SAP S4HANA Python API Docs | dltHub

SAP S/4HANA REST APIs (primarily OData services) provide access to enterprise ERP data and processes for integration and extension purposes. The REST API base URL is https://{host}:{port}/sap/opu/odata/sap/ and supports Basic Authentication and various OAuth 2.0 flows (e.g., Client Credentials, SAML Bearer Assertion).

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 SAP S4HANA data in under 10 minutes.

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What data can I load from SAP S4HANA?

Here are some of the endpoints you can load from SAP S4HANA:

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How do I authenticate with the SAP S4HANA API?

SAP S/4HANA supports Basic Authentication (requires Authorization header with base64-encoded credentials) and OAuth 2.0 (requires an access token in the Authorization: Bearer header). For OAuth 2.0 flows, the token is typically obtained from an authorization server endpoint.

1. Get your credentials

To set up credentials for SAP S/4HANA Cloud REST APIs, you must perform the following steps in your S/4HANA Cloud system: 1. Navigate to the 'Communication Management' group in the Fiori Launchpad. 2. Open the 'Communication Users' app and create a new technical user (Communication User) with a secure password. 3. Open the 'Communication Systems' app, create a new system, and assign the previously created Communication User to it for 'Inbound Communication'. 4. Open the 'Communication Arrangements' app, create a new arrangement for the specific OData/REST service scenario you need, select the Communication System created in step 3, and save the arrangement. This process generates the necessary service URL and activates the user for API access. Note that for OAuth 2.0 scenarios, you may need to configure additional OAuth details within the Communication System or via SAP BTP.

2. Add them to .dlt/secrets.toml

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[sources.sap_s4hana_source]
username = "COMMUNICATION_USER_NAME"
password = "COMMUNICATION_USER_PASSWORD"
base_url = "https://myXXXXXX-api.s4hana.ondemand.com" # If using OAuth 2.0 (configured via BTP or specific service keys)
# client_id = "YOUR_CLIENT_ID"
# client_secret = "YOUR_CLIENT_SECRET"
# token_url = "https://your-token-url.authentication.sap.hana.ondemand.com/oauth/token"

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 SAP S4HANA 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 sap_s4hana_pipeline.py

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

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Pipeline sap_s4hana_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sap_s4hana_data
The duckdb destination used duckdb:/sap_s4hana.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 /sap/opu/odata/sap/ and /oauth/token from the SAP S4HANA 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 sap_s4hana_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{host}:{port}/sap/opu/odata/sap/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "products", "endpoint": {"path": "API_PRODUCT_SRV/A_Product", "data_selector": "value"}},
            {"name": "sales_orders", "endpoint": {"path": "API_SALES_ORDER_SRV/A_SalesOrder", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_s4hana_pipeline",
        destination="duckdb",
        dataset_name="sap_s4hana_data",
    )
    load_info = pipeline.run(sap_s4hana_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("sap_s4hana_pipeline").dataset()
sessions_df = data.products.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sap_s4hana_data.products LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sap_s4hana_pipeline").dataset()
data.products.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 SAP S4HANA 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