Load SAP Table Application Server data to Microsoft Fabric

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. Everything needed to build a working SAP Table Application Server → Microsoft Fabric pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your SAP Table Application Server to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the SAP Table Application Server API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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SAP Table Application Server API at a glance

These values come from the SAP Table Application Server API documentation. Check them against the vendor's current reference before relying on them in production.

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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

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[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 file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What SAP Table Application Server data can I load into Microsoft Fabric?

These are the SAP Table Application Server endpoints dlt can load into Microsoft Fabric:

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How do I load only new SAP Table Application Server records?

The SAP Table Application Server API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "business_partners", "endpoint": {
    "path": "API_BUSINESS_PARTNER/A_BusinessPartner",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated SAP Table Application Server pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading metadata and entities (or service_binding for OData-based APIs) from the SAP Table Application Server API into Microsoft Fabric:

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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 load_sap_table_application_server_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_table_application_server_pipeline",
        destination="fabric",
        dataset_name="sap_table_application_server_data",
    )
    load_info = pipeline.run(sap_table_application_server_source())
    print(load_info)


if __name__ == "__main__":
    load_sap_table_application_server_to_fabric()

Run it with python sap_table_application_server_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query SAP Table Application Server data in Microsoft Fabric?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

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

SQL:

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SELECT * FROM sap_table_application_server_data.business_partners LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the SAP Table Application Server to Microsoft Fabric pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw SAP Table Application Server loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load SAP Table Application Server data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack