Fiserv Optis Python API Docs | dltHub

Fiserv Optis is a financial services platform providing RESTful APIs for card issuing, transaction processing, and management across various banking domains. The REST API base URL is https://prod.api.fiservapps.com and API requests generally require a Bearer token or HMAC signature in the Authorization 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 add "dlt[hub]" and start loading Fiserv Optis data in under 10 minutes.

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What data can I load from Fiserv Optis?

Here are some of the endpoints you can load from Fiserv Optis:

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How do I authenticate with the Fiserv Optis API?

Authentication is performed by obtaining a JWT access token via a POST request to a dedicated endpoint, which is then passed in the Authorization header as a Bearer token. For other Fiserv API products that use HMAC, a signature must be generated using an API Key, timestamp, request body, and URL path, then passed in the Authorization header.

1. Get your credentials

1. Navigate to the Fiserv Developer Portal at https://fiserv.dev/. 2. Register for an account using your business email address and complete the identity verification/MFA process. 3. Log in to the Developer Portal. 4. Navigate to the API Keys page. 5. Click Create new key to initiate a request. 6. Select your environment (Sandbox for testing, Production for live operations). 7. Provide required details (e.g., Merchant ID, StoreID) if requesting a Production key. 8. Once approved/generated, the portal will display your API Key and Secret. Securely store these, as the secret may only be visible once.

2. Add them to .dlt/secrets.toml

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[sources.fiserv_optis_source]
access_token = "REPLACE_ME"

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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Fiserv Optis 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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uv run python fiserv_optis_pipeline.py

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

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Pipeline fiserv_optis_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset fiserv_optis_data
The duckdb destination used duckdb:/fiserv_optis.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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uv run dlthub 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 /payments/v2/paymentHistory and /payments/v2/rightTimePayment from the Fiserv Optis 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 fiserv_optis_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://prod.api.fiservapps.com",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "payment_history", "endpoint": {"path": "payments/v2/paymentHistory"}},
            {"name": "payment_details", "endpoint": {"path": "payments/v2/paymentDetails"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="fiserv_optis_pipeline",
        destination="duckdb",
        dataset_name="fiserv_optis_data",
    )
    load_info = pipeline.run(fiserv_optis_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("fiserv_optis_pipeline").dataset()
sessions_df = data.payment_history.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM fiserv_optis_data.payment_history LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("fiserv_optis_pipeline").dataset()
data.payment_history.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 Fiserv Optis 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 harness:

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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