We extract restaurant ratings, inspector reviews, hotel Keys, cuisine metadata, and geolocation coordinates from the Michelin Guide. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery on your schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Restaurants & Awards objects from michelinguide.com. All fields typed and schema-versioned.
"restaurant_id": "349821", "name": "Osteria Francescana", "award_type": "3 Stars", "cuisine_type": "Creative", "price_bracket": "$$$$", "city": "Modena", "country": "Italy", "chef_name": "Massimo Bottura"
| # | restaurant_id | name | award_type | cuisine_type | price_bracket | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inspector Reviews objects from michelinguide.com. All fields typed and schema-versioned.
"restaurant_id": "349821", "review_text": "A meal here is a memorable event, blending tradition with avant-garde techniques.", "language": "en", "published_date": "2023-11-14", "specialties": "['Five ages of Parmigiano Reggiano', 'Oops! I Dropped the Lemon Tart']", "wine_list_notable": true
| # | restaurant_id | review_text | language | published_date | specialties | wine_list_notable |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotels & Keys objects from michelinguide.com. All fields typed and schema-versioned.
"hotel_id": "88421", "hotel_name": "Aman Tokyo", "key_rating": "3 Keys", "price_per_night_start": 1200, "amenities": "['Spa', 'Pool', 'Fitness Centre', 'Restaurant']", "design_style": "Minimalist Japanese", "booking_url": "https://guide.michelin.com/en/hotels/..."
| # | hotel_id | hotel_name | key_rating | description | address | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Facilities & Services objects from michelinguide.com. All fields typed and schema-versioned.
"restaurant_id": "349821", "wheelchair_accessible": true, "valet_parking": false, "air_conditioning": true, "vegetarian_menu": true, "private_rooms": true, "terrace_dining": false
| # | restaurant_id | wheelchair_accessible | valet_parking | air_conditioning | vegetarian_menu | great_view |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Discovery objects from michelinguide.com. All fields typed and schema-versioned.
"location": "Paris", "filter_award": "1 Star", "position": 1, "result_name": "Septime", "result_url": "/en/ile-de-france/paris/restaurant/septime", "scraped_timestamp": "2026-05-12T09:14:33Z"
| # | keyword | location | radius_km | filter_award | filter_cuisine | position |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Michelin Guide scraper handles the platform's map-based pagination, Next.js hydration states, and multi-language routing to deliver accurate hospitality data.
Capture 1, 2, and 3 Michelin Stars, Bib Gourmand recognitions, Green Stars, and Selected restaurant status across all global regions.
Extract the new Michelin Key ratings for hotels, including price estimates, design tags, and amenity lists.
Pull full written inspector reviews, atmosphere tags, and notable dish mentions for sentiment analysis and enrichment.
Extract accurate latitude and longitude coordinates directly from the map state, bypassing standard address normalisation issues.
Scrape content across en, fr, it, ja, and other regional subdirectories to capture localised inspector notes.
Track price brackets, primary cuisine classifications, and specific dietary offerings like vegetarian or vegan menus.
Extract official website URLs, phone numbers, and integrated booking partner links for lead generation.
Capture head chef names associated with Starred and Selected restaurants for industry mapping.
Run continuous pipelines that detect newly added restaurants, upgraded Stars, or removed listings during annual announcements.
Brief in. Clean data out.
Select target regions, award categories, or specific data types like hotels vs restaurants. We design the schema.
We configure crawlers to handle Next.js state extraction, map pagination, and regional routing.
Schema validation, coordinate accuracy checks, and translation consistency verification before launch.
JSON / CSV / Parquet pushed to your S3 bucket or BigQuery dataset on an agreed schedule.
Extracting data from modern map-driven SPA architectures requires specific techniques. Here is how we ensure reliable delivery.
The Michelin Guide relies on Next.js. Instead of brittle DOM scraping, we extract the raw JSON payloads embedded in the page hydration state, ensuring 100% accuracy for coordinates, awards, and IDs.
Standard pagination is limited on map-centric sites. Our crawlers systematically divide global regions into coordinate bounding boxes, querying the backend API to ensure zero dropped listings.
Michelin uses complex regional subdirectories (e.g., /en/ile-de-france/paris). We force consistent locale headers and map regional URLs to a unified schema, preventing duplicate entries across languages.
To prevent IP bans during full-catalogue sweeps, we route traffic through residential proxy pools, rotating IPs per request and managing session cookies effectively.
Michelin announces awards regionally throughout the year. We monitor specific regional indexes daily, pushing diffs immediately when new Stars, Bib Gourmands, or Keys are published.
Hospitality groups track cuisine trends, pricing models, and geographic density of premium dining to inform expansion strategies.
OTAs and luxury travel platforms ingest Michelin Star and Key data to badge premium inventory on their own platforms.
Premium food and beverage distributors use the database to identify and target high-end restaurants and executive chefs.
Commercial real estate analysts correlate Michelin Star density with neighbourhood gentrification and property value trends.
Hotel groups monitor the new Michelin Key awards to benchmark their properties against local luxury competitors.
LLM developers use the highly structured, multi-lingual inspector reviews to train sentiment and culinary classification models.
"The Michelin Guide remains the gold standard for global hospitality data, but extracting its map-based Next.js architecture requires dedicated infrastructure."
Most teams underestimate the investment required: reliable Michelin scraping requires handling map-driven pagination, extracting Next.js hydration states, and managing rate limits across global regions. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the pipeline.
Everything supported by our michelinguide.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 regional crawl orchestration and deduplication. Playwright is deployed selectively to handle complex map interactions and trigger hydration states.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request to ensure uninterrupted access to regional Michelin directories.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About michelinguide.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from the Michelin Guide is generally permissible under applicable law. DataFlirt targets only public, non-authenticated restaurant, hotel, and award data. We do not extract personal user data or circumvent authentication walls. Clients should review the site's ToS and consult legal counsel for specific use cases.
Yes. The Michelin Guide relies heavily on map-based discovery. We extract the exact latitude and longitude coordinates embedded in the application state for every listed property.
While the global catalogue is relatively static, regional awards are announced on specific dates throughout the year. We can configure pipelines to run daily change-detection sweeps to catch new additions immediately.
Yes. We extract 1, 2, and 3 Key ratings, along with hotel descriptions, amenities, price estimates, and booking URLs.
We can target specific regional subdirectories (e.g., /en, /fr, /ja) to extract inspector reviews in your preferred language, or scrape multiple locales simultaneously and map them to a unified schema.
Our packages start at full extractions of specific regions or award tiers (e.g., all Starred restaurants globally). Contact us with your specific data requirements for a scoped quote.
Yes. We provide a sample run of up to 500 records as part of the pre-engagement scoping process, allowing you to validate the schema and coordinate accuracy before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off global restaurant dump or continuous tracking of new Star additions, we scope, build, and operate the pipeline. Tell us what you need.
