NCR Voyix Python API Docs | dltHub

Build a NCR Voyix-to-database pipeline in Python using dlt with AI harness support for Claude Code, Cursor, and Codex.

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NCR Voyix provides a cloud-based Business Services Platform for retail and restaurant commerce including POS, ecommerce, and payments. The REST API base URL is https://api.ncr.com and all requests require an Authorization header using HMAC SHA-512 signing.

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 NCR Voyix data in under 10 minutes.


What data can I load from NCR Voyix?

Here are some of the endpoints you can load from NCR Voyix:

ResourceEndpointMethodData selectorDescription
catalog_items/catalog/itemsGETpageContentList catalog items, optionally filtered by status and paged/sorted.
item_details_search/catalog/item-details/searchGETpageContentSearch consolidated item details at a site by pattern, category, group, and status.
sites_find_by_criteria/site/sites/find-by-criteriaPOSTpageContentSearch for sites matching the supplied criteria.
orders_find/order/orders/findPOSTpageContentSearch for orders by criteria such as customer email.
sites_find_nearby/site/sites/find-nearby/{coordinates}GETSearch for sites near specific coordinates.

How do I authenticate with the NCR Voyix API?

The API uses AccessKey (HMAC SHA-512) authentication. The Authorization header must be set to 'AccessKey {sharedKey}:{signature}', where the signature is computed over the request components, and a 'Date' header must also be provided.

1. Get your credentials

To obtain API credentials for the NCR Voyix platform, log in to the NCR Voyix Developer Experience portal. Navigate to the Applications tab. Within the Access Keys section, click the plus (+) icon to generate a new key pair (Shared Key and Secret Key). Ensure you save the secret key immediately, as it cannot be retrieved again after the modal is closed.

2. Add them to .dlt/secrets.toml

[sources.ncr_voyix_source] shared_key = "your_shared_key_here" secret_key = "your_secret_key_here" nep_organization = "your_organization_id" nep_enterprise_unit = "your_enterprise_unit_id"

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:

uv init uv add "dlt[hub]"

1. Install the dlt AI harness:

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:

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 NCR Voyix 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:

uv run python ncr_voyix_pipeline.py

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

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

Inspect your pipeline and data:

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.


Python pipeline example

This example loads authentication and provisioning from the NCR Voyix API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def ncr_voyix_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.ncr.com", "auth": {"type": "api_key", "api_key": api_key, "name": "Authorization", "location": "header"}, }, "resources": [ {"name": "catalog_items", "endpoint": {"path": "catalog/items", "data_selector": "pageContent"}}, {"name": "sites_find_by_criteria", "endpoint": {"path": "site/sites/find-by-criteria", "data_selector": "pageContent"}} ], } yield from rest_api_resources(config) def get_data() -> None: pipeline = dlt.pipeline( pipeline_name="ncr_voyix_pipeline", destination="duckdb", dataset_name="ncr_voyix_data", ) load_info = pipeline.run(ncr_voyix_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):

import dlt data = dlt.pipeline("ncr_voyix_pipeline").dataset() sessions_df = data.catalog_items.df() print(sessions_df.head())

SQL (DuckDB example):

SELECT * FROM ncr_voyix_data.catalog_items LIMIT 10;

In a marimo or Jupyter notebook:

import dlt data = dlt.pipeline("ncr_voyix_pipeline").dataset() data.catalog_items.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 NCR Voyix data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

DestinationExample value
DuckDB (local, default)"duckdb"
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.


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

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