Visa Direct Connect Python API Docs | dltHub

Visa Direct Connect provides APIs for streamlined access to multiple payment endpoints, enabling payouts to accounts, wallets, and cards. It uses specific profiles for integration and offers APIs for Visa Direct for Account, Wallet, and Card. The Send Payout API sends funds, while the Request Exception API handles reversals. The REST API base URL is https://api.visa.com/vdp and All requests require Mutual TLS client certificate authentication and an API key header..

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 Visa Direct Connect data in under 10 minutes.

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What data can I load from Visa Direct Connect?

Here are some of the endpoints you can load from Visa Direct Connect:

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How do I authenticate with the Visa Direct Connect API?

Provide a client certificate (.pem) and private key for Mutual TLS and include the API key in the 'Authorization' header as 'API-Key {key}'.

1. Get your credentials

1. Log in to the Visa Developer Center (https://developer.visa.com). 2. Navigate to 'My Apps' and create a new project or select an existing one. 3. Under the project, enable the Visa Direct Connect capability. 4. In the 'Credentials' tab, click 'Generate Certificate' to obtain a client certificate and private key. 5. Download the generated .pem files and copy the API key shown in the same tab. 6. Store the certificate, private key, and API key securely for use in dlt configuration.

2. Add them to .dlt/secrets.toml

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[sources.visa_direct_connect_source]
certificate = "/path/to/client_certificate.pem"
private_key = "/path/to/private_key.pem"
api_key = "YOUR_API_KEY"

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 Visa Direct Connect 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 visa_direct_connect_pipeline.py

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

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Pipeline visa_direct_connect_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset visa_direct_connect_data
The duckdb destination used duckdb:/visa_direct_connect.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 visa_direct_connect_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 query_payout and get_account_balance from the Visa Direct Connect 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 visa_direct_connect_source(certificate=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.visa.com/vdp",
            "auth": {
                "type": "mtls",
                "certificate": certificate,
            },
        },
        "resources": [
            {"name": "query_payout", "endpoint": {"path": "visadirect/connect/v1/payouts/query", "data_selector": "payouts"}},
            {"name": "get_account_balance", "endpoint": {"path": "visadirect/connect/v1/balance", "data_selector": "balance"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="visa_direct_connect_pipeline",
        destination="duckdb",
        dataset_name="visa_direct_connect_data",
    )
    load_info = pipeline.run(visa_direct_connect_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("visa_direct_connect_pipeline").dataset()
sessions_df = data.query_payout.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM visa_direct_connect_data.query_payout LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("visa_direct_connect_pipeline").dataset()
data.query_payout.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 Visa Direct Connect 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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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