We extract restaurant reviews, editorial ratings, Hit List inclusions, and neighbourhood guides from The Infatuation. 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 Restaurant Reviews objects from theinfatuation.com. All fields typed and schema-versioned.
"restaurant_id": "lucali-brooklyn", "name": "Lucali", "city": "New York", "rating": 9.3, "cuisine": "Pizza", "price_tier": "$$", "perfect_for_tags": "['Date Night', 'Group Dinners']"
| # | restaurant_id | name | city | neighbourhood | rating | cuisine |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hit Lists & Guides objects from theinfatuation.com. All fields typed and schema-versioned.
"guide_id": "first-timers-guide-nyc", "title": "The First Timer's Guide To NYC", "city": "New York", "restaurant_count": 24, "author": "Hannah Albertine", "last_updated": "2023-11-04", "url": "https://www.theinfatuation.com/new-york/guides/first-timers-guide-nyc"
| # | guide_id | title | city | description | author | published_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location Data objects from theinfatuation.com. All fields typed and schema-versioned.
"name": "Lucali", "address_line_1": "575 Henry St", "city": "Brooklyn", "zip_code": "11231", "latitude": 40.6818, "longitude": -74.0002
| # | restaurant_id | name | address_line_1 | address_line_2 | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Metadata & Tags objects from theinfatuation.com. All fields typed and schema-versioned.
"features": "['Outdoor Seating', 'Walk-ins Welcome']", "vibe": "Casual", "reservation_policy": "No Reservations", "alcohol_policy": "BYOB", "noise_level": "Moderate", "seating_options": "['Counter', 'Tables']"
| # | restaurant_id | features | vibe | reservation_policy | delivery_partners | dietary_options |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Authors objects from theinfatuation.com. All fields typed and schema-versioned.
"author_id": "chris-stang", "name": "Chris Stang", "role": "Co-Founder", "city_focus": "New York", "review_count": 342, "guide_count": 45
| # | author_id | name | role | bio | city_focus | review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our extraction pipeline targets editorial content, decimal ratings, and Next.js hydration states to deliver structured hospitality intelligence.
Capture precise decimal ratings out of 10 and track how scores change over time as restaurants are re-reviewed.
Track which restaurants enter or exit city-specific Hit Lists to identify trending venues and neighbourhood shifts.
Extract contextual tags like Date Night, Business Dinner, or Day Drinking to map venue utility.
Pull exact coordinates, street addresses, and editorial neighbourhood assignments for spatial analysis.
Monitor which reviewers cover specific venues and extract attribution metadata for every published piece.
Extract structured cuisine types, price tier indicators, and specific menu recommendations.
Capture hero images, gallery URLs, and alt-text metadata associated with reviews and guides.
Only emit updates when a restaurant's rating changes or a review is amended, reducing downstream processing.
Extract data across London, New York, Los Angeles, Chicago, and all 50+ supported markets.
Run one-off bulk exports or configure weekly pipelines to capture newly published reviews.
Brief in. Clean data out.
Provide target cities, guide URLs, or specific restaurant lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and Next.js state extraction for theinfatuation.com.
Schema validation, null-rate checks, and data normalisation routines before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting editorial data requires navigating modern frontend architectures. Here is how we ensure reliable delivery.
The Infatuation relies on Next.js. We bypass brittle DOM parsing by extracting structured JSON directly from the __NEXT_DATA__ hydration scripts, ensuring cleaner data and higher schema stability.
Editorial neighbourhood names often conflict with standard postal boundaries. We extract both the editorial designation and the raw coordinates to allow accurate mapping in your downstream tools.
Frontend layouts for guides and reviews update frequently. Our selector strategy uses multiple fallback chains so a minor design change does not break your data pipeline.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and providing a clean changelog of rating updates.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and coverage drops, responding before you notice.
Retail property groups analyse neighbourhood heatmaps based on restaurant density and editorial ratings to identify gentrifying areas.
Delivery apps map high-rated restaurants and Hit List inclusions to target for exclusive platform acquisition.
Restaurant groups track competitor ratings, trending cuisine types, and neighbourhood saturation by city.
Travel platforms integrate curated editorial reviews and Perfect For tags into consumer travel itineraries.
Private equity firms track hospitality trends and review velocity as leading indicators for consumer spending.
Agencies monitor client restaurant mentions, rating changes, and guide inclusions across editorial platforms.
"The Infatuation holds the highest signal-to-noise ratio in restaurant reviews, but extracting that editorial data requires navigating complex Next.js frontends."
Most teams underestimate the investment required: reliable scraping of The Infatuation requires reverse-engineering Next.js hydration states, standardising geospatial data, and maintaining selectors across frequent layout updates. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our theinfatuation.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 and Next.js state extraction.
We maintain pools of residential ISP proxies. Rotation happens per-request to prevent rate limiting and IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About theinfatuation.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated reviews, guides, and ratings. We do not extract personal data or circumvent authentication walls.
We extract structured JSON directly from the Next.js hydration scripts embedded in the page source. This method is faster and more reliable than parsing DOM elements, ensuring high data fidelity.
We support all cities covered by The Infatuation, including New York, London, Los Angeles, Chicago, Miami, San Francisco, and Austin. The pipeline dynamically discovers new cities as they are added.
Pipelines can be configured to run daily or weekly. A full catalogue refresh of all cities completes within a few hours.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record for restaurant ratings and Hit List inclusions from the date your pipeline starts.
Our packages start at a defined city list or a specific volume of restaurants with weekly delivery. Contact us with your use case for a scoped quote.
Yes. We extract the complete editorial review body, alongside the rating, author attribution, published date, and all associated metadata.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off database of New York restaurants or continuous tracking of Hit Lists across 50 cities, we scope, build, and operate the pipeline. Tell us what you need.
