Braintree Python API Docs | dltHub

Braintree is a payments platform (a PayPal service) that provides APIs to process transactions, manage customers, vault payment methods, and handle subscriptions. The REST API base URL is https://api.braintreegateway.com/merchants/{merchant_id} and Requests use API keys with HTTP Basic authentication for REST, and Basic or Bearer tokens for GraphQL..

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 Braintree data in under 10 minutes.

---

What data can I load from Braintree?

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

---

How do I authenticate with the Braintree API?

For GraphQL, include an Authorization header with Basic <Base64(public_key:private_key)> or Bearer . Also send Braintree-Version: YYYY-MM-DD and Content-Type: application/json. REST endpoints use HTTP Basic auth with merchant_id, public_key and private_key.

1. Get your credentials

1. Sign in to the Braintree Control Panel (or create a sandbox account). 2) Navigate to Settings → API → API Keys. 3) Locate and copy your merchant_id, public_key, and private_key. 4) For client‑side tokenization, generate a client token via the server SDK or find tokenization keys in the same API section.

2. Add them to .dlt/secrets.toml

Copy
[sources.braintree_payments_source]
merchant_id = "your_merchant_id"
public_key = "your_public_key"
private_key = "your_private_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Braintree 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:

Copy
python braintree_payments_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline braintree_payments_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.

---

Python pipeline example

This example loads transactions and customers from the Braintree API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def braintree_payments_source(private_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.braintreegateway.com/merchants/{merchant_id}",
            "auth": {
                "type": "http_basic",
                "private_key": private_key,
            },
        },
        "resources": [
            {"name": "transactions", "endpoint": {"path": "merchants/{merchant_id}/transactions", "data_selector": "transactions"}},
            {"name": "customers", "endpoint": {"path": "merchants/{merchant_id}/customers", "data_selector": "customers"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="braintree_payments_pipeline",
        destination="duckdb",
        dataset_name="braintree_payments_data",
    )
    load_info = pipeline.run(braintree_payments_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.

---

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):

Copy
import dlt

data = dlt.pipeline("braintree_payments_pipeline").dataset()
sessions_df = data.transactions.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM braintree_payments_data.transactions LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("braintree_payments_pipeline").dataset()
data.transactions.df().head()

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

---

What destinations can I load Braintree 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.

---

Troubleshooting

Authentication failures

Check Authorization header format: GraphQL expects Basic <Base64(public:private)> for API keys or Bearer for tokenization keys/fingerprints. Also include Braintree-Version header; missing or incorrect keys return authentication/authorization errors.

GraphQL errors and status codes

GraphQL always returns HTTP 200; inspect the top-level "errors" array and "extensions.requestId" for debugging. Partial successes can return both data and errors in the same response.

Rate limits and service availability

Observe 429/503 responses from REST endpoints. For GraphQL, errorClass values such as RESOURCE_LIMIT or SERVICE_AVAILABILITY will be returned in the errors[].extensions.

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

---

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.

Copy
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