Load FactSet Analytics data to Microsoft Fabric

FactSet Analytics API provides access to portfolio analytics and financial calculation data via a REST interface. Everything needed to build a working FactSet Analytics → 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 FactSet Analytics 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 FactSet Analytics 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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FactSet Analytics API at a glance

These values come from the FactSet Analytics API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the FactSet Analytics API?

FactSet APIs use Basic HTTP authentication where the Authorization header is set to 'Basic <base64_encoded_username-serial

>'. The username is the FactSet username-serial and the password is the API key.

1. Get your credentials

1. Log in to the FactSet Developer Portal. 2. Navigate to Profile > API Authentication. 3. Click 'Create', select 'API Key', and click 'Next'. 4. Enter the required details (account information and IP address ranges for your requests), then click 'Submit'. 5. Copy the generated API key immediately, as it will not be displayed again; use your FactSet username-serial as the username and this key as the password for HTTP Basic Authentication.

2. Add them to .dlt/secrets.toml

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[sources.factset_analytics_source]
factset_username_serial = "YOUR_USERNAME-SERIAL"
factset_api_key = "YOUR_GENERATED_API_KEY"

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 FactSet Analytics data can I load into Microsoft Fabric?

These are the FactSet Analytics endpoints dlt can load into Microsoft Fabric:

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How do I load only new FactSet Analytics records?

FactSet Analytics exposes _paginationCursor on analytics/fields/v1/user-defined-field-group, 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": "user_defined_field_groups", "endpoint": {
    "path": "analytics/fields/v1/user-defined-field-group",
    "data_selector": "data",
    "incremental": {"cursor_path": "_paginationCursor", "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 FactSet Analytics pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /analytics/engines/pa/v3/calculations and /analytics/accounts/v3/models from the FactSet Analytics API into Microsoft Fabric:

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

@dlt.source
def factset_analytics_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.factset.com",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "user_defined_field_groups", "endpoint": {"path": "analytics/fields/v1/user-defined-field-group", "data_selector": "data"}},
            {"name": "analytics_calculations", "endpoint": {"path": "analytics/engines/pa/v3/calculations", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_factset_analytics_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="factset_analytics_pipeline",
        destination="fabric",
        dataset_name="factset_analytics_data",
    )
    load_info = pipeline.run(factset_analytics_source())
    print(load_info)


if __name__ == "__main__":
    load_factset_analytics_to_fabric()

Run it with python factset_analytics_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 FactSet Analytics 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("factset_analytics_pipeline").dataset()
df = data.user_defined_field_groups.df()
print(df.head())

SQL:

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SELECT * FROM factset_analytics_data.user_defined_field_groups LIMIT 10;

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

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How do I deploy the FactSet Analytics 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 FactSet Analytics 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 FactSet Analytics 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