Dwolla Python API Docs | dltHub

Dwolla is a payments platform providing an API for account-to-account (ACH/RTP/FedNow) transfers, customer and funding-source management, and webhooks. The REST API base URL is https://api.dwolla.com and all requests require a Bearer token for authentication.

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 Dwolla data in under 10 minutes.

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What data can I load from Dwolla?

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

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How do I authenticate with the Dwolla API?

OAuth 2.0 client credentials flow: exchange your application Client ID and Client Secret for a Bearer access token, then include Authorization: Bearer and Accept/Content-Type application/vnd.dwolla.v1.hal+json in requests.

1. Get your credentials

1. Sign up / log into Dwolla Dashboard (Sandbox or Production). 2) Create an application in the Developers / API keys section to obtain a Client ID and Client Secret. 3) Use Client ID and Client Secret with the OAuth client_credentials token endpoint to request an access token. 4) Store the returned Bearer token; include it in Authorization header for API calls. (Dashboard shows Sandbox vs Production keys.)

2. Add them to .dlt/secrets.toml

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[sources.dwolla_migrations_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_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 Dwolla 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 dwolla_migrations_pipeline.py

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

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Pipeline dwolla_migrations_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset dwolla_migrations_data
The duckdb destination used duckdb:/dwolla_migrations.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 dwolla_migrations_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 customers and transfers from the Dwolla 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 dwolla_migrations_source(client_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.dwolla.com",
            "auth": {
                "type": "bearer",
                "token": client_secret,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "customers", "data_selector": "_embedded -> customers"}},
            {"name": "transfers", "endpoint": {"path": "transfers", "data_selector": "_embedded -> transfers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="dwolla_migrations_pipeline",
        destination="duckdb",
        dataset_name="dwolla_migrations_data",
    )
    load_info = pipeline.run(dwolla_migrations_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("dwolla_migrations_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM dwolla_migrations_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("dwolla_migrations_pipeline").dataset()
data.customers.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 Dwolla 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

Most auth uses OAuth client credentials. A 401 Unauthorized occurs when the Bearer token is missing, expired, or invalid. Ensure you exchange Client ID/Secret for a token and include Authorization: Bearer . Use X-Request-ID header from logs to correlate requests.

Rate limits (429 Too Many Requests)

Dwolla enforces concurrency and volume rate limits. On 429 responses, inspect Retry-After header and back off. Reduce concurrency and retry with exponential backoff.

Pagination and HAL embedding

List endpoints return JSON-HAL with paging via _links (next, prev) and list data inside _embedded (e.g., _embedded -> customers or transfers). Check _links for next href to paginate; absence of prev indicates start.

Common validation and business errors

400/422 responses return structured error bodies with top-level error codes and embedded errors. 403 can indicate insufficient permissions. For failed transfers, check transfer failure endpoints and webhook events for details.

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