Load Paxata data to Microsoft Fabric

Paxata is an enterprise data preparation platform that provides a REST API for programmatic access to project management and data integration tasks. Everything needed to build a working Paxata → Microsoft Fabric 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 Paxata to Microsoft Fabric 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 Paxata 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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Paxata API at a glance

These values come from the Paxata API documentation. Check them against the vendor's current reference before relying on them in production.

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How do I authenticate with the Paxata API?

The API uses Bearer token authentication, which is passed in the Authorization header as 'Bearer '.

1. Get your credentials

Log in to the Paxata application dashboard. Navigate to the User menu (typically found in the top-right corner of the interface) and select 'My account'. Within the account settings, locate the 'Tokens' section to generate a new authentication token for API access. Your System Administrator may need to provide specific permissions or confirmation before you can generate tokens.

2. Add them to .dlt/secrets.toml

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[sources.paxata_source]
paxata_url = "https://your-paxata-instance.com/rest"
paxata_token = "your_generated_bearer_token_here"

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 Paxata data can I load into Microsoft Fabric?

These are the Paxata endpoints dlt can load into Microsoft Fabric:

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How do I load only new Paxata records?

The Paxata API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "users", "endpoint": {
    "path": "users",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Paxata pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /users and /library/data from the Paxata API into Microsoft Fabric:

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

@dlt.source
def paxata_source(paxata_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://datarobot.paxata.com/rest",
            "auth": {"type": "bearer", "token": paxata_token},
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users"}},
            {"name": "library_data", "endpoint": {"path": "library/data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_paxata_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="paxata_pipeline",
        destination="fabric",
        dataset_name="paxata_data",
    )
    load_info = pipeline.run(paxata_source())
    print(load_info)


if __name__ == "__main__":
    load_paxata_to_fabric()

Run it with python paxata_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 Paxata data in Microsoft Fabric?

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("paxata_pipeline").dataset()
df = data.users.df()
print(df.head())

SQL:

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SELECT * FROM paxata_data.users LIMIT 10;

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

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How do I deploy the Paxata to Microsoft Fabric 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 Paxata 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 Paxata 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