SAP BW Open Hub Message Server Python API Docs | dltHub

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. The REST API base URL is The connection is defined by host, system number, and client ID properties rather than a standard REST base URL endpoint. and The service uses Basic authentication requiring a username and password..

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 BW Open Hub Message Server data in under 10 minutes.

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What data can I load from SAP BW Open Hub Message Server?

Here are some of the endpoints you can load from SAP BW Open Hub Message Server:

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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 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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI harness:

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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:

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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 BW Open Hub Message 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:

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python sap_bw_open_hub_message_server_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline sap_bw_open_hub_message_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.

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Python pipeline example

This example loads RSB_API_OHS_DEST_GETLIST and RSB_API_OHS_DEST_READ_DATA from the SAP BW Open Hub Message Server API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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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 get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_bw_open_hub_message_server_pipeline",
        destination="duckdb",
        dataset_name="sap_bw_open_hub_message_server_data",
    )
    load_info = pipeline.run(sap_bw_open_hub_message_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.

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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):

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import dlt

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

SQL (DuckDB example):

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SELECT * FROM sap_bw_open_hub_message_server_data.open_hub_destinations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

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

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load SAP BW Open Hub Message 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.

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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.

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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