SAP BW Message Server Python API Docs | dltHub

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. The REST API base URL is The base URL is constructed based on the system installation as <Protocol>://<Host>:<Port>/sap/bw or <Protocol>://<Host>:<Port>/sap/opu/odata/sap/. and Requests generally use Basic Authentication, though system-specific SSO may also be supported via the underlying SAP NetWeaver infrastructure..

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 Message Server data in under 10 minutes.

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What data can I load from SAP BW Message Server?

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

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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

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[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 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 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_message_server_pipeline.py

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

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Pipeline sap_bw_message_server_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset sap_bw_message_server_data
The duckdb destination used duckdb:/sap_bw_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_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 /sap/bw/ and /sap/opu/odata/ from the SAP BW 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_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 get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="sap_bw_message_server_pipeline",
        destination="duckdb",
        dataset_name="sap_bw_message_server_data",
    )
    load_info = pipeline.run(sap_bw_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_message_server_pipeline").dataset()
sessions_df = data.discovery.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM sap_bw_message_server_data.discovery LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("sap_bw_message_server_pipeline").dataset()
data.discovery.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 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