Load Edamam Food Database data to DuckDB

Edamam Food Database API provides access to a comprehensive food and grocery database with nutrition, barcode, and natural language processing capabilities. Everything needed to build a working Edamam Food Database → 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 Edamam Food Database 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 Edamam Food Database 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 Edamam Food Database 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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Edamam Food Database API at a glance

These values come from the Edamam Food Database API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Edamam Food Database API?

The API uses query parameters for authentication; you must include both your app_id and app_key in every request. Optionally, if Active User tracking is configured for your app_id, you must also provide a user-specific identifier in the Edamam-Account-User header.

1. Get your credentials

1. Navigate to the Edamam Developer Portal at developer.edamam.com and log in or create an account.\n2. Once logged in, go to the Dashboard.\n3. Navigate to the Applications section.\n4. Click the 'Create New Application' button if you do not have an existing one for the Food Database API.\n5. Select the specific API (Food Database API) and your desired plan.\n6. After creating the application, your unique 'Application ID' (app_id) and 'Application Key' (app_key) will be displayed on the application's dashboard. Ensure you record these immediately, as the app_key may not be retrievable again for security reasons.

2. Add them to .dlt/secrets.toml

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[sources.edamam_food_database_source]
app_key = "REPLACE_ME"

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 Edamam Food Database data can I load into DuckDB?

These are the Edamam Food Database endpoints dlt can load into DuckDB:

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How do I load only new Edamam Food Database records?

The Edamam Food Database API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "food_parser", "endpoint": {
    "path": "api/food-database/v2/parser",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "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 Edamam Food Database pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/food-database/v2/parser and /api/food-database/v2/nutrients from the Edamam Food Database API into DuckDB:

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

@dlt.source
def edamam_food_database_source(app_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.edamam.com",
            "auth": {"type": "api_key", "api_key": app_key, "name": "app_key", "location": "query"},
        },
        "resources": [
            {"name": "food_parser", "endpoint": {"path": "api/food-database/v2/parser", "data_selector": "hints"}},
            {"name": "food_nutrients", "endpoint": {"path": "api/food-database/v2/nutrients"}}
        ],
    }
    yield from rest_api_resources(config)


def load_edamam_food_database_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="edamam_food_database_pipeline",
        destination="duckdb",
        dataset_name="edamam_food_database_data",
    )
    load_info = pipeline.run(edamam_food_database_source())
    print(load_info)


if __name__ == "__main__":
    load_edamam_food_database_to_duckdb()

Run it with python edamam_food_database_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 Edamam Food Database 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("edamam_food_database_pipeline").dataset()
df = data.food_parser.df()
print(df.head())

SQL:

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SELECT * FROM edamam_food_database_data.food_parser LIMIT 10;

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

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How do I deploy the Edamam Food Database 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 Edamam Food Database 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 Edamam Food Database 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