Load SAP BW Open Hub Message Server data to Microsoft Fabric

SAP BW Open Hub is a service that allows extracting data from an SAP BW system to non-SAP systems using ABAP-based destination APIs or RFC-based connectors for third-party tools. Everything needed to build a working SAP BW Open Hub 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.

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Build your SAP BW Open Hub 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 Open Hub 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.

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SAP BW Open Hub Message Server API at a glance

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values come from the SAP BW Open Hub Message Server API reference — the authoritative source if anything here looks out of date.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

Authentication is typically handled via SAP user credentials (username and password) using Basic authentication, often requiring the SAP .NET Connector 3.0 installed on an Integration Runtime or equivalent middleware.

1. Get your credentials

SAP BW Open Hub does not use an "API key" for authentication in the conventional REST sense. Instead, it typically uses SAP Basic Authentication (Username/Password). To obtain credentials:

1. Contact your SAP Basis administrator to create a dedicated SAP user account for the integration.
2. Ensure the user has the necessary authorizations for the specific Open Hub Destination and RFC access.
3. Obtain the connection details from your SAP landscape: Message Server Host, Message Server Service (or port), System ID, Logon Group, and Client ID.
4. Configure these in your dlt project as secure credentials.

2. Add them to .dlt/secrets.toml

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[sources.sap_bw_open_hub_message_server_source]
user_name = "your_sap_username"
password = "your_sap_password"
message_server = "your_message_server_host"
message_server_service = "sapmsXX" # or port number
system_id = "SID"
logon_group = "your_logon_group"
client_id = "100"

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 Open Hub Message Server data can I load into Microsoft Fabric?

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

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

The SAP BW Open Hub 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.

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{"name": "open_hub_destinations", "endpoint": {
    "path": "RSB_API_OHS_DEST_GETLIST",
    # 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 Open Hub Message Server pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading RSB_API_OHS_DEST_GETLIST and RSB_API_OHS_DEST_READ_DATA from the SAP BW Open Hub Message 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_open_hub_message_server_source(password=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The connection is defined by host, system number, and client ID properties rather than a standard REST base URL endpoint.",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": password},
        },
        "resources": [
            {"name": "open_hub_destinations", "endpoint": {"path": "RSB_API_OHS_DEST_GETLIST"}},
            {"name": "read_data", "endpoint": {"path": "RSB_API_OHS_DEST_READ_DATA"}}
        ],
    }
    yield from rest_api_resources(config)


def load_sap_bw_open_hub_message_server_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_bw_open_hub_message_server_pipeline",
        destination="fabric",
        dataset_name="sap_bw_open_hub_message_server_data",
    )
    load_info = pipeline.run(sap_bw_open_hub_message_server_source())
    print(load_info)


if __name__ == "__main__":
    load_sap_bw_open_hub_message_server_to_fabric()

Run it with python sap_bw_open_hub_message_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 Open Hub 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):

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import dlt

data = dlt.pipeline("sap_bw_open_hub_message_server_pipeline").dataset()
df = data.open_hub_destinations.df()
print(df.head())

SQL:

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SELECT * FROM sap_bw_open_hub_message_server_data.open_hub_destinations 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 Open Hub 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 Open Hub Message 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 Open Hub 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.

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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