We extract cost-per-serving metrics, ingredient lists, nutritional profiles, and step-by-step instructions from Budgetbytes. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 budgetbytes.com. All fields typed and schema-versioned.
"recipe_id": "bb-4921", "title": "Spicy Noodles", "author": "Beth Moncel", "publish_date": "2023-08-14", "prep_time_mins": 5, "cook_time_mins": 10, "yield_servings": 2, "average_rating": 4.8, "review_count": 142
| # | recipe_id | url | title | author | publish_date | category |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Cost Metrics objects from budgetbytes.com. All fields typed and schema-versioned.
"recipe_id": "bb-4921", "total_cost": 2.45, "cost_per_serving": 1.23, "currency": "USD", "cost_date": "2023-08-14", "cost_notes": "Prices based on local grocery store estimates", "equipment_costs": "None"
| # | recipe_id | total_cost | cost_per_serving | currency | cost_date | ingredient_costs |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Ingredients objects from budgetbytes.com. All fields typed and schema-versioned.
"recipe_id": "bb-4921", "ingredient_name": "soy sauce", "quantity": 2.0, "unit": "tbsp", "preparation_notes": "low sodium preferred", "us_metric_toggle": "US", "ingredient_category": "sauce"
| # | recipe_id | ingredient_name | quantity | unit | preparation_notes | us_metric_toggle |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Instructions & Nutrition objects from budgetbytes.com. All fields typed and schema-versioned.
"recipe_id": "bb-4921", "step_number": 1, "instruction_text": "Combine soy sauce, brown sugar, and chili garlic sauce in a small bowl.", "calories": 320, "carbohydrates_g": 54, "protein_g": 12, "fat_g": 8, "sodium_mg": 850
| # | recipe_id | step_number | instruction_text | step_image_url | calories | carbohydrates_g |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Comments & Reviews objects from budgetbytes.com. All fields typed and schema-versioned.
"comment_id": "c-99421", "recipe_id": "bb-4921", "author_name": "Sarah J.", "comment_date": "2023-08-15", "rating_given": 5, "comment_text": "Added peanut butter to the sauce, turned out great!", "author_reply": "Great addition!", "reply_date": "2023-08-16"
| # | comment_id | recipe_id | author_name | comment_date | rating_given | comment_text |
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Our Budgetbytes scraper handles every layer of the platform: recipe metadata, ingredient lists, cost metrics, and the review corpus — with ad-blocker integration and schema parsing built in.
Capture total recipe cost and cost per serving exactly as calculated on the site.
Extract quantities, units, and preparation notes into structured, queryable fields.
Scrape calories, macros, and micro-nutrients per serving from the nutrition label.
Ordered step-by-step cooking instructions with associated inline images.
Capture dietary tags like vegan, vegetarian, gluten-free, and meal types.
Extract average star ratings and total review counts for every recipe.
Scrape user feedback, recipe modifications, and author replies across paginated threads.
Standardise prep, cook, and total times into integer minutes for easy database querying.
Capture default serving sizes and scaling factors to align with ingredient quantities.
Parse embedded JSON-LD recipe schema for high-fidelity data extraction when DOM layouts change.
Brief in. Clean data out.
Provide category URLs, tag lists, or specify a full site crawl. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and JSON-LD parsers to extract recipe data cleanly.
Schema validation, null-rate checks, and unit normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Recipe sites present unique extraction challenges. Here is how we standardise unstructured food data into clean warehouse records.
Recipe blogs rely heavily on programmatic ad networks that inject complex, shifting DOM elements between recipe steps. Our crawlers use network-level ad blocking to strip out noise before parsing, ensuring clean instruction sequencing.
Over a 10+ year history, Budgetbytes has used multiple WordPress recipe plugins. We maintain extraction rules for legacy formats and modern WP Recipe Maker blocks, falling back to JSON-LD schema when available.
Modern recipes feature JavaScript toggles for unit conversion. We execute these state changes via Playwright to extract both measurement systems, giving you normalised data without manual conversion math.
Category archives and long comment threads often rely on lazy loading or complex pagination. Our crawlers navigate these structures to ensure no recipe or user review is missed during a full site crawl.
Ingredient quantities often use HTML fraction entities (like ½). Our pipeline automatically converts these into standard decimal floats (0.5) before delivery, ensuring your database receives clean numeric types.
Feed cost-effective recipe data into consumer meal planning apps to offer budget-conscious weekly menus.
Aggregate macro and micro-nutrient data for dietary research and health application databases.
Correlate Budgetbytes cost estimates with current regional grocery prices to track food inflation.
Train machine learning models on structured, cost-optimised ingredient combinations and instructions.
Populate aggregator sites with high-quality, budget-friendly meal ideas categorised by dietary preference.
Analyse popular ingredients, trending budget meals, and user engagement over time to inform FMCG product development.
"Budgetbytes provides a unique intersection of culinary instruction and strict economic constraints, making it an invaluable dataset for cost-aware food tech."
Extracting recipe data requires parsing complex, nested HTML structures often interrupted by ad networks and varying WordPress plugin formats. DataFlirt standardises this chaos into clean, queryable datasets so your data science team can focus on analysis rather than DOM traversal.
Everything supported by our budgetbytes.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 unit toggles and ad-blocking middleware.
We maintain pools of residential ISP proxies to bypass basic bot protection and ensure uninterrupted access to the site structure.
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 budgetbytes.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available recipe information, ingredients, and nutritional data is generally permissible. DataFlirt targets only public, non-authenticated content. We do not extract personal data or access premium paid meal plans. Clients should review applicable terms of service and consult legal counsel for specific use cases.
Budgetbytes has used various WordPress plugins over its history. Our pipeline includes multiple fallback chains — targeting modern WP Recipe Maker blocks, legacy HTML structures, and embedded JSON-LD schema — to ensure consistent extraction regardless of the post age.
Yes. Where the site provides a JavaScript toggle for unit conversion, we use Playwright to execute the state change and extract both measurement systems into your structured dataset.
Cost data on Budgetbytes is typically calculated at the time of publication. We extract the stated cost and the publication date. If the author updates the post with new pricing, our change-detection diffing will capture the updated values on the next scheduled run.
Yes. We can extract full comment threads, including star ratings, user feedback, recipe modifications, and author replies, handling all necessary pagination.
We extract the high-resolution source URLs for hero images and inline step-by-step photos. We can either deliver these URLs in the dataset or download the image binaries directly to your S3 bucket.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete recipe catalogue dump or continuous updates for new posts — we scope, build, and operate the pipeline. Tell us what you need.
