We extract recipe instructions, ingredient lists, macronutrient data, prep times, and user ratings from Simply Recipes. 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 Core objects from simplyrecipes.com. All fields typed and schema-versioned.
"url": "https://www.simplyrecipes.com/recipes/classic_baked_chicken/", "title": "Classic Baked Chicken", "author": "Elise Bauer", "publish_date": "2023-04-12T14:30:00Z", "prep_time_minutes": 15, "cook_time_minutes": 45, "yield": "4 servings", "rating": 4.8
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Complete list of extractable fields for Ingredients objects from simplyrecipes.com. All fields typed and schema-versioned.
"recipe_url": "https://www.simplyrecipes.com/recipes/classic_baked_chicken/", "ingredient_raw": "2 tablespoons unsalted butter, melted", "quantity": 2.0, "unit": "tablespoon", "ingredient_name": "unsalted butter", "preparation_notes": "melted", "ingredient_group": "Marinade"
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Complete list of extractable fields for Instructions objects from simplyrecipes.com. All fields typed and schema-versioned.
"recipe_url": "https://www.simplyrecipes.com/recipes/classic_baked_chicken/", "step_number": 3, "step_text": "Bake in the preheated oven until the internal temperature reaches 165F.", "step_image_url": "https://www.simplyrecipes.com/thmb/baked-chicken-step3.jpg", "temperature_celsius": 190.0, "duration_minutes": 45, "section_name": "Baking"
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Complete list of extractable fields for Nutritional Profile objects from simplyrecipes.com. All fields typed and schema-versioned.
"recipe_url": "https://www.simplyrecipes.com/recipes/classic_baked_chicken/", "serving_size": "1 piece", "calories": 320, "total_fat_g": 18.5, "carbohydrates_g": 2.1, "protein_g": 34.2, "sodium_mg": 450, "fiber_g": 0.5
| # | recipe_url | serving_size | calories | total_fat_g | saturated_fat_g | cholesterol_mg |
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Complete list of extractable fields for Reviews & Comments objects from simplyrecipes.com. All fields typed and schema-versioned.
"comment_id": "c_849201", "recipe_url": "https://www.simplyrecipes.com/recipes/classic_baked_chicken/", "user_name": "Sarah Jenkins", "comment_date": "2023-11-05T09:12:00Z", "rating": 5, "comment_text": "Followed the recipe exactly and it turned out perfect. The skin was very crispy.", "helpful_votes": 14, "reply_count": 1
| # | comment_id | recipe_url | user_name | comment_date | rating | comment_text |
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Our Simply Recipes scraper handles unstructured text parsing, natural language ingredient normalisation, and pagination logic to deliver clean, relational database rows.
We parse raw ingredient strings into distinct quantity, unit, and ingredient components, resolving fractions and standardising imperial to metric units.
Capture prep time, cook time, inactive time, and total time as integer minutes. Extract serving sizes and yield metrics for scaling calculations.
Extract calories, fat, protein, carbohydrates, sodium, and fibre data per serving from the nutritional information panels.
Capture ordered instruction arrays, including section headers, inline images, and specific temperature or duration mentions.
Map recipes to their hierarchical categories, regional cuisines, dietary tags, and seasonal collections.
Extract aggregate star ratings, review counts, and individual user comments paginated across the recipe discussion threads.
Capture author profiles, publication dates, and update timestamps to track content freshness and editorial changes.
Extract high-resolution hero image URLs, step-by-step instructional photos, and embedded video asset links.
Run scheduled pipelines to detect newly published recipes, updated instructions, or fresh user comments.
Brief in. Clean data out.
Provide target categories, specific dietary tags, or author profiles. We design the extraction schema together.
We configure Scrapy crawlers with custom parsing logic for ingredient strings and nutritional data extraction.
Schema validation, unit standardisation checks, and null-rate monitoring before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Recipe websites present unique parsing challenges. Here is how we convert natural language into queryable database rows.
Recipe ingredients are written as free text. We use custom parsing logic to split strings like '1 1/2 cups loosely packed brown sugar' into exact numerical quantities, standardised units, and core ingredient names.
A single recipe contains multiple one-to-many relationships: ingredients, instructions, and reviews. We output nested JSON or distinct relational CSV files linked by unique recipe IDs to maintain data integrity.
We combine DOM traversal with JSON-LD schema extraction. If the visual layout changes, our scrapers fall back to embedded metadata to ensure continuous data delivery without pipeline breakage.
Popular recipes have hundreds of comments loaded dynamically. Our Playwright instances execute the required JavaScript to expand comment threads and capture the complete user discussion corpus.
We convert fractions to decimals and provide mapped metric equivalents for imperial measurements, ensuring your downstream applications receive consistent numeric types.
Health and fitness apps integrate structured recipe data to generate automated weekly meal plans based on macronutrient targets.
Retailers map parsed ingredient lists to their inventory SKUs to enable one-click cart population for specific recipes.
Machine learning teams use structured instruction sequences and ingredient pairings to train generative culinary AI models.
Dietitians and health platforms aggregate recipe macro profiles to analyse dietary trends and categorise content by health goals.
Food portals and search engines index recipe metadata, prep times, and user ratings to improve discovery and filtering capabilities.
FMCG brands track ingredient velocity and flavour pairing trends across popular recipes to inform new product development.
"Simply Recipes holds a highly curated repository of culinary knowledge, but extracting normalised ingredient quantities and macro profiles requires parsing complex unstructured text."
Most teams underestimate the investment required: reliable recipe scraping requires handling varied markup structures, normalising imperial and metric units, and parsing natural language ingredients into structured database rows. DataFlirt absorbs that complexity so your engineers can focus on product features, not parsing logic.
Everything supported by our simplyrecipes.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 and deduplication. Playwright handles JavaScript rendering for dynamic comment sections and interactive elements.
We deploy specific text parsing routines to handle culinary measurements, fraction conversion, and unstructured ingredient strings.
Pipelines run on AWS Lambda and ECS. 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 simplyrecipes.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available recipe data is generally permissible. DataFlirt targets only public, non-authenticated instructional and nutritional data. We do not extract personal user data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use custom parsing routines built for culinary data. This logic splits raw text into numeric quantities, standard units, and base ingredient names, while separating preparation notes into distinct fields.
Yes. Our pipeline can convert string fractions into decimals and map imperial measurements to metric equivalents based on your schema requirements.
We extract the source URLs for hero images and instructional step photos. We can deliver these URLs in the dataset or configure a pipeline to download and store the assets in your S3 bucket.
We support daily, weekly, or monthly pipeline cadences. Change detection logic ensures we only process newly published recipes or updated content to minimise processing overhead.
Our smallest packages start at a defined list of category URLs or specific dietary tags. For full site extraction, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 100 recipes as part of the pre-engagement scoping process so you can validate schema fit, parsing accuracy, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a specific dietary category export or a continuous feed of new recipes and nutritional data, we scope, build, and operate the pipeline. Tell us what you need.
