SAP Table Application Server Python API Docs | dltHub

SAP provides access to data and business logic via OData services built using the ABAP RESTful Programming Model (RAP) or SAP Gateway, which expose SAP tables and application data as RESTful APIs. The REST API base URL is The base URL is specific to the deployed OData service endpoint on the SAP system, typically following the pattern https://<host>:<port>/sap/opu/odata/sap/<service_name>. and Supports Basic authentication and OAuth 2.0..

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 SAP Table Application Server data in under 10 minutes.

---

What data can I load from SAP Table Application Server?

Here are some of the endpoints you can load from SAP Table Application Server:

---

How do I authenticate with the SAP Table Application Server API?

SAP systems typically support Basic authentication or OAuth 2.0 (often via XSUAA). For OAuth, clients must first retrieve an access token from the token endpoint using client credentials or authorization code grant, then provide this as a Bearer token in the 'Authorization' header.

1. Get your credentials

To obtain an API key for SAP services, log in to the SAP Business Accelerator Hub (formerly SAP API Business Hub) using your SAP Universal ID. Navigate to your 'Settings' page or click 'Show API Key' on a specific resource page to retrieve your unique key. Alternatively, if connecting to an OData service or ABAP RESTful API via SAP API Management, the key is generated after creating a Product and Subscription in the Developer Hub. For on-premises systems, credentials are often managed via the SAP Cloud Connector, which handles secure tunnel authentication.

2. Add them to .dlt/secrets.toml

Copy
[sources.sap_table_application_server_source]
api_key = "your_actual_api_key_here"
# If using a custom header:
# headers = { "APIKey" = "your_actual_api_key_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI harness:

Copy
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:

Copy
dlthub 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 SAP Table Application Server 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:

Copy
python sap_table_application_server_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline sap_table_application_server_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.

---

Python pipeline example

This example loads metadata and entities (or service_binding for OData-based APIs) from the SAP Table Application Server API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def sap_table_application_server_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is specific to the deployed OData service endpoint on the SAP system, typically following the pattern https://<host>:<port>/sap/opu/odata/sap/<service_name>.",
            "auth": {"type": "bearer", "token": client_id},
        },
        "resources": [
            {"name": "business_partners", "endpoint": {"path": "API_BUSINESS_PARTNER/A_BusinessPartner"}},
            {"name": "purchase_orders", "endpoint": {"path": "API_PURCHASEORDER_PROCESS_SRV/A_PurchaseOrder"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_table_application_server_pipeline",
        destination="duckdb",
        dataset_name="sap_table_application_server_data",
    )
    load_info = pipeline.run(sap_table_application_server_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):

Copy
import dlt

data = dlt.pipeline("sap_table_application_server_pipeline").dataset()
sessions_df = data.business_partners.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM sap_table_application_server_data.business_partners LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("sap_table_application_server_pipeline").dataset()
data.business_partners.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 SAP Table Application Server 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.

---

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.

Copy
dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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