Load Snowflake data to Microsoft Fabric

Snowflake REST API (including the SQL API) allows developers to programmatically access and interact with Snowflake data and resources. Everything needed to build a working Snowflake → 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 Snowflake 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 Snowflake 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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Snowflake API at a glance

These values come from the Snowflake API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Snowflake API?

All requests require an 'Authorization' header set to 'Bearer '. Additionally, an optional 'X-Snowflake-Authorization-Token-Type' header may be included (values: KEYPAIR_JWT, OAUTH, PROGRAMMATIC_ACCESS_TOKEN, WORKLOAD_IDENTITY_FEDERATION).

1. Get your credentials

Snowflake does not provide a single static 'API key' through a dashboard in the traditional sense for all REST operations. Instead, it uses secure authentication methods. For most integrations, a Programmatic Access Token (PAT) is the recommended approach. To obtain one: 1. Log in to your Snowflake account. 2. Navigate to your user settings or use the SQL command 'ALTER USER SET ENABLE_PROGRAMMATIC_ACCESS_TOKEN = TRUE;'. 3. Generate the token via the Snowflake console or SQL. Alternatively, you can use Key Pair authentication by generating a public-private key pair (using OpenSSL), assigning the public key to your Snowflake user via SQL ('ALTER USER SET RSA_PUBLIC_KEY = ...'), and then signing JWTs with your private key in your code.

2. Add them to .dlt/secrets.toml

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[sources.snowflake_source]
api_key = "REPLACE_ME"

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 Snowflake data can I load into Microsoft Fabric?

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

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How do I load only new Snowflake records?

The Snowflake API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "databases", "endpoint": {
    "path": "api/v2/databases",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Snowflake pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading '/api/v2/statements/ and /api/v2/statements/{statementHandle}' from the Snowflake API into Microsoft Fabric:

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

@dlt.source
def snowflake_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<account_identifier>.snowflakecomputing.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "databases", "endpoint": {"path": "api/v2/databases"}},
            {"name": "tables", "endpoint": {"path": "api/v2/databases/{database}/schemas/{schema}/tables"}}
        ],
    }
    yield from rest_api_resources(config)


def load_snowflake_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="snowflake_pipeline",
        destination="fabric",
        dataset_name="snowflake_data",
    )
    load_info = pipeline.run(snowflake_source())
    print(load_info)


if __name__ == "__main__":
    load_snowflake_to_fabric()

Run it with uv run python snowflake_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 Snowflake 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("snowflake_pipeline").dataset()
df = data.statements.df()
print(df.head())

SQL:

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SELECT * FROM snowflake_data.statements LIMIT 10;

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

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How do I deploy the Snowflake 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 Snowflake 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 Snowflake 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