We extract destination guides, curated itineraries, points of interest, local insights, and maps from Moon. 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 Destination Guides objects from moon.com. All fields typed and schema-versioned.
"destination_id": "dest_8492", "name": "Oaxaca City", "region": "Oaxaca", "country": "Mexico", "best_time_to_visit": "October to April", "climate": "Temperate", "tags": "['Food', 'Culture', 'History']"
| # | destination_id | name | region | country | continent | best_time_to_visit |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Itineraries objects from moon.com. All fields typed and schema-versioned.
"itinerary_id": "itin_112", "title": "Pacific Coast Highway Road Trip", "duration_days": 14, "theme": "Road Trip", "stops": 12, "transport_mode": "Car", "budget_estimate": "Moderate"
| # | itinerary_id | title | destination | duration_days | theme | difficulty |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Points of Interest objects from moon.com. All fields typed and schema-versioned.
"poi_id": "poi_9921", "name": "Monte Alban", "category": "Attraction", "sub_category": "Archaeological Site", "latitude": 17.0439, "longitude": -96.7676, "admission_fee": "90 MXN"
| # | poi_id | name | destination | category | sub_category | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Dining & Nightlife objects from moon.com. All fields typed and schema-versioned.
"venue_id": "dine_441", "name": "Casa Oaxaca", "cuisine": "Contemporary Mexican", "price_tier": "$$$", "atmosphere": "Upscale", "recommended_dishes": "['Mole Negro', 'Ceviche']", "hours": "13:00-23:00"
| # | venue_id | name | destination | cuisine | price_tier | recommended_dishes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Accommodations objects from moon.com. All fields typed and schema-versioned.
"hotel_id": "hot_772", "name": "Quinta Real Oaxaca", "neighborhood": "Centro Historico", "accommodation_type": "Boutique Hotel", "price_tier": "$$$$", "amenities": "['Pool', 'Restaurant', 'WiFi']", "phone": "+52 951 501 6100"
| # | hotel_id | name | destination | neighborhood | accommodation_type | price_tier |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Moon scraper handles every layer of the travel catalogue: destination overviews, structured itineraries, local insights, and spatial POI data.
Extract regional guides, climate data, and optimal travel windows for thousands of global locations.
Parse day-by-day travel plans, route maps, and recommended transport modes from editorial content.
Capture latitude, longitude, and physical addresses for attractions, parks, and historical sites.
Scrape restaurant lists, price tiers, cuisine types, and signature dishes curated by local authors.
Extract hotel types, neighbourhoods, amenities, and booking links across budget categories.
Mine specific local tips, cultural etiquette, and safety advice written by Moon contributors.
Track local festivals, seasonal events, and public holidays mentioned in the destination guides.
Extract bus routes, train schedules, and airport transfer tips for regional navigation.
Parse embedded interactive maps to extract spatial relationships between recommended POIs.
Normalise varied editorial guide formats into a consistent relational database schema.
Brief in. Clean data out.
Provide target regions, countries, or specific guide URLs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and DOM parsers for moon.com.
Schema validation, coordinate checks, and null-rate monitoring before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from editorial travel content requires specialised parsing. Here is how we build reliable pipelines.
Moon applies basic rate limiting to intensive crawlers. We use residential IPs with randomised request timing to prevent IP bans and ensure consistent access to guide pages.
Travel guides often embed structured data within editorial paragraphs. We use regex and NLP models to extract attributes like price tiers, operating hours, and addresses from raw text.
Embedded maps contain valuable coordinate data. We intercept XHR requests to extract raw GeoJSON for POIs, ensuring you receive precise latitude and longitude values.
Moon updates guide layouts periodically. We use multiple fallback chains per field to ensure continuous extraction even when editorial formatting changes.
We maintain a hash index of last-seen values. Subsequent runs only push diffs when guides are updated, reducing storage bloat and processing load.
Online travel agencies ingest detailed POI and destination data to enhance their booking pages with local context.
Mobile developers use structured itineraries and coordinates to build interactive travel companions and route planners.
Hospitality analysts track destination popularity and emerging regions based on guide updates and coverage expansion.
ML teams train travel recommendation engines using curated local insights, categorical tags, and sentiment analysis.
GIS teams map POI density and tourist corridors using extracted coordinates to model foot traffic.
Tour operators combine guide data with flight APIs to generate automated travel packages based on recommended itineraries.
"Moon travel guides contain decades of curated local knowledge, but extracting that unstructured text into a spatial database requires specialised pipeline architecture."
Most teams underestimate the complexity of parsing editorial travel content. Reliable Moon scraping requires NLP text extraction, coordinate normalisation, and XHR interception for map data. DataFlirt absorbs that complexity so your engineers can focus on product development.
Everything supported by our moon.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 map rendering and XHR interception for coordinate data.
We maintain pools of residential ISP proxies. Rotation happens per-request to bypass rate limits and ensure continuous access.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About moon.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available travel information is generally permissible. DataFlirt targets only public guides and POI data. We do not extract premium paid content or circumvent authentication walls.
We use a combination of regex, CSS selectors, and NLP models to extract structured attributes like prices, hours, and addresses from editorial paragraphs.
Yes. We intercept XHR requests and parse embedded map data to extract precise latitude and longitude for destinations and POIs.
Travel guides update infrequently. We typically configure weekly or monthly pipeline runs to capture new editions and editorial revisions.
Yes. We can scope the extraction to specific continents, countries, or US states based on your data requirements.
Our smallest packages start at a defined set of destinations with monthly delivery. For full-site extraction, we price based on total volume.
Absolutely. We provide a sample run of up to 50 POIs or 5 itineraries as part of the pre-engagement scoping process to validate schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a specific regional guide extraction or a continuous feed of global POIs - we scope, build, and operate the pipeline. Tell us what you need.
