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Loading Qualtrics Data to Dremio Using Python and dlt

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Loading data from Qualtrics to Dremio can be efficiently managed using the open-source Python library called dlt. Qualtrics is a cloud-based survey platform that allows users to create, distribute, and analyze surveys. Dremio is a data lakehouse solution that offers flexibility, scalability, and performance for data management. By leveraging dlt, you can streamline the process of transferring survey data from Qualtrics to Dremio, ensuring data consistency and integrity. This documentation will guide you through the steps required to set up and execute this data pipeline. For more information on Qualtrics, visit here.

dlt Key Features​

  • Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more
  • Run it where Python runs: On Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Learn more
  • User-friendly, declarative interface: Removes knowledge obstacles for beginners while empowering senior professionals. Learn more
  • Scalability via iterators, chunking, and parallelization: Efficient processing of large datasets by breaking them down into manageable chunks and leveraging parallel processing. Learn more
  • Implicit extraction DAGs: Automatically generates an extraction DAG based on the dependencies identified between the data sources and their transformations. Learn more

Getting started with your pipeline locally​

OpenAPI Source Generator dlt-init-openapi

This walkthrough makes use of the dlt-init-openapi generator cli tool. You can read more about it here. The code generated by this tool uses the dlt rest_api verified source, docs for this are here.

0. Prerequisites​

dlt and dlt-init-openapi requires Python 3.9 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt and dlt-init-openapi​

First you need to install the dlt-init-openapi cli tool.

pip install dlt-init-openapi

The dlt-init-openapi cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.

# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you 
# can just hit Enter and all will be rendered.
dlt-init-openapi qualtrics --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/qualtrics.yaml --global-limit 2
cd qualtrics_pipeline
# install generated requirements
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

dlt>=0.4.12

You now have the following folder structure in your project:

qualtrics_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── rest_api/                # The rest api verified source
│   └── ...
├── qualtrics/                
│   └── __init__.py          # TODO: possibly tweak this file
├── qualtrics_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

1.1. Tweak qualtrics/__init__.py​

This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api source set up in a different way. The generated file for the qualtrics source will look like this:

Click to view full file (97 lines)

from typing import List

import dlt
from dlt.extract.source import DltResource
from rest_api import rest_api_source
from rest_api.typing import RESTAPIConfig


@dlt.source(name="qualtrics_source", max_table_nesting=2)
def qualtrics_source(
        api_key: str = dlt.secrets.value,
        base_url: str = dlt.config.value,
) -> List[DltResource]:

    # source configuration
    source_config: RESTAPIConfig = {
        "client": {
            "base_url": base_url,
            "auth": {
                
            "type": "api_key",
            "api_key": api_key,
            "name": "X-API-TOKEN",
            "location": "header"

            },
        },
        "resources": 
        [
        # Gets all distributions for a given survey
        {
            "name": "distribution",
            "table_name": "distribution",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "result.elements",
                "path": "/distributions",
                "params": {
                    "surveyId": "FILL_ME_IN", # TODO: fill in required query parameter

                },
                "paginator": "auto",
            }
        },
        # Get event subscriptions
        {
            "name": "event_subscriptions_response",
            "table_name": "event_subscriptions_response",
            "endpoint": {
                "data_selector": "$",
                "path": "/eventsubscriptions/{SubscriptionId}",
                "params": {
                    "SubscriptionId": "FILL_ME_IN", # TODO: fill in required path parameter

                },
                "paginator": "auto",
            }
        },
        # Retrieves all the individual links for a given distribution
        {
            "name": "link",
            "table_name": "link",
            "endpoint": {
                "data_selector": "result.elements",
                "path": "/distributions/{DistributionId}/links",
                "params": {
                    "DistributionId": {
                        "type": "resolve",
                        "resource": "distribution",
                        "field": "id",
                    },
                    "surveyId": "FILL_ME_IN", # TODO: fill in required query parameter

                },
                "paginator": "auto",
            }
        },
        # Gets a single Qualtrics survey speficied by its ID
        {
            "name": "survey_response",
            "table_name": "survey_response",
            "endpoint": {
                "data_selector": "$",
                "path": "/survey-definitions/{SurveyId}",
                "params": {
                    "SurveyId": "FILL_ME_IN", # TODO: fill in required path parameter

                },
                "paginator": "auto",
            }
        },
        ]
    }

    return rest_api_source(source_config)

2. Configuring your source and destination credentials​

info

dlt-init-openapi will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml.

  • If you know your API needs authentication, but none was detected, you can learn more about adding authentication to the rest_api here.
  • OAuth detection currently is not supported, but you can supply your own authentication mechanism as outlined here.

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml​


[runtime]
log_level="INFO"

[sources.qualtrics]
# Base URL for the API
base_url = "https://fra1.qualtrics.com/API/v3"

generated secrets.toml​


[sources.qualtrics]
# secrets for your qualtrics source
api_key = "FILL ME OUT"  # TODO: fill in your credentials

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations

At this time, the dlt-init-openapi cli tool will always create pipelines that load to a local duckdb instance. Switching to a different destination is trivial, all you need to do is change the destination parameter in qualtrics_pipeline.py to dremio and supply the credentials as outlined in the destination doc linked below.

  • Read more about setting up the rest_api source in our docs.
  • Read more about setting up the Dremio destination in our docs.

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at qualtrics_pipeline.py, as well as a folder qualtrics that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:


import dlt

from qualtrics import qualtrics_source


if __name__ == "__main__":
    pipeline = dlt.pipeline(
        pipeline_name="qualtrics_pipeline",
        destination='duckdb',
        dataset_name="qualtrics_data",
        progress="log",
        export_schema_path="schemas/export"
    )
    source = qualtrics_source()
    info = pipeline.run(source)
    print(info)

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python qualtrics_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline qualtrics_pipeline     info

You can also use streamlit to inspect the contents of your Dremio destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline qualtrics_pipeline     show

5. Next steps to get your pipeline running in production​

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to​

  • Deploy with GitHub Actions: Learn how to set up a CI/CD pipeline using GitHub Actions to automate your deployments. Follow the guide here.
  • Deploy with Airflow: Use Google Composer to manage your Airflow environment and deploy your pipeline. Detailed instructions can be found here.
  • Deploy with Google Cloud Functions: Discover how to deploy your pipeline using Google Cloud Functions for a serverless approach. Step-by-step guide available here.
  • Explore other deployment options: For more deployment methods and detailed walkthroughs, visit the comprehensive guide here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth operations and quick identification of issues. How to Monitor your pipeline
  • Set up alerts: Configure alerts to get notified about important events and potential issues in your dlt pipeline. Set up alerts
  • And set up tracing: Implement tracing to get detailed insights into the performance and behavior of your dlt pipeline. And set up tracing

Available Sources and Resources​

For this verified source the following sources and resources are available

Source Qualtrics​

Collects survey responses, distribution data, and event subscription details from Qualtrics.

Resource NameWrite DispositionDescription
distributionappendDetails about the distribution of surveys to respondents
linkappendInformation about the links generated for survey distribution
survey_responseappendResponses collected from the distributed surveys
event_subscriptions_responseappendData related to event subscriptions and their responses

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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