We extract property details, dynamic room rates, availability calendars, and amenity sets from Wyndham. 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 Property Details objects from wyndham.com. All fields typed and schema-versioned.
"property_id": "WY-48291", "brand": "La Quinta", "name": "La Quinta Inn & Suites by Wyndham Austin", "city": "Austin", "state": "TX", "star_rating": 3.5, "latitude": 30.2672, "longitude": -97.7431
| # | property_id | brand | name | address | city | state |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Room Rates objects from wyndham.com. All fields typed and schema-versioned.
"property_id": "WY-48291", "date": "2026-10-14", "room_name": "Standard King Room", "base_rate": 145.0, "taxes": 24.65, "total_rate": 169.65, "currency": "USD", "refundable": false
| # | property_id | date | room_type_id | room_name | bed_type | max_occupancy |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Availability objects from wyndham.com. All fields typed and schema-versioned.
"property_id": "WY-48291", "check_in_date": "2026-10-14", "check_out_date": "2026-10-16", "adults": 2, "available_rooms": 4, "status": "AVAILABLE", "minimum_stay": 1
| # | property_id | check_in_date | check_out_date | adults | children | available_rooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities objects from wyndham.com. All fields typed and schema-versioned.
"property_id": "WY-48291", "pool": true, "wifi": true, "parking": true, "pet_friendly": true, "fitness_center": false, "airport_shuttle": false
| # | property_id | pool | wifi | parking | fitness_center | pet_friendly |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from wyndham.com. All fields typed and schema-versioned.
"property_id": "WY-48291", "overall_score": 4.2, "cleanliness_score": 4.5, "service_score": 4.1, "location_score": 4.8, "total_reviews": 1284, "tripadvisor_rating": 4.0
| # | property_id | overall_score | cleanliness_score | service_score | location_score | total_reviews |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Wyndham scraper captures data from Super 8, Ramada, La Quinta, and Wyndham Grand properties — handling dynamic rate APIs, session tokens, and geo-fenced pricing.
Extract property metadata from all Wyndham franchise brands using a single normalised schema.
Capture base rates, taxes, resort fees, and total prices for specific date ranges and occupancy levels.
Map room availability across 30, 60, or 90-day windows to forecast occupancy and demand.
Extract precise latitude and longitude for spatial analysis and competitor distance calculations.
Standardise room configurations, bed types, and occupancy limits across different properties.
Extract and structure property-level and room-level amenities into boolean arrays.
Use regional proxy nodes to capture point-of-sale specific pricing and availability.
Isolate base rates from local taxes and mandatory resort fees for accurate price parity analysis.
Schedule intra-day runs to capture rate adjustments and flash sales in real time.
Brief in. Clean data out.
Provide target cities, brand filters, or specific property IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and API reverse-engineering for wyndham.com.
Schema validation, null-rate checks, price-outlier detection, and availability sampling before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Hotel booking engines use complex session management and bot mitigation. Here is how we ensure reliable rate extraction.
Rather than scraping raw HTML, we reverse-engineer Wyndham's internal GraphQL and REST APIs. This provides cleaner data, faster execution, and access to hidden fields like raw inventory counts.
Hotel pricing APIs require valid session tokens and CSRF headers. Our pipeline generates and rotates these tokens automatically, mimicking organic user flows to prevent rate limiting.
Wyndham uses Akamai for edge security. We bypass this using residential proxies, TLS fingerprinting, and automated CAPTCHA solving to maintain uninterrupted access.
Extracting rates for multiple dates and occupancies requires thousands of requests per property. We optimise request batching to capture full 90-day availability calendars quickly.
Data structures vary between a Super 8 and a Wyndham Grand. Our pipeline normalises room types, amenities, and rate rules into a single predictable schema.
OTAs and metasearch engines track Wyndham direct rates against third-party channels to ensure rate parity compliance.
Rival hotel chains monitor Wyndham pricing strategies and promotional windows across key geographic markets.
Revenue managers analyse availability drops and rate increases to model market demand and optimise their own pricing.
Travel startups ingest structured property data to populate their own booking interfaces and recommendation engines.
Private equity firms track property density, brand distribution, and average daily rates (ADR) to evaluate hospitality investments.
Travel management companies extract rate data to negotiate better corporate discounts based on actual market pricing.
"Wyndham operates thousands of properties across twenty brands — tracking their dynamic rates requires a pipeline built for scale, not just a script."
Extracting accurate pricing from hotel chains involves navigating complex session tokens, geo-fenced rates, and aggressive bot mitigation. DataFlirt manages the proxy rotation and API reverse-engineering so your team receives clean, normalised rate data.
Everything supported by our wyndham.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies across multiple regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About wyndham.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Wyndham is generally permissible. DataFlirt targets only public, non-authenticated property, pricing, and availability data. We do not extract personal data or circumvent authentication walls. Clients should review Wyndham's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, TLS fingerprinting, and automated session management to bypass Akamai edge security. Our request timing is modelled on human behaviour to prevent rate limiting.
Yes. We configure pipelines to query specific check-in dates, lengths of stay, and occupancy counts. We can generate 30, 60, or 90-day availability matrices per property.
We support daily, hourly, or custom intra-day cadences depending on your target property volume and required date permutations.
Yes. Our pipeline supports Super 8, Days Inn, Ramada, La Quinta, Wyndham Grand, and all other franchise brands under the Wyndham umbrella.
Absolutely. We provide a sample run of up to 100 properties as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or a continuous rate-monitoring feed — we scope, build, and operate the pipeline. Tell us what you need.
