: True # default = False
    qualtrics__using_core_mailing_lists: True # default = False

(Optional) Step 5: Additional configurations

Passing Through Additional Fields

This package includes all source columns defined in the macros folder. You can add more columns using our pass-through column variables. These variables allow for the pass-through fields to be aliased (alias) and casted (transform_sql) if desired, but not required. Datatype casting is configured via a sql snippet within the transform_sql key. You may add the desired sql while omitting the as field_name at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables:

# dbt_project.yml

vars:
  qualtrics__survey_pass_through_columns:
    - name: "that_field"
      alias: "renamed_to_this_field"
      transform_sql: "cast(renamed_to_this_field as string)"
  qualtrics__directory_pass_through_columns:
    - name: "this_field"
  qualtrics__directory_contact_pass_through_columns:
    - name: "old_name"
      alias: "new_name"
  qualtrics__distribution_pass_through_columns:
    - name: "unique_string_field"
      transform_sql: "cast(unique_string_field as string)"
  qualtrics__core_contact_pass_through_columns: # relevant only if you have `core_*` tables enabled
    - name: "pass_this_through"

Please create an issue if you'd like to see passthrough column support for other tables in the Qualtrics schema.

Changing the Build Schema

By default this package will build the Qualtrics staging models within a schema titled (<target_schema> + _stg_qualtrics) and the qualtrics final models within a schema titled (<target_schema> + _qualtrics) in your target database. If this is not where you would like your modeled qualtrics data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

models:
  qualtrics:
    +schema: my_new_schema_name # leave blank for just the target_schema
  qualtrics_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

# dbt_project.yml

vars:
    qualtrics_<default_source_table_name>_identifier: your_table_name 

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/qualtrics_source
      version: [">=0.2.0", "<0.3.0"]

    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Check out this dbt Discourse article on the best workflow for contributing to a package.

Are there any resources available?

• If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.