Load Qualtrics data to Microsoft SQL Server

Qualtrics is an experience-management platform whose v3 REST API provides access to surveys, survey responses, and related account resources. Everything needed to build a working Qualtrics → Microsoft SQL Server pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Qualtrics to Microsoft SQL Server pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Qualtrics API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Qualtrics API at a glance

These values come from the Qualtrics API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Qualtrics API?

Send the Qualtrics API token in the X-API-TOKEN request header, for example X-API-TOKEN: yourapitokenhere. For POST or PUT requests with JSON bodies, also send Content-Type: application/json; the token is an opaque account-generated value and Qualtrics documentation does not specify a required prefix or format.

1. Get your credentials

1. Sign in to Qualtrics. 2. Open Account Settings (click your name/profile menu). 3. Open the API or Qualtrics IDs section; the exact navigation may vary with the redesigned account settings. 4. Select Generate API Token and securely copy the token. 5. Configure requests to send the token in the HTTPS header X-API-TOKEN; use your survey account's datacenter-specific base URL, such as https://{datacenter-id}.qualtrics.com/API/v3. Each user must generate their own token. If Generate API Token is unavailable or disabled, the account likely lacks the API/Offline App feature and an administrator must enable or purchase access. Regenerating the token invalidates the previous token, so rotate dependent integrations immediately.

2. Add them to .dlt/secrets.toml

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[sources.qualtrics_source]
api_key = "your_qualtrics_api_token"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Qualtrics data can I load into Microsoft SQL Server?

These are the Qualtrics endpoints dlt can load into Microsoft SQL Server:

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How do I load only new Qualtrics records?

Qualtrics exposes lastModified on surveys, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "surveys", "endpoint": {
    "path": "surveys",
    "data_selector": "result.elements",
    "incremental": {"cursor_path": "lastModified", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Qualtrics pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading surveys and responseexports from the Qualtrics API into Microsoft SQL Server:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def qualtrics_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{datacenter_id}.qualtrics.com/API/v3",
            "auth": {"type": "api_key", "api_key": api_token, "name": "X-API-TOKEN", "location": "header"},
        },
        "resources": [
            {"name": "surveys", "endpoint": {"path": "surveys", "data_selector": "result.elements"}},
            {"name": "response_exports", "endpoint": {"path": "responseexports", "method": "POST"}}
        ],
    }
    yield from rest_api_resources(config)


def load_qualtrics_to_mssql() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="qualtrics_pipeline",
        destination="mssql",
        dataset_name="qualtrics_data",
    )
    load_info = pipeline.run(qualtrics_source())
    print(load_info)


if __name__ == "__main__":
    load_qualtrics_to_mssql()

Run it with uv run python qualtrics_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Qualtrics data in Microsoft SQL Server?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("qualtrics_pipeline").dataset()
df = data.surveys.df()
print(df.head())

SQL:

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SELECT * FROM qualtrics_data.surveys LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Qualtrics to Microsoft SQL Server pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Qualtrics loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Qualtrics data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack