Load SAP BW Message Server data to Microsoft Fabric

SAP BW provides various HTTP/REST-based services for data exchange, OData access, and system modeling via the Internet Communication Framework (ICF) on SAP NetWeaver Application Server. Everything needed to build a working SAP BW Message 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.

---

Build your SAP BW Message 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 Message 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.

---

SAP BW Message Server API at a glance

These values come from the SAP BW Message Server API reference — the authoritative source if anything here looks out of date.

---

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

Authentication typically follows SAP NetWeaver standard mechanisms, commonly using Basic Authentication via the Authorization header with base64-encoded credentials (username

).

1. Get your credentials

SAP BW does not have a single native Message Server REST API that uses static API keys. Instead, access to BW data via HTTP/REST is managed through the Internet Communication Framework (ICF) services (transaction SICF). To obtain credentials: 1. In your SAP system, use transaction SICF to locate or create the relevant HTTP/REST service. 2. Ensure the service is active. 3. Authenticate using standard SAP credentials (Username/Password or SAP Logon Tickets) as defined by your organization's security policy. 4. For programmatic access, you will typically use an SAP user account with appropriate authorizations for the specific OData service or custom ABAP handler you are targeting.

2. Add them to .dlt/secrets.toml

Copy
[sources.sap_bw_message_server_source]
user = "your_sap_username"
password = "your_sap_password"
client = "your_client_id"
message_server = "your_message_server_host"
logon_group = "your_logon_group"
system_number = "your_system_number"

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.

---

What SAP BW Message Server data can I load into Microsoft Fabric?

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

---

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

The SAP BW Message 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.

Copy
{"name": "discovery", "endpoint": {
    "path": "sap/bw/whm/backend/discovery",
    # 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.

---

What does the generated SAP BW Message Server pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /sap/bw/ and /sap/opu/odata/ from the SAP BW Message Server API into Microsoft Fabric:

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

@dlt.source
def sap_bw_message_server_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is constructed based on the system installation as <Protocol>://<Host>:<Port>/sap/bw or <Protocol>://<Host>:<Port>/sap/opu/odata/sap/.",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": credentials},
        },
        "resources": [
            {"name": "discovery", "endpoint": {"path": "sap/bw/whm/backend/discovery"}},
            {"name": "info_providers", "endpoint": {"path": "sap/bw4/v1/catalog/infoproviders"}}
        ],
    }
    yield from rest_api_resources(config)


def load_sap_bw_message_server_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_bw_message_server_pipeline",
        destination="fabric",
        dataset_name="sap_bw_message_server_data",
    )
    load_info = pipeline.run(sap_bw_message_server_source())
    print(load_info)


if __name__ == "__main__":
    load_sap_bw_message_server_to_fabric()

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

---

How do I query SAP BW Message 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):

Copy
import dlt

data = dlt.pipeline("sap_bw_message_server_pipeline").dataset()
df = data.discovery.df()
print(df.head())

SQL:

Copy
SELECT * FROM sap_bw_message_server_data.discovery LIMIT 10;

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

---

How do I deploy the SAP BW Message 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 Message Server loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load SAP BW Message 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.

---

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