Quandl Python API Docs | dltHub

Nasdaq Data Link (formerly Quandl) is a REST API platform for discovering and programmatically downloading financial, economic and alternative datasets (time-series datasets and table/datatable resources). The REST API base URL is https://data.nasdaq.com/api/v3 and all requests require your API key (api_key) — supplied as a query parameter or in authorized client calls..

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 Quandl data in under 10 minutes.

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What data can I load from Quandl?

Here are some of the endpoints you can load from Quandl:

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How do I authenticate with the Quandl API?

Authentication is performed with an API key. The simplest method is adding api_key=YOURAPIKEY as a query parameter on requests. Many documented examples use the api_key query parameter for both dataset and tables endpoints.

1. Get your credentials

1. Sign in or create an account at https://data.nasdaq.com.
2. Go to your account settings / API key (often “Account” → “API Key” or Dashboard → API Key).
3. Copy the displayed API key.
4. Use that value as api_key in requests or set it in your client library configuration.

2. Add them to .dlt/secrets.toml

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[sources.quandl_source]
api_key = "YOUR_API_KEY_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 Quandl 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 quandl_pipeline.py

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

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Pipeline quandl_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset quandl_data
The duckdb destination used duckdb:/quandl.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 quandl_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 datasets and datatables from the Quandl 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 quandl_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://data.nasdaq.com/api/v3",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "datasets/{database_code}/{dataset_code}.json", "data_selector": "dataset"}},
            {"name": "datatables", "endpoint": {"path": "datatables/{datatable_code}.json", "data_selector": "datatable.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="quandl_pipeline",
        destination="duckdb",
        dataset_name="quandl_data",
    )
    load_info = pipeline.run(quandl_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("quandl_pipeline").dataset()
sessions_df = data.datatables.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM quandl_data.datatables LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("quandl_pipeline").dataset()
data.datatables.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 Quandl 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 you receive HTTP 401 or responses indicating missing credentials, verify you included your API key. The safest approach is to add ?api_key=YOURAPIKEY to the request URL. Ensure the key is active in your Nasdaq Data Link account.

Rate limits and bulk‑download limits

The Tables bulk‑export feature is limited (examples: 60 bulk downloads/hour for subscribers). Large requests are subject to row limits (10,000 rows per normal call). For very large tables use qopts.export=true (bulk/export) or use cursor‑based pagination (next_cursor_id) to iterate pages.

Pagination and large‑result behavior

Standard Tables API returns up to 10,000 rows per call. For pagination the response contains next_cursor_id; pass qopts.cursor_id (or qopts.cursor_id=...) to fetch next pages until next_cursor_id is null. For datasets data endpoints, use start_date/end_date, limit/per_page parameters where supported; for datatables use qopts and cursor_id. Use qopts.export=true to create an asynchronous bulk export (returns datatable_bulk_download.file.link).

Common API errors / messages

• 401 Unauthorized: missing/invalid api_key
• 403 Forbidden: subscription required for premium dataset
• 404 Not Found: dataset/datatable not found
• 413 Request Entity Too Large or query too long: "Your request query parameters are too long."
• 429 Too Many Requests: rate limit exceeded (throttle bulk and API calls)
• 500 Server Error: intermittent server failure; retry/backoff
• Error message when query returns too many results: "Your query will return too many results. Please apply some filters and try again." — use qopts.export=true or narrower filters.

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