Striga Python API Docs | dltHub

Striga is a platform that provides embedded banking, payments, wallet and card APIs for compliance‑ready financial services. The REST API base URL is https://www.sandbox.striga.com/api/v1 and all requests require HMAC‑signed authentication with an API key and signature.

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 Striga data in under 10 minutes.

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What data can I load from Striga?

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

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How do I authenticate with the Striga API?

Authentication uses HMAC‑signed requests with an API key and secret; each request must include an 'X-API-Key' header and an 'X-Signature' header containing the HMAC signature of the request payload.

1. Get your credentials

1. Sign up for a Striga developer account at https://dashboard.striga.com.
2. After email verification, log in and navigate to the API Keys section in the dashboard.
3. Click Create Sandbox API Key to generate a sandbox API key and secret pair.
4. Copy the displayed API Key and API Secret; store them securely.
5. Use these credentials in your dlt configuration (see secrets.toml example).

2. Add them to .dlt/secrets.toml

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[sources.striga_google_pay_push_source]
api_key = "your_api_key_here"
api_secret = "your_api_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 Striga 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 striga_google_pay_push_pipeline.py

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

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Pipeline striga_google_pay_push_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset striga_google_pay_push_data
The duckdb destination used duckdb:/striga_google_pay_push.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 striga_google_pay_push_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 get_user_by_id and get_all_wallets from the Striga 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 striga_google_pay_push_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.sandbox.striga.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "get_user_by_id", "endpoint": {"path": "user/{id}", "data_selector": "user"}},
            {"name": "get_all_wallets", "endpoint": {"path": "wallets", "data_selector": "wallets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="striga_google_pay_push_pipeline",
        destination="duckdb",
        dataset_name="striga_google_pay_push_data",
    )
    load_info = pipeline.run(striga_google_pay_push_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("striga_google_pay_push_pipeline").dataset()
sessions_df = data.get_user_by_id.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM striga_google_pay_push_data.get_user_by_id LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("striga_google_pay_push_pipeline").dataset()
data.get_user_by_id.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 Striga 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 receive a 401 Unauthorized response, verify that your X-API-Key header contains the correct sandbox API key and that the X-Signature header is generated using the proper HMAC algorithm and secret.

Rate limits

Striga enforces rate limits per account. A 429 Too Many Requests response indicates you have exceeded the allowed request quota. Implement exponential back‑off and respect the Retry-After header if present.

Pagination

List endpoints may paginate results using page and pageSize query parameters. Check the response for next_page or similar fields to retrieve subsequent pages.

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