Load FHIR data to Microsoft Fabric

FHIR is an HL7 standard for exchanging healthcare information electronically using a RESTful API structure. Everything needed to build a working FHIR → 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.

---

Build your FHIR 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 FHIR 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.

---

FHIR API at a glance

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

---

How do I authenticate with the FHIR API?

FHIR servers typically use OAuth 2.0 to authorize requests. The client must present an 'Authorization' header containing a Bearer token: 'Authorization: Bearer <access_token>'.

1. Get your credentials

1. Register your application in the provider's developer console or administrative portal to obtain a unique Client ID and Client Secret. 2. Configure the necessary scopes (permissions) for your application, such as read/write access to specific FHIR resources. 3. Identify the Token Endpoint and Authorization Server URL provided by your healthcare data provider (e.g., Azure Entra ID, SMART on FHIR server). 4. Ensure your server environment is configured to securely store these credentials, as they act as the authentication mechanism for your data pipeline.

2. Add them to .dlt/secrets.toml

Copy
[sources.fhir_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_here"
token_url = "https://your-auth-provider.com/token"
fhir_base_url = "https://your-fhir-service-url.com"

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.

---

What FHIR data can I load into Microsoft Fabric?

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

---

How do I load only new FHIR records?

FHIR exposes _lastUpdated on v2/fhir/Patient, 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.

Copy
{"name": "patient", "endpoint": {
    "path": "v2/fhir/Patient",
    "data_selector": "entry",
    "incremental": {"cursor_path": "_lastUpdated", "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.

---

What does the generated FHIR pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading token and authorize from the FHIR API into Microsoft Fabric:

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

@dlt.source
def fhir_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The FHIR service base URL is defined by the server implementation (e.g., 'https://server/path') and is the root address for all FHIR resource interactions.",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "patient", "endpoint": {"path": "v2/fhir/Patient", "data_selector": "entry"}},
            {"name": "explanation_of_benefit", "endpoint": {"path": "v2/fhir/ExplanationOfBenefit", "data_selector": "entry"}}
        ],
    }
    yield from rest_api_resources(config)


def load_fhir_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fhir_pipeline",
        destination="fabric",
        dataset_name="fhir_data",
    )
    load_info = pipeline.run(fhir_source())
    print(load_info)


if __name__ == "__main__":
    load_fhir_to_fabric()

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

---

How do I query FHIR 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):

Copy
import dlt

data = dlt.pipeline("fhir_pipeline").dataset()
df = data.patient.df()
print(df.head())

SQL:

Copy
SELECT * FROM fhir_data.patient LIMIT 10;

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

---

How do I deploy the FHIR 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 FHIR loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load FHIR 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.

---

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