Load SAP BW Application Server data to Microsoft Fabric

SAP BW OData API is an OData-based REST interface for querying SAP BW data, executing BEx queries, and accessing InfoProviders. Everything needed to build a working SAP BW 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 BW 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 BW 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 BW Application Server API at a glance

These values come from the SAP BW Application Server API reference — the authoritative source if anything here looks out of date.

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

Authentication typically requires an initial request with Basic Auth to fetch a CSRF token (passing 'X-CSRF-Token: Fetch'), which is then returned in the response headers and must be provided in the 'X-CSRF-Token' header for subsequent stateful operations. Cookie-based session management is also common for maintaining the session state.

1. Get your credentials

SAP BW does not use a standard 'API key' dashboard for REST access. Access is typically managed through the SAP NetWeaver AS ABAP. 1. Create a dedicated technical user in SAP (transaction SU01) with appropriate roles for the specific OData service or Information Access (InA) service. 2. Ensure the required SICF services (e.g., /sap/bw/ina or OData services created via SEGW) are active in transaction SICF. 3. Authenticate using Basic Authentication (Username and Password) or by exchanging a token if using SAP's XSRF-protected APIs (requires a initial GET request to fetch an X-CSRF-Token).

2. Add them to .dlt/secrets.toml

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[sources.sap_bw_application_server_source]
username = "your_sap_username"
password = "your_sap_password"
base_url = "https://<your-abap-server-host>:<port>"
client_id = "your_client_id"

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 BW Application Server data can I load into Microsoft Fabric?

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

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

The SAP BW 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": "bw_queries", "endpoint": {
    "path": "RSOD_BW_QUERY_SRV/Queries",
    # 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 BW Application Server pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /sap/bw/ina/GetServerInfo and /v1/logon/token from the SAP BW 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_bw_application_server_source(x_csrf_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<host>:<port>/sap/opu/odata/sap/",
            "auth": {"type": "bearer", "token": x_csrf_token},
        },
        "resources": [
            {"name": "bw_queries", "endpoint": {"path": "RSOD_BW_QUERY_SRV/Queries", "data_selector": "d"}},
            {"name": "query_results", "endpoint": {"path": "RSOD_BW_QUERY_SRV/Queries('{queryName}')/Results", "data_selector": "d"}}
        ],
    }
    yield from rest_api_resources(config)


def load_sap_bw_application_server_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_bw_application_server_pipeline",
        destination="fabric",
        dataset_name="sap_bw_application_server_data",
    )
    load_info = pipeline.run(sap_bw_application_server_source())
    print(load_info)


if __name__ == "__main__":
    load_sap_bw_application_server_to_fabric()

Run it with python sap_bw_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 BW 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_bw_application_server_pipeline").dataset()
df = data.bw_queries.df()
print(df.head())

SQL:

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SELECT * FROM sap_bw_application_server_data.bw_queries LIMIT 10;

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

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How do I deploy the SAP BW 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 BW 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 BW 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