Wandisco Live Data Migrator Python API Docs | dltHub

Wandisco Live Data Migrator is a REST API for managing data migration tasks and filesystem targets. The REST API base URL is http://<ldm-hostname>:18080 and All requests require either Basic Authentication (username/password) or a Bearer JWT token..

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 Wandisco Live Data Migrator data in under 10 minutes.

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What data can I load from Wandisco Live Data Migrator?

Here are some of the endpoints you can load from Wandisco Live Data Migrator:

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How do I authenticate with the Wandisco Live Data Migrator API?

Use HTTP Basic authentication by providing username and password (e.g., curl -u user:pass). Alternatively, POST to /authentication/authenticate with JSON {"userName":"...","password":"..."} to receive a JWT, then include it as 'Authorization: Bearer ' in subsequent calls.

1. Get your credentials

Contact your Wandisco Live Data Migrator administrator to obtain a username and password for Basic auth, or to receive a JWT generation endpoint token. No self‑service dashboard is described in the public docs.

2. Add them to .dlt/secrets.toml

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[sources.wandisco_live_data_migrator_source]
bearer_token = "your_jwt_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 Wandisco Live Data Migrator 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 wandisco_live_data_migrator_pipeline.py

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

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Pipeline wandisco_live_data_migrator_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wandisco_live_data_migrator_data
The duckdb destination used duckdb:/wandisco_live_data_migrator.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 wandisco_live_data_migrator_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 stats and fs_targets from the Wandisco Live Data Migrator 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 wandisco_live_data_migrator_source(bearer_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<ldm-hostname>:18080",
            "auth": {
                "type": "bearer",
                "token": bearer_token,
            },
        },
        "resources": [
            {"name": "stats", "endpoint": {"path": "stats"}},
            {"name": "fs_targets", "endpoint": {"path": "fs/targets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wandisco_live_data_migrator_pipeline",
        destination="duckdb",
        dataset_name="wandisco_live_data_migrator_data",
    )
    load_info = pipeline.run(wandisco_live_data_migrator_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("wandisco_live_data_migrator_pipeline").dataset()
sessions_df = data.stats.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wandisco_live_data_migrator_data.stats LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wandisco_live_data_migrator_pipeline").dataset()
data.stats.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 Wandisco Live Data Migrator 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 failures

• If credentials are missing or incorrect, the API returns HTTP 401 Unauthorized. Verify that the username/password or JWT token is valid and that the Authorization header is correctly formatted.

Rate limiting

• The API may limit request rates and respond with HTTP 429 Too Many Requests. Implement retry logic with exponential backoff when this status is received.

Pagination

• List endpoints can return large collections. When a response includes a nextPageToken (or similar) field, use its value as a query parameter on the next request to retrieve subsequent pages. The exact pagination scheme is documented in the runtime Swagger UI.

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