Load MongoDB Atlas data to Microsoft Fabric

MongoDB Atlas Administration API allows developers to manage all components in MongoDB Atlas. Everything needed to build a working MongoDB Atlas → 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 MongoDB Atlas 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 MongoDB Atlas 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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MongoDB Atlas API at a glance

These values come from the MongoDB Atlas API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MongoDB Atlas API?

The API uses OAuth 2.0 with a service account client ID and secret to generate a bearer token, which must be passed in the 'Authorization' header as 'Bearer {token}'.

1. Get your credentials

To obtain programmatic credentials for MongoDB Atlas, navigate to the Atlas UI and go to Access Manager in your Organization settings. Select API Keys, click Add API Key, and assign the necessary organization-level roles. Upon creation, copy and save your Public Key and Private Key immediately, as the Private Key will not be visible again. Alternatively, the recommended modern method is to create a Service Account, which provides a Client ID and a rotatable Secret that can be used to generate OAuth 2.0 access tokens.

2. Add them to .dlt/secrets.toml

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[sources.mongodb_atlas_source]
public_key = "your_public_key_here"
private_key = "your_private_key_here" # OR for Service Account (recommended)
client_id = "your_client_id_here"
client_secret = "your_client_secret_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 MongoDB Atlas data can I load into Microsoft Fabric?

These are the MongoDB Atlas endpoints dlt can load into Microsoft Fabric:

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How do I load only new MongoDB Atlas records?

MongoDB Atlas exposes pageNum on api/atlas/v2/orgs, 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": "organizations", "endpoint": {
    "path": "api/atlas/v2/orgs",
    "data_selector": "results",
    "incremental": {"cursor_path": "pageNum", "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 MongoDB Atlas pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading orgs and groups/{groupId}/clusters from the MongoDB Atlas API into Microsoft Fabric:

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

@dlt.source
def mongodb_atlas_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cloud.mongodb.com/api/atlas/v2",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "organizations", "endpoint": {"path": "api/atlas/v2/orgs", "data_selector": "results"}},
            {"name": "clusters", "endpoint": {"path": "api/atlas/v2/clusters", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_mongodb_atlas_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mongodb_atlas_pipeline",
        destination="fabric",
        dataset_name="mongodb_atlas_data",
    )
    load_info = pipeline.run(mongodb_atlas_source())
    print(load_info)


if __name__ == "__main__":
    load_mongodb_atlas_to_fabric()

Run it with uv run python mongodb_atlas_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 MongoDB Atlas 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("mongodb_atlas_pipeline").dataset()
df = data.organizations.df()
print(df.head())

SQL:

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SELECT * FROM mongodb_atlas_data.organizations LIMIT 10;

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

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How do I deploy the MongoDB Atlas 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 MongoDB Atlas 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 MongoDB Atlas 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