Microsoft SQL Server Python API Docs | dltHub

Microsoft SQL Server Reporting Services REST API is a RESTful service for managing and retrieving report server catalog objects such as reports, folders, and data sources. The REST API base URL is https://{report-server-host}/reports/api/v1.0 and Authentication uses Windows Integrated security by default or Basic authentication if enabled on the report server..

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 Microsoft SQL Server data in under 10 minutes.

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What data can I load from Microsoft SQL Server?

Here are some of the endpoints you can load from Microsoft SQL Server:

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How do I authenticate with the Microsoft SQL Server API?

When using Basic authentication, include an Authorization: Basic <base64(username:password)> header; Windows Integrated authentication relies on the underlying OS/network context.

1. Get your credentials

1. Log in to the Windows domain that hosts the SSRS server.
2. If Basic authentication is required, open the RSReportServer.config file on the server.
3. Set <Add Key="AuthenticationType" Value="Basic"/> and restart the Report Server service.
4. Create or use an existing Windows user account; the username and password become the API credentials.
5. Ensure the account has appropriate permissions on the report server catalog.

2. Add them to .dlt/secrets.toml

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[sources.mssql_source_source]
username = "your_username_here"
password = "your_password_here"

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 Workbench:

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dlt 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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dlt 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 Microsoft SQL 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 mssql_source_pipeline.py

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

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Pipeline mssql_source_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mssql_source_data
The duckdb destination used duckdb:/mssql_source.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 mssql_source_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 catalog_items and reports from the Microsoft SQL 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 mssql_source_source(username=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{report-server-host}/reports/api/v1.0",
            "auth": {
                "type": "http_basic",
                "password": username,
            },
        },
        "resources": [
            {"name": "catalog_items", "endpoint": {"path": "reports/api/v1.0/CatalogItems", "data_selector": "value"}},
            {"name": "reports", "endpoint": {"path": "reports/api/v1.0/Reports", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mssql_source_pipeline",
        destination="duckdb",
        dataset_name="mssql_source_data",
    )
    load_info = pipeline.run(mssql_source_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("mssql_source_pipeline").dataset()
sessions_df = data.catalog_items.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mssql_source_data.catalog_items LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mssql_source_pipeline").dataset()
data.catalog_items.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 Microsoft SQL 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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Troubleshooting

Authentication Errors

• 401 Unauthorized: Occurs when credentials are missing or invalid. Ensure Windows Integrated authentication is configured or that Basic authentication is enabled and the correct Authorization header is sent.
• 403 Forbidden: The authenticated user does not have permission to access the requested resource. Verify the user’s role on the report server.

Rate Limits & Pagination

• SSRS does not enforce a strict rate limit, but large result sets are paginated using the skip and top OData query parameters. Use these parameters to retrieve data in chunks.
• If a 400 Bad Request is returned when using pagination, check that the parameters are within acceptable ranges as described in OData conventions.

Server Errors

• 500 Internal Server Error: Indicates an issue on the report server side. Review the server logs for detailed information.
• 503 Service Unavailable: The report server is temporarily unable to handle the request; retry with exponential backoff.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime 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