Oracle Retail Xstore POS Python API Docs | dltHub

Oracle Retail Xstore Office Cloud Service exposes REST APIs for administering Xstore data and services and for integrating Xstore Office with related retail systems. The REST API base URL is https://<XSTORE_OFFICE_TENANT_HOST>/xcenter/rest and OAuth 2.0 client-credentials flow with a Bearer access token.

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 Oracle Retail Xstore POS data in under 10 minutes.

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What data can I load from Oracle Retail Xstore POS?

Here are some of the endpoints you can load from Oracle Retail Xstore POS:

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How do I authenticate with the Oracle Retail Xstore POS API?

Obtain an OAuth 2.0 access token from the tenant-specific IDCS/OCI IAM endpoint https://<IDCS_TENANT_HOST>/oauth2/v1/token using HTTP Basic authentication with the client ID and client secret encoded as Base64(client_id

), Content-Type: application/x-www-form-urlencoded;charset=UTF-8, grant_type=client_credentials, and the environment-specific scope. Send the resulting token to Xstore Office/Xcenter REST endpoints in the header Authorization: Bearer <access_token>. Xstore Office API clients also require an OAuth client granted the Xstore Access AppRole.

1. Get your credentials

Oracle Retail Xstore Office REST APIs do not use a standalone API-key dashboard. They use OAuth 2.0 clients (Apps) in the provisioned IDCS or OCI IAM tenant, and the client must have the Xstore Access AppRole. Obtain credentials as follows: 1) Have an administrator identify the Xstore Office tenant, host, and tenant ID, and access the provisioned IDCS/OCI IAM administration UI. 2) Create an OAuth client/App. For initial Xstore Office setup, use the documented Xstore Office Setup App flow; Oracle also documents a REST API for creating the initial OAuth client when applicable. 3) Record the generated client ID and client secret; also record the IDCS/OCI IAM base URL and the customer-environment-specific OAuth scope. 4) Grant the client the Xstore Access AppRole; Oracle says this AppRole is required for Xstore Office REST API invocation and should be granted to the OAuth client, not users or groups. 5) Request an access token from https://<IDCS_BASE_URL>/oauth2/v1/token with HTTP Basic authentication using client_id

, form data grant_type=client_credentials, and the required scope. 6) Call Xstore Office REST endpoints with Authorization: Bearer <access_token>. The Xstore Office Administration/Security guides and My Oracle Support documentation may be required because the detailed setup screens and service guide are customer-access-controlled.

2. Add them to .dlt/secrets.toml

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[sources.oracle_retail_xstore_pos_source]
client_id = "your_oauth_client_id"
client_secret = "your_oauth_client_secret"
idcs_base_url = "https://your-idcs-or-iam-host"
scope = "your_customer_environment_scope"
tenant_id = "your_xstore_office_tenant_id"
base_url = "https://your-xstore-office-host"

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 Oracle Retail Xstore POS 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 oracle_retail_xstore_pos_pipeline.py

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

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Pipeline oracle_retail_xstore_pos_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset oracle_retail_xstore_pos_data
The duckdb destination used duckdb:/oracle_retail_xstore_pos.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 oauth2/v1/token and xcenter/rest/... from the Oracle Retail Xstore POS 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 oracle_retail_xstore_pos_source(oauth_client_id_and_secret=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<XSTORE_OFFICE_TENANT_HOST>/xcenter/rest",
            "auth": {"type": "bearer", "token": oauth_client_id_and_secret},
        },
        "resources": [
            {"name": "xservices_rest_api", "endpoint": {"path": "xservices"}},
            {"name": "file_resource", "endpoint": {"path": "{tenant}/v1/file/{directory}/{store}/{filename}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="oracle_retail_xstore_pos_pipeline",
        destination="duckdb",
        dataset_name="oracle_retail_xstore_pos_data",
    )
    load_info = pipeline.run(oracle_retail_xstore_pos_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("oracle_retail_xstore_pos_pipeline").dataset()
sessions_df = data.xservices_rest_api.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM oracle_retail_xstore_pos_data.xservices_rest_api LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("oracle_retail_xstore_pos_pipeline").dataset()
data.xservices_rest_api.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 Oracle Retail Xstore POS 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