We extract dynamic pricing, room availability, suite specifications, and package offers from Raffles. 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 Properties & Locations objects from raffles.com. All fields typed and schema-versioned.
"hotel_id": "RFL-SIN", "name": "Raffles Singapore", "country": "Singapore", "city": "Singapore", "total_suites": 115, "coordinate_lat": 1.2949, "coordinate_lng": 103.8532, "star_rating": 5
| # | hotel_id | name | region | country | city | star_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Room & Suite Types objects from raffles.com. All fields typed and schema-versioned.
"room_id": "STE-PRM", "name": "Promenade Suite", "category": "Suite", "max_occupancy": 3, "size_sqm": 85, "bed_type": "King", "view_type": "Courtyard"
| # | hotel_id | room_id | name | category | max_occupancy | bed_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Availability objects from raffles.com. All fields typed and schema-versioned.
"hotel_id": "RFL-SIN", "room_id": "STE-PRM", "check_in_date": "2024-11-12", "check_out_date": "2024-11-14", "currency": "SGD", "base_rate": 1850.0, "total_rate": 2199.65, "is_available": true
| # | hotel_id | room_id | check_in_date | check_out_date | los | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dining & Restaurants objects from raffles.com. All fields typed and schema-versioned.
"venue_id": "DIN-TIF", "name": "Tiffin Room", "cuisine": "North Indian", "dress_code": "Smart Casual", "reservation_required": true, "operating_hours": "12:00 PM to 2:00 PM, 6:30 PM to 9:30 PM"
| # | hotel_id | venue_id | name | cuisine | dress_code | operating_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Offers & Packages objects from raffles.com. All fields typed and schema-versioned.
"offer_id": "OFF-JNY", "title": "Join The Journey", "min_stay": 2, "discount_pct": 15, "valid_from": "2024-01-01", "valid_to": "2024-12-31", "inclusions_list": "['Breakfast for two', 'Late check-out']"
| # | hotel_id | offer_id | title | description | valid_from | valid_to |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Raffles scraper handles the booking engine layer: dynamic pricing calendars, suite availability, tax breakdowns, and package inclusions. Built with session management and bot circumvention.
Extract global hotel directories, coordinate data, star ratings, and contact information for all Raffles properties.
Capture square meterage, bed configurations, maximum occupancy, and view types for every room and suite category.
Track base rates, tax components, and total pricing across multiple dates and lengths of stay.
Monitor sold-out dates and inventory depth signals across specific room categories.
Extract restaurant details, cuisine types, dress codes, operating hours, and spa service catalogues.
Capture advance purchase rates, bed and breakfast offers, and specific inclusion lists.
Normalise pricing data across local currencies or convert to a standard base currency during extraction.
Parse cancellation windows, deposit requirements, and guarantee rules for each rate type.
Run availability checks at hourly, daily, or weekly intervals with change-detection diffing.
Brief in. Clean data out.
Provide target properties, date ranges, lengths of stay, and currency preferences. We design the schema.
We configure Scrapy crawlers, session management, and API request replication for the Raffles booking engine.
Schema validation, null-rate checks, and price anomaly detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Hotel chains use complex session states and bot mitigation. Here is how we maintain reliable extraction.
Booking engines require valid session tokens and sequential request patterns. Our crawlers maintain complete cookie jars and replicate the exact XHR request sequence a human user generates when searching for dates.
Hotel sites rely on Akamai and Cloudflare to block automated traffic. We use residential proxies and rotate TLS fingerprints to ensure high success rates on availability searches.
Aggressive scraping triggers IP bans. We queue date searches across distributed workers with randomised delays, keeping request volumes within safe thresholds.
Pricing displays vary by region. Some include taxes, others exclude them. We normalise all extracted rates to provide explicit base rate, tax component, and total rate fields.
We maintain a hash index of last-seen prices. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Luxury hotel groups track Raffles pricing and availability to optimise their own daily rates and promotional offers.
Revenue teams analyse length-of-stay pricing curves and advance purchase discounts to adjust inventory allocation.
Distribution managers monitor direct booking rates against OTA channels to ensure rate parity compliance.
Luxury travel platforms ingest real-time availability to power their own booking interfaces.
Analysts track new property openings, suite configurations, and amenity trends in the ultra-luxury segment.
Data teams correlate sell-out dates and price surges with local events to predict future market demand.
"Hotel pricing is highly dynamic and location-dependent. You need a pipeline that handles session state and currency normalisation perfectly."
Extracting data from luxury hotel booking engines requires more than simple HTTP requests. It demands stateful session management, residential proxy rotation to bypass Akamai, and careful parsing of complex tax structures. DataFlirt handles this infrastructure so you receive clean pricing data.
Everything supported by our raffles.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 orchestration and retry logic. Playwright manages JavaScript execution and complex booking session states.
Pools of residential IPs prevent Akamai blocks. Rotation happens per session to maintain booking flow integrity.
Pipelines run on AWS ECS. Airflow handles scheduling and dependency management. Postgres stores pipeline state.
Data delivered to where your team already works — no new tooling required.
About raffles.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We configure the crawler to check combinations of check-in dates and lengths of stay based on your exact requirements.
We parse the booking summary to separate the base rate from local taxes, city taxes, and service charges, delivering a clean breakdown.
We use residential proxies, realistic browser headers, and controlled request rates to maintain high success rates against standard mitigation.
No. We only extract publicly available rates. Member pricing requires authenticated user sessions, which we do not support.
We support daily, hourly, or custom scheduled runs. High-frequency checks are distributed across larger proxy pools to avoid rate limits.
Yes. The pipeline extracts the specific text regarding cancellation windows and deposit requirements for every rate type.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily rate monitor or a complete property catalogue extraction, we scope, build, and operate the pipeline. Tell us what you need.
