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Loading Qualtrics Data to Azure Cloud Storage with dlt in Python

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Qualtrics is a cloud-based survey platform that allows users to build surveys, distribute them, and analyze the results. This documentation explains how to load data from Qualtrics to Azure Cloud Storage using the open-source Python library dlt. Azure Cloud Storage is a filesystem destination that stores data on Microsoft Azure, making it easy to create data lakes. You can upload data in formats such as JSONL, Parquet, or CSV. This guide will walk you through the steps to achieve seamless data integration. For more information on Qualtrics, visit here.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about lineage.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more: Adjust a schema docs.
  • Scalability via Iterators, Chunking, and Parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Read more about performance.
  • Schema Evolution: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, such as table or column alterations, dlt notifies stakeholders, allowing them to take necessary actions, such as reviewing and validating the changes, updating downstream processes, or performing impact analysis.
  • Advanced Deployment Options: dlt supports various advanced deployment options, including deploying from branches, local folders, or git repositories. This flexibility allows users to tailor their deployment strategies according to their specific needs. Learn more about using dlt init with branches, local folders, or git repos.

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 filesystem 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 Azure Cloud Storage destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to Azure Cloud Storage, update the [destination.filesystem.credentials] section in your secrets.toml.

[destination.filesystem.credentials]
azure_storage_account_name="Please set me up!"
azure_storage_account_key="Please set me up!"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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 Azure Cloud Storage 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 and deploy your dlt pipeline using GitHub Actions for CI/CD. Read more
  • Deploy with Airflow and Google Composer: A guide to deploying your dlt pipeline using Airflow managed by Google Composer. Read more
  • Deploy with Google Cloud Functions: Step-by-step instructions to deploy your dlt pipeline using Google Cloud Functions. Read more
  • Other Deployment Methods: Explore various other methods to deploy your dlt pipeline. Read more

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 and efficient operation. Read more
  • Set up alerts: Set up alerts for your pipeline to get notified about any issues or important events that require your attention. Read more
  • Set up tracing: Implement tracing in your dlt pipeline to gain insights into the execution flow and performance. Read more

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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