Qualtrics dbt Package

This dbt package transforms data from Fivetran's Qualtrics connector into analytics-ready tables.

Resources

• Number of materialized models¹: 44
• Connector documentation
  • Qualtrics connector documentation
  • Qualtrics ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog
• dbt Core™ supported versions
  • >=1.3.0, <3.0.0

What does this dbt package do?

This package enables you to transform core object tables into analytics-ready models and consolidate survey responses with user, question, and survey details. It creates enriched models with metrics focused on surveys, contacts, directories, and distributions.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_qualtrics

Final output tables

By default, this package materializes the following final tables:

Table	Description
qualtrics__contact	Detailed view of all contacts (from both the XM Directory and Research Core contact endpoints), enhanced with response and mailing list metrics. Example Analytics Questions: Which contacts have the highest survey response and completion rates? How do engagement metrics differ between different contact cohorts? Which contacts have been sent surveys but never responded?
qualtrics__daily_breakdown	Provides a daily summary of survey activity including survey sends, responses, and distribution performance to monitor day-to-day engagement and identify trends. Example Analytics Questions: Are there optimal days or times when survey completion rates spike? How does response behavior change during weekdays versus weekends? Which contact segments show consistent engagement versus sporadic activity patterns?
qualtrics__directory	Manages contact directories with metrics on total contacts, survey distributions sent, and engagement rates to organize audiences and optimize contact list management. Example Analytics Questions: Which directories are becoming oversaturated with unresponsive contacts? Where should we focus re-engagement campaigns based on recent activity drops? Are there directories with high contact volume but low survey participation?
qualtrics__distribution	Monitors survey distribution campaigns including send methods, recipient counts, and response metrics to optimize distribution strategies and timing. Example Analytics Questions: Do email campaigns consistently outperform SMS for specific audience segments? What is the optimal wait time between survey send and follow-up reminders? Which campaigns show high open rates but low completion rates?
qualtrics__response	Provides detailed question-level response data including answers to individual questions and sub-questions, enriched with survey context to analyze response patterns and answer distributions. Example Analytics Questions: Are there questions that consistently cause respondents to abandon the survey? Which embedded data fields correlate with specific answer choices? Do certain question types (matrix, multiple choice, text) show different completion behaviors?
qualtrics__survey	Tracks survey-level metrics including response counts, question counts, distribution details, and survey status to monitor survey performance and response rates. Example Analytics Questions: Is there a survey length threshold where completion rates significantly drop off? Which surveys show strong initial engagement but poor follow-through? How do anonymous survey responses compare to identified distribution channels?

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

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Prerequisites

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

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

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

• To add the package in the Fivetran dashboard, follow our Quickstart guide.
• To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

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: [">=1.3.0", "<1.4.0"] # we recommend using ranges to capture non-breaking changes automatically

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/qualtrics_source in your packages.yml since this package has been deprecated.

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']

Define database and schema variables

Option A: Single connection

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:

vars:
    qualtrics_database: your_destination_name
    qualtrics_schema: your_schema_name

Option B: Union multiple connections

If you have multiple Qualtrics connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the qualtrics_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  qualtrics:
    qualtrics_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

Previous versions of this package employed two separate, mutually exclusive variables for unioning: qualtrics_union_schemas and qualtrics_union_databases. While these variables are still supported, qualtrics_sources is the recommended variable to configure.

Optional: Incorporate unioned sources into DAG

If you use Fivetran Transformations for dbt Core™ and are unioning multiple Qualtrics connections, you can define your sources in a property .yml file, using this as a template. Set the variable has_defined_sources: true under the Qualtrics namespace in your dbt_project.yml. Otherwise, your Qualtrics connections won't appear in your DAG. See the union_connections macro documentation for full configuration details.

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) Additional configurations

Disable XM Directory contacts and mailing list memberships

By default, this package includes data from directory_contact and its child table contact_mailing_list_membership. If you do not have either of these tables or want to exclude them, add the following to your dbt_project.yml.

vars:
    qualtrics__using_directory_contacts: false # default = true
    qualtrics__using_contact_mailing_list_memberships: false # default = true

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-through 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> + _qualtrics_source) 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 +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default 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. This config is available only when running the package on a single connection.

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 

Source casing for case-sensitive destinations

By default, the package applies case-insensitive comparisons when resolving source_relation values. If your destination is case-sensitive and you want downstream transformations to respect the exact casing of your source database and schema names, set the following variable:

vars:
    fivetran_using_source_casing: true

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

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core setup guides.

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/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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

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.