Load MongoDB data to Snowflake

MongoDB Atlas Data API provides HTTPS access to MongoDB collections for CRUD and aggregation operations without requiring native database drivers. Everything needed to build a working MongoDB → Snowflake 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 MongoDB to Snowflake 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 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.

---

MongoDB API at a glance

These values come from the MongoDB API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the MongoDB API?

The Data API is authenticated using an API key provided in the 'api-key' header, or alternatively, by including a Bearer token in the 'Authorization' header.

1. Get your credentials

To obtain API credentials in the MongoDB Atlas dashboard: 1. Navigate to your Organization or Project dashboard. 2. Under the 'Access Manager' or 'Users and Teams' section (depending on your UI version), select 'API Keys'. 3. Click 'Add API Key'. 4. Provide a description and select the appropriate organization or project roles. 5. Click 'Next'. 6. Copy and save your Public Key and Private Key immediately. The Private Key will not be visible again after this step. Ensure you add your current IP address to the API Key's access list if required by your security policy. Note: MongoDB recommends using Service Accounts (OAuth 2.0) as a more secure alternative to legacy API keys.

2. Add them to .dlt/secrets.toml

Copy
[sources.mongodb_source]
# Note: Use of legacy API Keys is discouraged in favor of Service Account credentials
mongodb_public_key = "your_public_key_here"
mongodb_private_key = "your_private_key_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.

---

What MongoDB data can I load into Snowflake?

These are the MongoDB endpoints dlt can load into Snowflake:

---

How do I load only new MongoDB records?

MongoDB exposes updated_at on action/find, 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": "find_documents", "endpoint": {
    "path": "action/find",
    "data_selector": "documents",
    "incremental": {"cursor_path": "updated_at", "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 MongoDB pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /orgs and /groups (or projects) from the MongoDB API into Snowflake:

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

@dlt.source
def mongodb_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://data.mongodb-api.com/app/{app-id}/endpoint/data/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "find_documents", "endpoint": {"path": "action/find", "data_selector": "documents"}},
            {"name": "aggregate_documents", "endpoint": {"path": "action/aggregate", "data_selector": "documents"}}
        ],
    }
    yield from rest_api_resources(config)


def load_mongodb_to_snowflake() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mongodb_pipeline",
        destination="snowflake",
        dataset_name="mongodb_data",
    )
    load_info = pipeline.run(mongodb_source())
    print(load_info)


if __name__ == "__main__":
    load_mongodb_to_snowflake()

Run it with uv run python mongodb_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 MongoDB data in Snowflake?

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("mongodb_pipeline").dataset()
df = data.find_documents.df()
print(df.head())

SQL:

Copy
SELECT * FROM mongodb_data.find_documents LIMIT 10;

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

---

How do I deploy the MongoDB to Snowflake 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 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 MongoDB 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