We extract room rates, availability, Bonvoy points pricing, property amenities, and location data from Aloft. 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 aloft.com. All fields typed and schema-versioned.
"hotel_id": "BLRAL", "name": "Aloft Bengaluru Cessna Business Park", "city": "Bengaluru", "rating": 4.5, "total_rooms": 191, "pet_friendly": true, "check_in_time": "15:00"
| # | hotel_id | name | address | city | country | coordinates |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Room Rates objects from aloft.com. All fields typed and schema-versioned.
"hotel_id": "BLRAL", "room_type": "Aloft Room", "bed_type": "King", "base_rate": 8500.0, "total_rate": 10030.0, "currency": "INR", "member_rate": 8330.0, "date": "2024-11-15"
| # | hotel_id | room_type | bed_type | max_occupancy | base_rate | total_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Availability objects from aloft.com. All fields typed and schema-versioned.
"hotel_id": "BLRAL", "date": "2024-11-15", "room_type": "Savvy Suite", "is_available": true, "remaining_rooms": 3, "minimum_stay": 1, "blackout_date": false
| # | hotel_id | date | room_type | is_available | remaining_rooms | minimum_stay |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Bonvoy Redemption objects from aloft.com. All fields typed and schema-versioned.
"hotel_id": "BLRAL", "date": "2024-11-15", "points_required": 12500, "cash_upgrade": 0.0, "point_savers_active": false, "standard_redemption": true, "peak_redemption": false, "off_peak_redemption": false
| # | hotel_id | date | points_required | cash_upgrade | point_savers_active | standard_redemption |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Amenities & Policies objects from aloft.com. All fields typed and schema-versioned.
"hotel_id": "BLRAL", "parking_fee": 0.0, "wifi_fee": 0.0, "breakfast_included": false, "cancellation_deadline": "24 hours prior to arrival", "deposit_required": false, "smoking_policy": "Non-smoking"
| # | hotel_id | parking_fee | wifi_fee | breakfast_included | cancellation_deadline | deposit_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Our Aloft scraper handles every layer of the Marriott booking engine: property listings, dynamic pricing, Bonvoy points tracking, availability, and policies. JavaScript rendering, session management, and anti-bot circumvention built in.
Name, address, coordinates, amenities, pet policies, and check-in rules scraped at the property level.
Capture base rates, total rates including taxes, member rates, and advance purchase discounts. Timestamped per crawl.
Extract standard, peak, and off-peak redemption rates alongside PointSavers availability.
Track room inventory depth, sold-out status, and minimum stay requirements across specific date ranges.
Normalise room types, bed configurations, and maximum occupancy limits across all properties.
Extract exact cancellation deadlines, deposit rules, and penalty fees for every rate type.
Scrape rates in local property currency or convert to your preferred base currency via Marriott's engine.
Precise latitude and longitude data for mapping and spatial analysis.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences.
Brief in. Clean data out.
Provide property IDs, city targets, or date ranges. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for aloft.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Marriott invests heavily in scraping detection. Here is how we stay resilient. Teams choose managed infrastructure over DIY for a reason.
Marriott's bot detection operates on TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints, trained on real user behaviour patterns.
Aloft property pages and booking engines are heavily JavaScript-rendered. We run full Playwright browser sessions to hydrate dynamic rate widgets, capturing data that headless HTTP clients miss entirely.
Booking engines change DOM structures frequently. Our selector strategy uses multiple fallback chains per field. A layout change does not break your data pipeline overnight.
For large property catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs. This reduces compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops. SLA uptime is contractual.
Online travel agencies monitor direct booking rates to ensure parity agreements are maintained.
Rival hotel brands track Aloft pricing strategies across specific markets to adjust their own revenue models.
Meta-search engines ingest property details and availability signals to enrich their booking platforms.
Pricing algorithms use competitor availability and rate fluctuations as inputs for dynamic pricing models.
Analysts track Bonvoy point requirements against cash rates to calculate actual point valuation per property.
Real estate developers analyse room counts and amenity distributions to identify gaps in specific geographies.
"Aloft's dynamic pricing and Bonvoy redemption tiers shift constantly based on occupancy algorithms. None of it is queryable unless you build the pipeline."
Most teams underestimate the investment required. Reliable Marriott infrastructure scraping requires residential proxies, full JavaScript rendering for SPA loads, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our aloft.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About aloft.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and property information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated rate and availability data. We do not extract personal data or circumvent authentication walls. Clients should review Marriott's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains. We monitor for rate limits in real time and trigger pool rotation automatically.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined property set. Full catalogue refreshes complete within a 6-12 hour window depending on size.
Yes. We track standard, peak, and off-peak redemption rates alongside PointSavers availability for specific date ranges.
Our smallest packages start at a defined property list with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 50 properties as part of the pre-engagement scoping process. Validate schema fit, field completeness, and data quality before signing any contract.
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 across 200 properties. We scope, build, and operate the pipeline. Tell us what you need.
