Sftp Python API Docs | dltHub

Sftp Gateway is a REST API to programmatically manage Thorn Tech’s SFTP Gateway (create/manage cloud connections, folders, and SFTP users and view diagnostics). The REST API base URL is https://{host}:443/backend/3.0.0 and all requests require a Bearer OAuth access token.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Sftp data in under 10 minutes.

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What data can I load from Sftp?

Here are some of the endpoints you can load from Sftp:

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How do I authenticate with the Sftp API?

Obtain an OAuth access token by POSTing web‑admin username/password plus application client‑id and client‑secret (from /opt/sftpgw/application.properties) to the token endpoint; include the returned Bearer token in Authorization: Bearer for all API calls.

1. Get your credentials

1. SSH to the SFTP Gateway instance; 2) sudo cat /opt/sftpgw/application.properties; 3) copy values of security.client-id and security.client-secret; 4) use a web admin username/password; 5) POST form-data grant_type=password, username, password, scope=read along with client-id/secret to the token endpoint to get the Bearer token.

2. Add them to .dlt/secrets.toml

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[sources.sftp_source]
oauth_token = "your_bearer_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Sftp API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python sftp_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline sftp_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sftp_data
The duckdb destination used duckdb:/sftp.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline sftp_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads users and folders from the Sftp API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def sftp_source(oauth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{host}:443/backend/3.0.0",
            "auth": {
                "type": "bearer",
                "token": oauth_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "backend/3.0.0/users"}},
            {"name": "folders", "endpoint": {"path": "backend/3.0.0/folders"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sftp_pipeline",
        destination="duckdb",
        dataset_name="sftp_data",
    )
    load_info = pipeline.run(sftp_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("sftp_pipeline").dataset()
sessions_df = data.users.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sftp_data.users LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sftp_pipeline").dataset()
data.users.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Sftp data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you get 401/403: verify you exchanged the web admin username/password plus application client-id and client-secret at the token endpoint (POST grant_type=password) and that Authorization: Bearer is present. Tokens are required for all admin endpoints.

Token retrieval and endpoints

Token endpoint differs by version: newer v3 uses /backend/login (or /backend/oauth/token on older installs). If POST to expected token path fails, check /opt/sftpgw/application.properties for client-id/client-secret and consult the appliance’s OpenAPI JSON (provided in docs) for the exact token path.

Common errors

• 400 Bad Request: malformed JSON or missing required fields on POST/PUT.
• 401/403 Unauthorized: invalid/expired token or incorrect client/web-admin credentials.
• 404 Not Found: bad endpoint path or resource id/username does not exist.
• 409 Conflict: resource already exists (e.g., username collision).

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack