We extract structured recipes, nutritional profiles, ingredient lists, dietary tags, and user reviews from Epicurious. 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 Recipes objects from epicurious.com. All fields typed and schema-versioned.
"recipe_id": "epi_847291", "title": "Classic Beef Wellington", "author": "Gordon Ramsay", "prep_time": "45 mins", "total_time": "2 hrs 30 mins", "rating": 4.8, "review_count": 1492, "yield": "Serves 6", "difficulty": "Advanced"
| # | recipe_id | title | author | prep_time | cook_time | total_time |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Ingredients objects from epicurious.com. All fields typed and schema-versioned.
"recipe_id": "epi_847291", "ingredient_name": "puff pastry", "quantity": 500, "unit": "grams", "preparation_note": "thawed if frozen", "raw_text": "500g all-butter puff pastry, thawed if frozen", "allergen_flag": "gluten"
| # | recipe_id | ingredient_id | ingredient_name | quantity | unit | preparation_note |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Nutrition objects from epicurious.com. All fields typed and schema-versioned.
"recipe_id": "epi_847291", "calories": 840, "fat_g": 54.2, "saturated_fat_g": 22.1, "carbohydrates_g": 38.5, "protein_g": 45.3, "sodium_mg": 920
| # | recipe_id | calories | fat_g | saturated_fat_g | carbohydrates_g | dietary_fiber_g |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Instructions objects from epicurious.com. All fields typed and schema-versioned.
"recipe_id": "epi_847291", "step_number": 3, "instruction_text": "Sear the beef fillet on all sides until browned.", "equipment_needed": "cast iron skillet", "technique_tag": "searing", "temperature_c": 200
| # | recipe_id | step_number | instruction_text | equipment_needed | image_url | video_timestamp |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews objects from epicurious.com. All fields typed and schema-versioned.
"review_id": "rev_993821", "recipe_id": "epi_847291", "star_rating": 5, "review_text": "Followed the instructions exactly. The duxelles was perfect.", "helpful_votes": 42, "make_again_pct": 100, "date_posted": "2023-11-24"
| # | review_id | recipe_id | user_name | star_rating | review_text | date_posted |
|---|---|---|---|---|---|---|
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Our Epicurious scraper handles complex recipe schemas, fractional ingredient normalisation, and dynamic review pagination while bypassing strict publisher bot defences.
Title, yield, prep time, cook time, and total time extracted and normalised into standard duration formats.
Raw ingredient strings parsed into discrete quantity, unit, and preparation note fields for database ingestion.
Extract macro and micro nutritional data points, including calories, fat, protein, and sodium per serving.
Capture user reviews, star ratings, helpful votes, and 'would make again' percentages across paginated endpoints.
Map recipes to vegan, vegetarian, keto, gluten-free, and other specific dietary classifications.
Extract metadata for recipe creators, test kitchen contributors, and linked editorial articles.
Scrape curated recipe collections, holiday menus, and seasonal roundups maintaining hierarchical relationships.
Capture high-resolution image URLs, video embed links, and associated thumbnail assets per recipe.
Run one-off bulk exports or configure continuous pipelines at weekly cadences to capture new publications.
Brief in. Clean data out.
Provide category URLs, dietary filters, or author profiles. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for epicurious.com.
Schema validation, null-rate checks, ingredient parsing accuracy, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Conde Nast employs strict scraping detection across its properties. Here is how we maintain reliable access to Epicurious data.
Epicurious uses enterprise bot protection that blocks standard HTTP clients. We utilise residential ISP proxies combined with Playwright browser sessions to spoof legitimate TLS fingerprints and interaction patterns.
While we scrape the DOM for reviews and comments, we extract core recipe metadata directly from embedded LD+JSON schemas, ensuring high accuracy for prep times, yields, and ingredient lists.
Recipes often use non-standard unicode fractions and mixed measurement systems. Our pipeline includes post-processing steps to normalise quantities into decimal formats and standard metric/imperial units.
User comments and reviews are loaded dynamically via JavaScript as the user scrolls. Our Playwright scripts handle infinite scroll events and pagination tokens to capture the complete review corpus.
Publisher sites frequently redesign their templates. We monitor extraction yields per field and alert on null-rate spikes, allowing us to update selectors before data quality degrades.
Grocery delivery services map structured ingredient lists to their inventory databases for automated cart population.
Health and fitness applications ingest macro and micro nutritional profiles to expand their searchable food databases.
Machine learning teams train culinary LLMs on high-quality, professionally tested recipe instructions and ingredient pairings.
Food industry analysts track trending ingredients, seasonal flavour profiles, and popular dietary categories based on publication frequency.
Culinary publishers analyse competitor recipe structures, review volumes, and keyword targeting to optimise their own content.
Founders building smart kitchen appliances use structured cooking times and temperature data to program device presets.
"Epicurious holds decades of professionally tested recipes and user feedback, forming the ultimate culinary dataset for food tech applications."
Extracting recipe data at scale requires more than simple HTTP requests. Conde Nast employs aggressive bot protection, and recipe structures vary wildly across decades of archives. DataFlirt manages the residential proxies, JavaScript rendering, and schema normalisation so your data science team can focus on culinary insights.
Everything supported by our epicurious.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 epicurious.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available recipe and nutritional data is generally permissible. DataFlirt targets only public, non-authenticated content. We do not extract personal user data or circumvent authentication walls. Clients should review publisher terms of service 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. This allows us to access public content reliably without triggering automated blocks.
Yes. Our pipeline includes parsing logic to convert raw strings like '1 1/2 cups' or '½ tsp' into structured numerical quantities and standard units, making the data immediately usable for database insertion.
We extract all nutritional data points provided by the publisher on the recipe page. If Epicurious has not calculated macros for a specific vintage recipe, those fields will return null.
For recipe catalogues, we typically recommend weekly or monthly delta runs to capture newly published content and updated reviews, reducing unnecessary compute costs.
Yes. 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 data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full historical recipe archive or continuous updates for new publications, we scope, build, and operate the pipeline. Tell us what you need.
