We extract recipe metadata, ingredient lists, step-by-step instructions, nutritional profiles, and dietary classifications from Yummly. 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 Recipe Metadata objects from yummly.com. All fields typed and schema-versioned.
"recipe_id": "Spicy-Garlic-Shrimp-938214", "title": "Spicy Garlic Shrimp", "prep_time_minutes": 10, "cook_time_minutes": 15, "servings": 4, "rating_value": 4.8, "rating_count": 1240, "primary_image_url": "https://lh3.googleusercontent.com/..."
| # | recipe_id | title | author_name | author_url | prep_time_minutes | cook_time_minutes |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Ingredients objects from yummly.com. All fields typed and schema-versioned.
"recipe_id": "Spicy-Garlic-Shrimp-938214", "ingredient_line": "2 tablespoons olive oil", "quantity": 2.0, "unit": "tablespoons", "ingredient_name": "olive oil", "is_optional": false, "preparation_notes": "None"
| # | recipe_id | ingredient_line | ingredient_raw | quantity | unit | ingredient_name |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Nutritional Profile objects from yummly.com. All fields typed and schema-versioned.
"recipe_id": "Spicy-Garlic-Shrimp-938214", "calories": 245, "fat_g": 12.5, "carbohydrates_g": 4.2, "protein_g": 28.4, "sodium_mg": 840, "sugar_g": 1.1
| # | recipe_id | calories | fat_g | saturated_fat_g | carbohydrates_g | sugar_g |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Dietary Tags objects from yummly.com. All fields typed and schema-versioned.
"recipe_id": "Spicy-Garlic-Shrimp-938214", "cuisines": "['Italian', 'Mediterranean']", "courses": "['Main Dishes']", "diets": "['Pescatarian', 'Keto', 'Gluten-Free']", "allergies": "['Seafood', 'Shellfish']", "tastes_salty": 0.6, "tastes_piquant": 0.8
| # | recipe_id | cuisines | courses | diets | allergies | holidays |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from yummly.com. All fields typed and schema-versioned.
"review_id": "rev_9847291", "recipe_id": "Spicy-Garlic-Shrimp-938214", "user_name": "ChefAnna22", "rating": 5, "review_text": "Added extra red pepper flakes. Perfect weeknight dinner.", "made_it_flag": true, "created_at": "2026-03-14T18:22:00Z"
| # | review_id | recipe_id | user_id | user_name | rating | review_text |
|---|---|---|---|---|---|---|
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Our Yummly scraper handles the complex React frontend, extracting deeply nested nutritional data, ingredient arrays, and dietary classifications with automated pagination and bot circumvention.
Title, prep times, cook times, yields, step-by-step instructions, and high-resolution media URLs scraped per recipe.
Capture macro and micro-nutrients including calories, fats, proteins, vitamins, and sodium levels mapped to standard schemas.
Extract raw ingredient strings and split them into distinct quantity, unit, and ingredient name fields for database insertion.
Extract Yummly's proprietary dietary classifications, allergen warnings, and cuisine mappings for every dish.
Paginate through user reviews to capture ratings, text feedback, 'made it' flags, and user uploaded photos.
Input keywords or category URLs to extract all matching recipes, sorted by relevance, popularity, or time.
Capture Yummly's unique taste metric scores (salty, savory, sour, bitter, sweet, piquant) for flavour profiling.
Monitor recipe catalogues for new additions, rating changes, or updated nutritional profiles without re-scraping the entire site.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences to keep your database fresh.
Brief in. Clean data out.
Provide search terms, category URLs, or dietary filters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for yummly.com.
Schema validation, null-rate checks, and ingredient parsing verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from modern React applications requires more than simple HTTP requests. Here is how we maintain pipeline stability.
Food portals deploy rate limiting and bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain uninterrupted access.
Yummly relies heavily on client-side rendering. We run full Playwright browser sessions to execute JavaScript, trigger lazy-loaded images, and hydrate nutritional data modules.
Frontend layouts change. Our selector strategy uses multiple fallback chains per field, including structured data extraction (LD+JSON), ensuring layout updates do not break your pipeline.
Ingredient lines are often messy strings. We apply parsing logic to separate quantities, units, and core ingredients, delivering clean tabular data ready for your application.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and coverage drops, responding before you notice any missing data.
App developers populate their databases with structured recipes, nutritional profiles, and dietary tags to power user meal plans.
Health tech companies aggregate macro and micro-nutrient data across thousands of recipes to build dietary recommendation engines.
FMCG brands analyse trending ingredients, popular cuisines, and user ratings to inform new product development.
Machine learning teams use structured ingredient lists and step-by-step instructions to train recipe generation models.
Retailers map parsed ingredient lists to their product catalogues, enabling one-click 'add to cart' functionality for recipes.
Food publishers monitor Yummly search rankings and review velocity to optimise their own content strategies.
"Yummly represents one of the largest structured culinary graphs available, but mapping its ingredients and nutritional profiles into queryable formats requires dedicated infrastructure."
Food tech teams underestimate the complexity of recipe extraction: normalising unit measurements, parsing dynamic React components, and bypassing rate limits require constant maintenance. DataFlirt absorbs that complexity so your engineers can focus on product development, not scraping infrastructure.
Everything supported by our yummly.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About yummly.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available recipe and nutritional information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract personal user data or circumvent authentication walls for paid Yummly Pro content. Clients should review Yummly's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate limit spikes in real time and trigger pool rotation automatically.
Yes. We apply parsing logic to raw ingredient strings to separate them into discrete fields: quantity, unit of measurement, ingredient name, and preparation notes, making the data immediately usable in relational databases.
We can configure pipelines to run at daily, weekly, or monthly cadences depending on your requirements. Change detection ensures we only process updated or new recipes after the initial historical load.
Our smallest packages start at a defined category or keyword set extraction with weekly delivery. For full catalogue extraction or custom schema requirements, we price based on volume and delivery frequency.
Yes. We extract the specific taste metrics (salty, savory, sour, bitter, sweet, piquant) that Yummly calculates for recipes, provided they are publicly visible on the recipe page.
Absolutely. We provide a sample run of up to 500 recipes as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and ingredient parsing quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off recipe catalogue dump or a continuous feed of new culinary content, we scope, build, and operate the pipeline. Tell us what you need.
