Load MySQL data to Microsoft Fabric

MySQL REST Service is a RESTful HTTPS service that exposes MySQL tables, views, and procedures as JSON document REST endpoints. Everything needed to build a working MySQL → 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 MySQL 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 MySQL 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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MySQL API at a glance

These values come from the MySQL API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the MySQL API?

Authentication is performed by POSTing credentials to the <service_base_url>/authentication/login endpoint, where the response provides a JWT token to be used in the Authorization: Bearer header for subsequent requests. Session cookie authentication is also supported by utilizing the Set-Cookie response header from the login request.

1. Get your credentials

To obtain credentials for the MySQL REST Service (MRS), first ensure an authentication app (such as 'MRS' or 'MySQL') is linked to your REST service. In the MySQL Workbench management interface or your admin dashboard, navigate to 'REST Authentication Apps' under your service connection. Right-click to add a new authentication app, then add a user by right-clicking the app entry and selecting 'Add User'. Provide a username and password to create the credentials required for the login endpoint.

2. Add them to .dlt/secrets.toml

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[sources.mysql_source]
username = "your_username"
password = "your_password"
# Use these in your dlt client configuration for authentication via credentials endpoint.

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 MySQL data can I load into Microsoft Fabric?

These are the MySQL endpoints dlt can load into Microsoft Fabric:

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How do I load only new MySQL records?

MySQL exposes limit/offset or query-based pagination parameters on /{schema}/{object}, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "table_rows", "endpoint": {
    "path": "/{schema}/{object}",
    "incremental": {"cursor_path": "limit/offset or query-based pagination parameters", "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 MySQL pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading authentication/login and authentication/logout from the MySQL API into Microsoft Fabric:

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

@dlt.source
def mysql_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<HOST>:<PORT>",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "table_rows", "endpoint": {"path": "/{schema}/{object}"}},
            {"name": "row_by_id", "endpoint": {"path": "/{schema}/{object}/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def load_mysql_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mysql_pipeline",
        destination="fabric",
        dataset_name="mysql_data",
    )
    load_info = pipeline.run(mysql_source())
    print(load_info)


if __name__ == "__main__":
    load_mysql_to_fabric()

Run it with uv run python mysql_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 MySQL 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("mysql_pipeline").dataset()
df = data.table_rows.df()
print(df.head())

SQL:

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SELECT * FROM mysql_data.table_rows LIMIT 10;

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

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How do I deploy the MySQL 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 MySQL 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 MySQL 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