pricing

DE-LU Day-Ahead Hourly Parquet

Free

Open dataset

Sample structure: 100 / 100

7 download links issued

Seller: sam oo

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Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: Not documented — confirm reuse terms with the seller
Source / creator: Not documented
Collection method: uploaded
Coverage start: Not documented
Coverage end: Not documented
Data last updated: Not documented
Update schedule: Not documented

Sample structure score: 100 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.

Check	Points	Evidence
Populated cells	50 / 50	110 of 110 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types	30 / 30	110 of 110 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape	20 / 20	10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.

Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

Field	Missing cells	Most common type	Other populated types
timestamp_utc	0 / 10	object	0 / 10
timezone	0 / 10	string	0 / 10
market	0 / 10	string	0 / 10
zone	0 / 10	string	0 / 10
country	0 / 10	string	0 / 10
price_eur_mwh	0 / 10	number	0 / 10
currency	0 / 10	string	0 / 10
unit	0 / 10	string	0 / 10
source	0 / 10	string	0 / 10
license	0 / 10	string	0 / 10
retrieved_at_utc	0 / 10	object	0 / 10

How the score is calculated, its limitations, and how to correct an assessment →

About this data

Day-ahead hourly electricity pricing data for Germany and Luxembourg from the datapoint.market relay origin.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example

python3 retrieve-dataset.py 7c3cfca0-7310-4b60-8895-d993d63b0815 --output dataset.bin

Full supplier documentation

Original supplier listing: DE-LU Parquet (origin relay) Full DE-LU day-ahead hourly parquet file for datapoint.market relay origin

Schema

Name	Type	Description
timestamp_utc	object
timezone	string
market	string
zone	string
country	string
price_eur_mwh	number
currency	string
unit	string
source	string
license	string
retrieved_at_utc	object

Sample Data

Preview a sample of the data before downloading.

Public sample only. Sign in to retrieve the full dataset, including free datasets.

For AI Agents

Via MCP Server

# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "DE-LU Day-Ahead Hourly Parquet" })
// Found: 7c3cfca0-7310-4b60-8895-d993d63b0815
get_download_url({ dataset_id: "7c3cfca0-7310-4b60-8895-d993d63b0815" })  // free — sign in with MCP OAuth first

Via REST API

# Free dataset — sign in or use your account API key:
curl https://api.databazaar.io/datasets/7c3cfca0-7310-4b60-8895-d993d63b0815/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"