Plotly Dash Python API Docs | dltHub

Plotly Dash is a Python framework for building interactive web applications with data visualizations. The REST API base URL is `` and Dash has no built‑in authentication; auth is handled by the hosting environment or Dash Enterprise extensions..

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 Plotly Dash data in under 10 minutes.

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What data can I load from Plotly Dash?

Here are some of the endpoints you can load from Plotly Dash:

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How do I authenticate with the Plotly Dash API?

If authentication is needed, it uses standard Flask mechanisms (e.g., session cookies, HTTP Basic, or bearer tokens configured by the deployment).

1. Get your credentials

Not applicable – Dash does not provide API credentials; authentication is configured in the hosting environment or through Dash Enterprise tools.

2. Add them to .dlt/secrets.toml

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[sources.plotly_dash_source]

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 Plotly Dash 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 plotly_dash_pipeline.py

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

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Pipeline plotly_dash_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset plotly_dash_data
The duckdb destination used duckdb:/plotly_dash.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 plotly_dash_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 callback_api and datatable from the Plotly Dash 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 plotly_dash_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "",
                "": ,
            },
        },
        "resources": [
            {"name": "callback_api", "endpoint": {"path": "my_callback"}},
            {"name": "datatable", "endpoint": {"path": "datatable", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="plotly_dash_pipeline",
        destination="duckdb",
        dataset_name="plotly_dash_data",
    )
    load_info = pipeline.run(plotly_dash_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("plotly_dash_pipeline").dataset()
sessions_df = data.callback_api.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM plotly_dash_data.callback_api LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("plotly_dash_pipeline").dataset()
data.callback_api.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 Plotly Dash 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

Dash itself does not enforce authentication. If the underlying Flask app uses authentication, typical 401/403 errors will be returned. Ensure the correct Flask auth mechanism (e.g., session cookie or token) is configured.

Rate Limiting / Throttling

Dash does not implement built‑in rate limiting. Any limits are imposed by the hosting platform (e.g., gunicorn, AWS API Gateway). Monitor HTTP 429 responses from those layers.

Pagination Quirks

The DataTable component uses page_count and page_size for backend pagination. Missing or mismatched values can cause empty responses or UI errors.

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"page_count represents the number of the pages in the paginated table. This is really only useful when performing backend pagination..."
"page_size represents the number of rows that will be displayed on a particular page when page_action is 'custom' or 'native'."

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