We extract hotel reviews, destination guides, restaurant recommendations, and Readers' Choice rankings from Condé Nast Traveler. 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 Hotel Reviews objects from cntraveler.com. All fields typed and schema-versioned.
"hotel_name": "The Ritz-Carlton, Kyoto", "location": "Kyoto, Japan", "rating": 98.4, "price_range": "$$$$", "readers_choice_winner": true, "gold_list_status": true, "editor_review": "A riverside sanctuary blending traditional ryokan aesthetics with modern luxury."
| # | hotel_name | location | rating | editor_review | price_range | amenities |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Destination Guides objects from cntraveler.com. All fields typed and schema-versioned.
"destination_name": "Amalfi Coast", "country": "Italy", "best_time_to_visit": "May to September", "top_hotels": "['Le Sirenuse', 'Hotel Santa Caterina']", "top_restaurants": "['La Sponda', 'Lo Scoglio']", "language": "Italian"
| # | destination_name | region | country | best_time_to_visit | currency | language |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Readers' Choice Awards objects from cntraveler.com. All fields typed and schema-versioned.
"award_year": 2023, "category": "Top 50 Hotels in the World", "rank": 1, "entity_name": "Ballyfin", "score": 99.2, "previous_rank": 4
| # | award_year | category | region | rank | entity_name | score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Restaurant Reviews objects from cntraveler.com. All fields typed and schema-versioned.
"restaurant_name": "Pujol", "city": "Mexico City", "cuisine": "Mexican", "price_tier": "$$$", "chef": "Enrique Olvera", "must_order": "Mole Madre"
| # | restaurant_name | city | cuisine | price_tier | chef | must_order |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Editorial Articles objects from cntraveler.com. All fields typed and schema-versioned.
"article_title": "The 21 Best Places to Go in 2024", "author": "CN Traveler Editors", "publish_date": "2023-11-15", "category": "Inspiration", "tags": "['Travel Guide', '2024']", "update_date": "2024-01-05"
| # | article_title | author | publish_date | update_date | category | tags |
|---|---|---|---|---|---|---|
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Our CNTraveler scraper handles every layer of the platform, extracting structured data from complex editorial layouts, infinite scroll feeds, and interactive maps.
Extract property details, editor reviews, amenities, and pricing tiers across all global regions.
Capture historical and current rankings for hotels, resorts, cities, islands, and airlines.
Compile curated itineraries, best-time-to-visit recommendations, and local laws for thousands of cities.
Extract editor-approved dining spots, signature dishes, and atmosphere descriptors.
Monitor the properties that make Condé Nast Traveler's highly coveted annual editor lists.
Scrape detailed reviews of cruise itineraries, cabin classes, and airline lounge experiences.
Extract full text, author metadata, publication dates, and embedded media from travel features.
Extract CDN links for professional photography galleries associated with properties and destinations.
Run one-off bulk exports or configure continuous pipelines at weekly cadences for new content.
Brief in. Clean data out.
Provide destination URLs, hotel categories, or award years. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and JavaScript rendering to handle media-heavy page loads.
Schema validation, null-rate checks, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern media sites rely on heavy JavaScript frameworks and aggressive caching. Here is how we extract structured data reliably.
CNTraveler uses modern frontend frameworks. We run full browser sessions to execute JavaScript, hydrate React components, and trigger lazy-loaded image galleries that headless clients miss entirely.
Media publishers deploy Web Application Firewalls to block automated scraping. Our crawlers use residential ISP proxies with realistic browser fingerprints to bypass rate limits.
Destination guides and article feeds rely on infinite scrolling. Our scripts simulate user scrolling behaviour to capture the complete document tree before extraction.
Editorial content formats vary wildly between standard articles, galleries, and interactive maps. We use multi-layer fallback chains to ensure consistent data extraction across all template types.
We maintain a hash index of last-seen publication and modification dates. Subsequent runs only pull newly published or updated guides, reducing compute overhead.
OTAs and booking platforms enrich their property listings with third-party editor reviews and award badges to drive conversion.
Hospitality brands monitor their inclusion in the Gold List, Hot List, and Readers' Choice Awards against competitor sets.
Tourism boards analyse destination sentiment, recommended itineraries, and trending regions to inform marketing spend.
LLM developers use high-quality, editorially curated travel guides and hotel descriptions to fine-tune travel recommendation models.
Hospitality holding companies track qualitative descriptors used by professional travel writers to assess property positioning.
Travel agents and concierge services ingest curated restaurant and activity data to build bespoke client itineraries.
"Condé Nast Traveler holds decades of the most authoritative hospitality curation on the internet. Extracting it from unstructured editorial layouts requires purpose-built infrastructure."
Most teams underestimate the complexity of scraping modern media publications. Extracting clean, structured data from disparate editorial templates, interactive maps, and infinite-scroll galleries requires full JavaScript rendering and resilient selector strategies. DataFlirt manages the infrastructure so your data science team can focus on analysis.
Everything supported by our cntraveler.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, infinite scrolling, and React hydration. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request to bypass media publisher WAFs and rate limits without triggering blocks.
Pipelines run on AWS Lambda and 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 cntraveler.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available editorial content is generally permissible under applicable law, provided it does not infringe on copyright for republication. DataFlirt extracts factual data like hotel names, locations, ratings, award status, and snippets for analytical use. We do not bypass subscription paywalls. Clients should consult legal counsel regarding fair use and copyright.
CNTraveler uses various templates for standard articles, galleries, and listicles. Our selector strategy uses multi-layer fallback chains incorporating CSS, XPath, and LD+JSON to normalise unstructured editorial text into clean, structured schemas.
Yes. We extract the complete hierarchy of Readers' Choice data, including award year, category, regional rank, property name, and numerical score across all available historical data.
We extract the CDN URLs for all property and destination images embedded in articles and galleries. We do not download the binary files directly, but provide the links for your systems to ingest.
For editorial sites like CNTraveler, we typically configure weekly or monthly pipeline runs to capture newly published guides, updated hotel reviews, and annual award releases. One-off historical extractions are also available.
Our smallest packages start at a defined category extraction, such as all European hotel reviews, with monthly delivery. For full-site extraction, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of historical Readers' Choice winners or a continuous feed of new hotel reviews, we scope, build, and operate the pipeline. Tell us what you need.
