We extract recipes, ingredient lists, nutritional profiles, prep times, and user reviews from BBC Good Food. 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 bbcgoodfood.com. All fields typed and schema-versioned.
"recipe_id": "847291", "title": "Classic Victoria sponge", "author": "Mary Cadogan", "prep_time_mins": 20, "cook_time_mins": 20, "skill_level": "Easy", "servings": 8, "rating_value": 4.8, "rating_count": 1432
| # | recipe_id | url | title | author | publication_date | prep_time_mins |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Ingredients objects from bbcgoodfood.com. All fields typed and schema-versioned.
"recipe_id": "847291", "raw_string": "200g caster sugar", "quantity": 200, "unit": "g", "ingredient_name": "caster sugar", "section_name": "For the cake", "is_optional": false
| # | recipe_id | ingredient_id | raw_string | quantity | unit | ingredient_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Nutritional Profile objects from bbcgoodfood.com. All fields typed and schema-versioned.
"recipe_id": "847291", "kcal": 412, "fat_g": 22.4, "saturates_g": 13.1, "carbs_g": 48.9, "sugars_g": 36.2, "fibre_g": 0.8, "protein_g": 4.1
| # | recipe_id | kcal | fat_g | saturates_g | carbs_g | sugars_g |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Instructions objects from bbcgoodfood.com. All fields typed and schema-versioned.
"recipe_id": "847291", "step_number": 1, "instruction_text": "Heat oven to 190C/170C fan/gas 5. Butter two 20cm sandwich tins and line with non-stick baking paper.", "temperature_c": 190, "equipment_mentioned": "['sandwich tins', 'baking paper']", "is_tip": false
| # | recipe_id | step_number | instruction_text | image_url | equipment_mentioned | temperature_c |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for User Reviews objects from bbcgoodfood.com. All fields typed and schema-versioned.
"review_id": "rev_993821", "recipe_id": "847291", "user_name": "BakingEnthusiast99", "star_rating": 5, "review_date": "2023-11-14", "review_text": "Turned out perfectly. I added a little vanilla extract to the buttercream.", "helpful_votes": 12, "has_reply": false
| # | review_id | recipe_id | user_name | star_rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our BBC Good Food scraper handles ingredient parsing, nutritional table extraction, and pagination logic, converting unstructured HTML and nested JSON-LD into clean, queryable datasets.
Extract title, description, author, publication date, and skill level for every published recipe.
Parse raw ingredient strings into distinct quantity, unit, and core ingredient name fields.
Capture kilocalories, fat, saturates, carbohydrates, sugars, fibre, protein, and salt values per serving.
Extract preparation time, cooking time, marinating time, and total serving yields.
Map recipes to dietary requirements (vegan, gluten-free), cuisine types, and course categories.
Collect aggregate star ratings, total review counts, and individual user comments with timestamps.
Extract step-by-step cooking methods, preserving order and capturing associated tips.
Extract high-resolution image URLs for the finished dish and individual preparation steps.
Utilise schema.org/Recipe structured data as a primary extraction source, falling back to DOM parsing for older content.
Brief in. Clean data out.
Provide target categories, dietary requirements, or specific search terms. We design the extraction schema together.
We configure crawlers, proxy rotation, and parsing logic to handle BBC Good Food's specific DOM structures.
Schema validation, null-rate checks, and ingredient parsing accuracy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Recipe sites present unique scraping challenges: inconsistent historical formatting, dynamic ad loads, and complex ingredient strings. Here is how we build resilient pipelines.
BBC Good Food implements schema.org/Recipe data, but it is often incomplete for older recipes. We extract the JSON-LD payload first, then fill missing fields using targeted DOM selectors.
Raw strings like '1 heaped tbsp plain flour' require parsing. We use regex and NLP models to separate the quantity (1), unit (tbsp), modifier (heaped), and ingredient (plain flour).
Nutritional data is presented in HTML tables. We map these tables to a fixed schema, ensuring units (g, mg, kcal) are stripped and values are cast to float types.
The site hosts over a decade of content. Older recipes use different HTML layouts than modern ones. Our pipelines employ fallback selector chains to maintain high extraction yields across all eras.
Category pages often rely on JavaScript-driven pagination or load-more buttons. We handle these state changes to ensure deep crawling of entire category trees.
Health and fitness applications populate their databases with structured recipes, mapping nutritional macros directly to user dietary goals.
Supermarket integration platforms map parsed recipe ingredients to specific retail SKUs for one-click basket population.
Dietitians and researchers analyse macro and micronutrient trends across different cuisine types and dietary categories.
Machine learning teams use structured ingredient lists and sequential cooking instructions to fine-tune culinary generation models.
FMCG brands track ingredient popularity, dietary trend shifts, and user review sentiment to inform new product development.
Syndicated food portals ingest structured recipe data to augment their own catalogues with established, highly-rated content.
"BBC Good Food holds decades of tested recipes and nutritional data, but extracting clean ingredient matrices requires complex parsing logic."
Most developers fail at ingredient parsing and historical formatting drift. DataFlirt handles the raw string normalisation, nutritional table extraction, and schema standardisation so your team receives clean, queryable culinary data ready for production use. We manage the infrastructure; you build the product.
Everything supported by our bbcgoodfood.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, ensuring high-throughput extraction across thousands of category pages.
Custom Python pipelines process raw ingredient strings, applying regex and NLP models to normalise quantities, units, and base ingredients.
Pipelines run on Kubernetes clusters. Airflow handles scheduling and dependency management, pushing cleaned data directly to your warehouse.
Data delivered to where your team already works — no new tooling required.
About bbcgoodfood.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available recipe data is generally permissible. DataFlirt extracts only public, non-authenticated content such as ingredients, instructions, and nutritional facts. We do not extract personal user data or bypass paywalls for premium app content. Clients should review terms of service and consult legal counsel for specific commercial use cases.
Our NLP and regex pipelines achieve over 94% accuracy in separating quantities, units, and core ingredients from raw strings. Edge cases and complex compound ingredients are flagged for manual review or delivered as raw strings alongside the parsed attempt.
Yes. We extract kilocalories, fat, saturates, carbohydrates, sugars, fibre, protein, and salt. Values are cleaned and cast to standard numeric types, removing text labels like 'g' or 'kcal' for immediate database insertion.
BBC Good Food has over a decade of content. Our pipelines use fallback selector chains. If the modern JSON-LD schema is missing, the scraper automatically falls back to parsing the legacy HTML DOM structure.
Yes. We capture the aggregate star rating and total review count for the recipe, as well as paginating through individual user comments to extract text, timestamps, and helpful vote counts.
For a static recipe catalogue, we typically run a full extraction and deliver a complete dataset. For ongoing monitoring of new recipes or changing review scores, we can configure weekly or monthly incremental updates.
Yes. We provide a sample extraction of up to 500 recipes during the scoping phase. This allows your engineering team to validate the ingredient parsing logic and schema structure before committing to a full pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of 15,000 recipes or continuous monitoring of new culinary content, we build and operate the pipeline. Tell us what you need.
