We extract product catalogues, regional price variations, weekly flyers, ingredients, and store locations from edeka.de. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Master Data objects from edeka.de. All fields typed and schema-versioned.
"ean": "4311501317134", "sku": "123456", "title": "EDEKA Bio Haferdrink Natur", "brand": "EDEKA Bio", "category": "Milch & Milchalternativen", "base_price": "1.19 EUR / 1 l", "current_price": 1.19, "is_private_label": true
| # | ean | sku | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Nutritional & Ingredients objects from edeka.de. All fields typed and schema-versioned.
"ean": "4311501317134", "ingredients_text": "Wasser, 11% HAFER, Sonnenblumenöl, Meersalz.", "allergens": "Glutenhaltiges Getreide", "nutri_score": "B", "vegan_label": true, "energy_kcal": 42, "fat_g": 1.5, "carbs_g": 6.5
| # | ean | ingredients_text | allergens | nutri_score | vegan_label | vegetarian_label |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Regional Pricing & Offers objects from edeka.de. All fields typed and schema-versioned.
"store_id": "1234", "ean": "4311501317134", "regular_price": 1.19, "offer_price": 0.99, "discount_pct": 16.8, "offer_start_date": "2024-10-21", "offer_end_date": "2024-10-26", "is_app_deal": false
| # | store_id | ean | regular_price | offer_price | discount_pct | offer_start_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from edeka.de. All fields typed and schema-versioned.
"store_id": "1234", "store_name": "EDEKA Center Kruse", "address_city": "Berlin", "address_zip": "10437", "latitude": 52.5401, "longitude": 13.4182, "has_bakery": true, "opening_hours": "Mo-Sa 07:00-22:00"
| # | store_id | store_name | address_street | address_city | address_zip | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Category Results objects from edeka.de. All fields typed and schema-versioned.
"keyword": "hafermilch", "position": 2, "ean": "4311501317134", "title": "EDEKA Bio Haferdrink Natur", "price": 1.19, "bio_badge": true, "vegan_badge": true, "scraped_at": "2024-10-21T08:12:00Z"
| # | keyword | category_id | position | ean | title | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Edeka scraper handles regional pricing, weekly offers, and complex nutritional tables with geo-fenced session management built in.
Title, weight, images, category mapping, and description scraped at SKU level for the entire catalogue.
Prices map to specific store IDs. We manage the session state to extract local pricing across Germany.
Extract energy, macronutrients, ingredient lists, and allergens formatted as clean structured data.
Capture weekly flyer data, start and end dates, discount percentages, and promotional mechanics.
Extract geo-coordinates, facilities, and opening hours for all Edeka markets.
Track Gut&Günstig and EDEKA Bio product lines against national A-brands.
Capture vegan, vegetarian, gluten-free, and regional product badges.
Extract and standardise price per 100g, 1kg, or 1L for accurate cross-product comparison.
Identify exclusive discounts available only through the Edeka App.
Brief in. Clean data out.
Provide categories, postcodes, or specific Edeka store IDs. We design the extraction schema together.
We configure Scrapy crawlers, geo-fenced session management, and rate-limit circumvention.
Nutritional field validation, price anomaly detection, and schema checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Grocery scraping is a geo-spatial problem. Here is how we maintain data accuracy across thousands of local markets.
Edeka prices and availability vary by store. We maintain distinct browser sessions with specific store cookies and location headers to extract accurate regional pricing rather than national defaults.
Modern frontend frameworks obscure data in the DOM. We intercept backend API calls and Next.js data props directly, yielding cleaner data and faster extraction times.
Frequent requests trigger bot protection. We route traffic through German residential IP pools, distributing requests across thousands of nodes to maintain high throughput.
Fresh produce often lacks standard EANs and relies on weight-based pricing. We normalise these variations into standard base prices for consistent comparative analysis.
Ingredient and allergen texts are often unstructured. We parse and clean these strings, mapping them to standard arrays suitable for immediate database ingestion.
Brands track retail prices across regions to monitor promotional compliance and competitor activity.
Analysts track defined grocery basket costs over time to measure real-world inflation rates.
Health applications ingest macro data, Nutri-Score, and allergen info to power dietary tracking tools.
Retail analysts measure private label penetration against national brands at the category level.
Brands map weekly discount cycles to optimise their own trade promotion spend.
Logistics teams map store density and facility distribution to plan supply chain routes.
"Edeka's regional pricing model means a single SKU holds hundreds of price points across Germany. Extracting this requires precise session management, not simple HTTP GETs."
Grocery scraping is fundamentally a geo-spatial problem. Prices, availability, and weekly offers on edeka.de vary block by block. DataFlirt orchestrates thousands of location-specific sessions, normalising base prices and nutritional data into a clean, warehouse-ready schema.
Everything supported by our edeka.de scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Traffic routed exclusively through German residential IP pools to match local store context and avoid regional blocking.
Direct extraction from backend APIs and frontend data props bypasses brittle DOM parsing and increases pipeline speed.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About edeka.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from edeka.de is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and nutritional data. We do not extract personal data or violate GDPR.
We programmatically set store context via cookies and HTTP headers. You provide the target store IDs or postcodes, and we extract the exact prices visible to local shoppers.
Yes. We extract complete macronutrient tables, energy values, ingredient lists, allergens, and Nutri-Score ratings for every applicable SKU.
Pipelines can run daily for weekly offer tracking or weekly for full catalogue refreshes. Frequency is configurable based on your requirements.
Yes. We track Gut&Günstig, EDEKA Bio, and other private labels, allowing you to compare their pricing and placement against national brands.
Yes. We provide a sample run of up to 500 SKUs from a specific store ID to validate schema fit and data quality before contract signing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off nutritional catalogue dump or continuous regional price monitoring — we scope, build, and operate the pipeline. Tell us what you need.
