Load SumTotal data to Microsoft Fabric

SumTotal is a learning and talent management platform that provides REST and OData APIs for integrating organizational data. Everything needed to build a working SumTotal → 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 SumTotal 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 SumTotal 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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SumTotal API at a glance

These values come from the SumTotal API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the SumTotal API?

SumTotal uses OAuth 2.0 authentication. To authenticate, you must POST a request with application/x-www-form-urlencoded credentials to the /apisecurity/connect/token endpoint, which returns a JWT access token that must be provided in the Authorization header as a Bearer token for subsequent requests.

1. Get your credentials

To obtain credentials for the SumTotal REST API, you must configure an OAuth client within the SumTotal administration dashboard. Follow these steps: 1. Log in to your SumTotal instance with administrative privileges. 2. Navigate to Administration > Common Objects > Configuration > OAuth Configuration. 3. Click Add to create a new OAuth client. 4. Define your Client ID. 5. Enter a secure Client Secret. 6. Select the 'allapis' scope to ensure access to REST APIs. 7. Ensure that PKCE (Proof Key for Code Exchange) is disabled, as enabling it may block standard machine-to-machine API calls. 8. Click Submit to save the configuration. Use the resulting Client ID and Client Secret, along with your tenant base URL, to authenticate via the OAuth 2.0 Client Credentials grant flow.

2. Add them to .dlt/secrets.toml

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[sources.sumtotal_source]
base_url = "https://your-tenant.sumtotal.host"
client_id = "your_client_id"
client_secret = "your_client_secret"
scope = "allapis"

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 SumTotal data can I load into Microsoft Fabric?

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

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How do I load only new SumTotal records?

SumTotal exposes offset on apis/api/v1/users, 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": "users", "endpoint": {
    "path": "apis/api/v1/users",
    "data_selector": "items",
    "incremental": {"cursor_path": "offset", "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 SumTotal pipeline look like?

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

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

@dlt.source
def sumtotal_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{site-url}/apis",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "apis/api/v1/users", "data_selector": "items"}},
            {"name": "transcripts", "endpoint": {"path": "odata/api/Transcript", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def load_sumtotal_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sumtotal_pipeline",
        destination="fabric",
        dataset_name="sumtotal_data",
    )
    load_info = pipeline.run(sumtotal_source())
    print(load_info)


if __name__ == "__main__":
    load_sumtotal_to_fabric()

Run it with python sumtotal_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 SumTotal 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("sumtotal_pipeline").dataset()
df = data.users.df()
print(df.head())

SQL:

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SELECT * FROM sumtotal_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 SumTotal 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 SumTotal 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 SumTotal 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