InfinityQS Python API Docs | dltHub

InfinityQS Enact is a cloud-based quality management platform that provides a REST API for accessing quality data and KPIs. The REST API base URL is https://api.infinityqs.com/enact/ and all requests require OAuth 2.0 authentication with a subscription key header.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading InfinityQS data in under 10 minutes.

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What data can I load from InfinityQS?

Here are some of the endpoints you can load from InfinityQS:

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How do I authenticate with the InfinityQS API?

The Enact API uses OAuth 2.0 and OpenID Connect for authentication, requiring a subscription key, client ID, client secret, and a refresh token to obtain access tokens. Requests typically require the subscription key to be passed in the headers.

1. Get your credentials

InfinityQS Enact does not appear to use a self-service API-key dashboard. For the documented publishing API: (1) sign in to Enact with an authorized account; (2) open the Publishing configuration page for KPI/aggregate or event publishing; (3) enable the relevant publishing option; (4) enter your HTTPS receiver URL; (5) create and enter an Access Key of up to 128 characters using the documented allowed characters; (6) in the Subscription Key field, select Copy and provide the copied key to the application administrator. The client application must send that Subscription Key in the OCP-APIM-Subscription-Key header on API GET and acknowledgement POST requests. If the Publishing page or key is unavailable, contact the Enact system administrator or InfinityQS support; the public documentation does not describe a general developer-dashboard API-key workflow.

2. Add them to .dlt/secrets.toml

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[sources.infinityqs_source]
api_key = "your_enact_subscription_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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uv run dlthub ai toolkit install rest-api-pipeline

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the InfinityQS API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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uv run python infinityqs_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline infinityqs_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset infinityqs_data
The duckdb destination used duckdb:/infinityqs.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads KPI data retrieval and acknowledgement from the InfinityQS API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def infinityqs_source(subscription_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.infinityqs.com/enact/",
            "auth": {"type": "bearer", "token": subscription_key},
        },
        "resources": [
            {"name": "kpi_data", "endpoint": {"path": "enact/{kpi}/{id}.json", "data_selector": "data"}},
            {"name": "acknowledgement", "endpoint": {"path": "enact/{kpi}/{id}/acknowledge", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="infinityqs_pipeline",
        destination="duckdb",
        dataset_name="infinityqs_data",
    )
    load_info = pipeline.run(infinityqs_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("infinityqs_pipeline").dataset()
sessions_df = data.kpi_data.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM infinityqs_data.kpi_data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("infinityqs_pipeline").dataset()
data.kpi_data.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load InfinityQS data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack