Load Microsoft SQL Server data to BigQuery

The Azure SQL Database REST API enables programmatic management of Azure SQL database resources within the Azure ecosystem. Everything needed to build a working Microsoft SQL Server → BigQuery 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 Microsoft SQL Server to BigQuery 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 Microsoft SQL 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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Microsoft SQL Server API at a glance

These values come from the Microsoft SQL Server API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Microsoft SQL Server API?

Azure SQL Management APIs use Azure Active Directory OAuth2 flows, typically requiring an Authorization header with a Bearer token. Requests must include an api-version parameter.

1. Get your credentials

Microsoft SQL Server does not have a native, monolithic REST API for general data access. For applications requiring RESTful interaction, the industry-standard approach is to deploy Azure Data API builder (DAB). To set up access: 1. Install or deploy Data API builder (via Docker or binary). 2. Configure the 'dab-config.json' file with your SQL Server connection string (e.g., 'Server=myServer;Database=myData;User Id=myUser;Password=myPassword;'). 3. Define the entities (tables, views, or stored procedures) you wish to expose. 4. Run the engine to automatically generate REST endpoints. Authentication for these endpoints is typically managed via integration with Microsoft Entra ID (formerly Azure AD) or via custom authentication headers configured in the DAB runtime.

2. Add them to .dlt/secrets.toml

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[sources.microsoft_sql_server_source]
sql_server_connection_string = "Server=tcp:your-server.database.windows.net,1433;Initial Catalog=your-db;User ID=your-user;Password=your-password;Encrypt=true;TrustServerCertificate=false;Connection Timeout=30;"
dab_api_key = "your_api_key_or_access_token_here"

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 Microsoft SQL Server data can I load into BigQuery?

These are the Microsoft SQL Server endpoints dlt can load into BigQuery:

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How do I load only new Microsoft SQL Server records?

The Microsoft SQL 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": "azure_sql_databases", "endpoint": {
    "path": "subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Sql/servers/{serverName}/databases",
    # 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 Microsoft SQL Server pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading v1/query and v1/procedure from the Microsoft SQL Server API into BigQuery:

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

@dlt.source
def microsoft_sql_server_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://management.azure.com",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "azure_sql_databases", "endpoint": {"path": "subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Sql/servers/{serverName}/databases", "data_selector": "value"}},
            {"name": "azure_sql_servers", "endpoint": {"path": "subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Sql/servers", "data_selector": "value"}}
        ],
    }
    yield from rest_api_resources(config)


def load_microsoft_sql_server_to_bigquery() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="microsoft_sql_server_pipeline",
        destination="bigquery",
        dataset_name="microsoft_sql_server_data",
    )
    load_info = pipeline.run(microsoft_sql_server_source())
    print(load_info)


if __name__ == "__main__":
    load_microsoft_sql_server_to_bigquery()

Run it with uv run python microsoft_sql_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 Microsoft SQL Server data in BigQuery?

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("microsoft_sql_server_pipeline").dataset()
df = data.azure_sql_databases.df()
print(df.head())

SQL:

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SELECT * FROM microsoft_sql_server_data.azure_sql_databases LIMIT 10;

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

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How do I deploy the Microsoft SQL Server to BigQuery 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 Microsoft SQL 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 Microsoft SQL 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