We extract property profiles, room rates, availability calendars, and curated amenities from Design Hotels. 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 Profiles objects from designhotels.com. All fields typed and schema-versioned.
"hotel_id": "DH-0921", "name": "The Michelberger Hotel", "location": "Berlin", "country": "Germany", "designer_name": "Werner Aisslinger", "total_rooms": 119, "star_rating": 4.0
| # | hotel_id | name | location | country | description | architecture_summary |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Room Types objects from designhotels.com. All fields typed and schema-versioned.
"hotel_id": "DH-0921", "room_name": "Loft Room", "room_size_sqm": 35, "max_occupancy": 2, "bed_type": "King", "view_type": "City View", "room_category": "Suite"
| # | hotel_id | room_name | room_size_sqm | max_occupancy | bed_type | view_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Availability objects from designhotels.com. All fields typed and schema-versioned.
"hotel_id": "DH-0921", "room_name": "Loft Room", "check_in": "2026-08-14", "check_out": "2026-08-16", "base_rate": 245.0, "currency": "EUR", "member_rate": 220.5
| # | hotel_id | room_name | check_in | check_out | base_rate | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities & Experiences objects from designhotels.com. All fields typed and schema-versioned.
"hotel_id": "DH-0921", "pet_friendly": true, "wifi_included": true, "pool_type": "None", "wellness_facilities": "['Sauna', 'Massage']", "dining_options": "['Organic Restaurant', 'Courtyard Bar']", "parking": "Valet"
| # | hotel_id | wellness_facilities | dining_options | sustainability_initiatives | nearby_attractions | pet_friendly |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Location & Contact objects from designhotels.com. All fields typed and schema-versioned.
"hotel_id": "DH-0921", "address_line_1": "Warschauer Str. 39-40", "city": "Berlin", "postal_code": "10243", "country": "Germany", "latitude": 52.5042, "longitude": 13.4474
| # | hotel_id | address_line_1 | city | postal_code | country | latitude |
|---|---|---|---|---|---|---|
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Our Design Hotels scraper handles the entire property catalogue: architectural metadata, dynamic availability calendars, room categorisation, and Marriott Bonvoy integration rates — delivered ready for analysis.
Extract hotel names, descriptions, designer details, architectural summaries, and total room counts across the entire global portfolio.
Capture base rates, taxes, fees, and member-only pricing across specific check-in and check-out date ranges.
Scrape real-time room availability blocks and inventory depth for specific properties and date parameters.
Extract room names, square meterage, bed configurations, maximum occupancy limits, and view types.
Capture wellness facilities, dining options, sustainability practices, and bespoke experiences unique to each property.
Extract precise latitude and longitude coordinates, full address strings, and neighbourhood classifications.
Extract CDN URLs for property galleries, room specific images, and architectural detail shots.
Extract pricing in local currencies or force specific currency parameters during the crawl phase.
Run daily or weekly pipelines to detect pricing fluctuations and availability changes across target properties.
Brief in. Clean data out.
Provide target regions, specific properties, or date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and calendar interaction logic.
Schema validation, null-rate checks, price-outlier detection, and sample property reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern hospitality sites rely on dynamic widgets and rate limiting. Here is how we maintain data integrity.
Hospitality platforms monitor request rates closely. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to prevent IP bans.
Availability calendars and pricing engines are heavily JavaScript-rendered. We run full Playwright browser sessions to interact with date pickers and hydrate pricing data.
We utilise multiple fallback chains per field — CSS selectors, XPath, and JSON payload interception — ensuring frontend layout updates do not break the extraction pipeline.
We maintain a hash index of last-seen values per property. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs. We alert on null-rate spikes, price outliers, and schema drift, maintaining SLA uptime.
Hospitality groups monitor boutique competitor pricing, amenity offerings, and availability trends.
Revenue managers track direct booking rates against OTA channels to ensure compliance with distribution agreements.
Luxury travel platforms ingest property metadata and imagery to populate their own curated booking engines.
Developers analyse room counts, square meterage, and architectural trends in specific markets to inform new projects.
Machine learning teams use curated property descriptions and amenity lists to train luxury travel recommendation models.
Consultancies track shifts in boutique hotel design, sustainability initiatives, and wellness offerings over time.
"Design Hotels curates the world's most unique properties, but accessing their architectural metadata and dynamic pricing requires automated extraction."
Most teams underestimate the investment required: reliable hospitality scraping requires residential proxies, full JavaScript rendering for availability calendars, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our designhotels.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 retry logic. Playwright handles JavaScript rendering and calendar interactions. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required for booking flows.
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 designhotels.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available property information and pricing is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal data or bypass authentication walls to access private member histories.
We utilise headless Playwright sessions to interact with the frontend date pickers, simulating user behaviour to trigger the XHR requests that return pricing and availability data for specific dates.
Yes. We can configure the pipeline to set specific currency cookies or parameters during the crawl, ensuring the extracted rates match your required financial reporting standards.
Pipeline frequency is configurable. We support daily full-catalogue refreshes or targeted intra-day runs for specific high-priority properties and date ranges.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per property and room type, allowing you to track rate fluctuations over time.
Our packages start at defined property lists with weekly delivery. For larger datasets involving complex date-range matrices, we price based on compute volume and delivery frequency.
Absolutely. We provide a sample run of up to 20 properties as part of the pre-engagement scoping process, allowing you to 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 price-monitoring feed across specific dates — we scope, build, and operate the pipeline. Tell us what you need.
