We extract property catalogues, dynamic room rates, availability calendars, and MeliáRewards pricing from melia.com. 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 & Metadata objects from melia.com. All fields typed and schema-versioned.
"hotel_id": "6401", "name": "Gran Meliá Palacio de los Duques", "brand": "Gran Meliá", "star_rating": 5, "city": "Madrid", "country": "Spain", "latitude": 40.4195, "longitude": -3.7111
| # | hotel_id | name | brand | star_rating | address | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Room Rates & Availability objects from melia.com. All fields typed and schema-versioned.
"hotel_id": "6401", "check_in": "2026-09-15", "check_out": "2026-09-18", "room_type_id": "DLX_KING", "board_basis": "Bed & Breakfast", "standard_rate": 450.0, "currency": "EUR", "is_refundable": true
| # | hotel_id | check_in | check_out | room_type_id | board_basis | standard_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Room Types & Inventory objects from melia.com. All fields typed and schema-versioned.
"room_id": "DLX_KING", "hotel_id": "6401", "name": "Deluxe Room", "size_sqm": 35, "max_occupancy": 2, "bed_type": "King", "view_type": "City View", "room_amenities": "['Minibar', 'Free WiFi', 'Espresso Machine']"
| # | room_id | hotel_id | name | size_sqm | max_occupancy | bed_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for MeliáRewards Pricing objects from melia.com. All fields typed and schema-versioned.
"hotel_id": "6401", "check_in": "2026-09-15", "check_out": "2026-09-18", "points_required": 42000, "points_plus_cash_points": 21000, "points_plus_cash_fiat": 180.0, "currency": "EUR", "member_tier_restrictions": "None"
| # | hotel_id | check_in | check_out | room_type_id | points_required | points_plus_cash_points |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from melia.com. All fields typed and schema-versioned.
"review_id": "REV_98421", "hotel_id": "6401", "rating_overall": 4.8, "rating_cleanliness": 5.0, "rating_service": 4.9, "rating_location": 4.7, "reviewer_type": "Couples", "review_date": "2026-08-12"
| # | review_id | hotel_id | rating_overall | rating_cleanliness | rating_service | rating_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Melia scraper handles every layer of the platform: property catalogues, dynamic pricing grids, availability calendars, and MeliáRewards points data — with JavaScript rendering and anti-bot circumvention built in.
Extract hotel names, brands (Gran Meliá, ME, Paradisus), star ratings, addresses, and geo-coordinates.
Capture standard rates, non-refundable discounts, and package pricing across various check-in and check-out combinations.
Monitor room inventory depth and sold-out dates up to 365 days in advance.
Extract points-only and points-plus-cash pricing for loyalty program analysis.
Map room categories, square meterage, bed configurations, and view types.
Differentiate rates between Room Only, Bed & Breakfast, Half Board, and All-Inclusive options.
Extract exact penalty dates, non-refundable flags, and free cancellation windows.
Scrape pricing localised to specific point-of-sale regions and currencies.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences.
Brief in. Clean data out.
Provide target cities, hotel IDs, or check-in windows. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for melia.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Hotel booking engines rely on complex session states and dynamic inventory grids. Here is how we extract reliable data without triggering rate limits.
Melia's pricing requires strict sequential requests. We manage stateful Playwright sessions to replicate valid user journeys from search to the final rate grid.
We use ISP residential proxies and inject randomised delays to mimic human booking behaviour, bypassing custom rate limiters and edge protection.
Availability grids load asynchronously. We intercept backend API responses directly to extract raw inventory data before it hits the DOM.
Hotel rates change based on the searcher's IP. We route requests through geo-specific proxy nodes to capture accurate point-of-sale pricing.
Every run emits structured logs. We alert on null-rate spikes, missing availability blocks, and schema drift to ensure data continuity.
OTAs and meta-search engines verify direct-booking prices against third-party distributor rates.
Rival hotel chains monitor Meliá's dynamic pricing and promotional discounting strategies.
Analysts ingest availability and pricing signals to optimise their own yield management algorithms.
Travel researchers track MeliáRewards point valuations and redemption availability over time.
Tourism boards and real estate analysts monitor property footprints, room counts, and brand distribution.
LLM-powered booking assistants require structured, real-time room availability and pricing data to serve user queries.
"Hotel pricing is the most volatile data on the internet. Without a reliable pipeline, your rate parity monitoring is just guesswork."
Extracting data from melia.com requires handling complex session tokens, multi-step booking flows, and aggressive rate limiting. DataFlirt manages the proxy rotation, JavaScript rendering, and schema maintenance so your team can focus on yield analysis rather than crawler maintenance.
Everything supported by our melia.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 manages complex booking flows, session cookies, and XHR interception.
We route requests through localised ISP proxies to capture accurate point-of-sale pricing and bypass regional blocking.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling for high-frequency rate checks. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About melia.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available hotel information, standard room rates, and availability is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal guest data or bypass authentication walls.
We utilise geo-targeted residential ISP proxies, maintain stateful Playwright browser sessions, and inject randomised delays during the booking flow simulation.
Yes. We extract the points-only and points-plus-cash rates for room types where this data is publicly surfaced on the standard booking grid.
Real-time streaming pipelines achieve sub-15-minute latency for specific hotel availability checks. Full catalogue rate sweeps typically complete within a 4-8 hour window.
Yes. Every rate record includes the associated board basis and the exact text of the cancellation policy.
Our smallest packages start at tracking 100 properties across a 30-day forward-looking window. For larger footprints or custom schema requirements, we price based on volume and frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or continuous rate monitoring across 300 hotels — we scope, build, and operate the pipeline. Tell us what you need.
