Load Django REST Framework data to DuckDB

Django REST Framework is a powerful and flexible toolkit for building Web APIs in Django projects. Everything needed to build a working Django REST Framework → DuckDB 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 Django REST Framework to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Django REST Framework to DuckDB and run it on dltHub

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 Django REST Framework 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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Django REST Framework API at a glance

These values come from the Django REST Framework API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Django REST Framework API?

Token authentication requires an 'Authorization' HTTP header with the format 'Token <token_value>'.

1. Get your credentials

To obtain credentials using the commonly used 'djangorestframework-api-key' library: 1. Install the package via 'pip install "djangorestframework-api-key==3.*"'. 2. Add 'rest_framework_api_key' to your 'INSTALLED_APPS' in 'settings.py'. 3. Run 'python manage.py migrate'. 4. Access the Django admin interface (typically at '/admin/'). 5. Navigate to the 'API Key Permissions' section in the admin dashboard to create, view, or revoke API keys for your service clients. Keys are displayed only once upon creation.

2. Add them to .dlt/secrets.toml

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[sources.django_rest_framework_source]
api_key = "your_generated_api_key_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 Django REST Framework data can I load into DuckDB?

These are the Django REST Framework endpoints dlt can load into DuckDB:

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How do I load only new Django REST Framework records?

Django REST Framework exposes updated_at on posts/, 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": "posts", "endpoint": {
    "path": "posts/",
    "data_selector": "results",
    "incremental": {"cursor_path": "updated_at", "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 Django REST Framework pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading api/token/ and api/token/refresh/ (commonly used for JWT-based authentication) or the standard API endpoints secured with Api-Key or Authorization headers. from the Django REST Framework API into DuckDB:

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

@dlt.source
def django_rest_framework_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "The base URL is specific to each Django deployment, typically ending in /api/ or similar (e.g., https://example.com/api/).",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "posts", "endpoint": {"path": "posts/", "data_selector": "results"}},
            {"name": "users", "endpoint": {"path": "users/", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_django_rest_framework_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="django_rest_framework_pipeline",
        destination="duckdb",
        dataset_name="django_rest_framework_data",
    )
    load_info = pipeline.run(django_rest_framework_source())
    print(load_info)


if __name__ == "__main__":
    load_django_rest_framework_to_duckdb()

Run it with python django_rest_framework_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 Django REST Framework data in DuckDB?

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("django_rest_framework_pipeline").dataset()
df = data.posts.df()
print(df.head())

SQL:

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SELECT * FROM django_rest_framework_data.posts LIMIT 10;

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

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How do I deploy the Django REST Framework to DuckDB 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 Django REST Framework 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 Django REST Framework 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