We extract property listings, dynamic pricing, room availability, and guest reviews from Fabhotels. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery on your schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Property Listings objects from fabhotels.com. All fields typed and schema-versioned.
"property_id": "FH1029", "name": "FabHotel Prime XYZ", "brand_tier": "Prime", "city": "Bengaluru", "locality": "Indiranagar", "star_rating": 4, "review_score": 4.2, "review_count": 342
| # | property_id | name | brand_tier | city | locality | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Availability objects from fabhotels.com. All fields typed and schema-versioned.
"property_id": "FH1029", "room_type": "Deluxe Room", "check_in_date": "2024-06-15", "base_price": 2500, "discount_price": 1800, "currency": "INR", "availability_status": true, "rooms_left": 3
| # | property_id | room_type | check_in_date | check_out_date | base_price | discount_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Room Details objects from fabhotels.com. All fields typed and schema-versioned.
"property_id": "FH1029", "room_type": "Deluxe Room", "room_size_sqft": 180, "bed_type": "King", "max_occupancy": 2, "ac_available": true, "wifi_available": true
| # | property_id | room_type | room_size_sqft | bed_type | max_occupancy | ac_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Amenities & Facilities objects from fabhotels.com. All fields typed and schema-versioned.
"property_id": "FH1029", "parking_available": true, "elevator": true, "power_backup": true, "security_24x7": true, "restaurant_on_site": false, "card_payment_accepted": true
| # | property_id | parking_available | elevator | power_backup | cctv | security_24x7 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Guest Reviews objects from fabhotels.com. All fields typed and schema-versioned.
"review_id": "REV88321", "property_id": "FH1029", "guest_name": "Rahul S.", "rating": 4.5, "review_text": "Clean rooms and good service.", "date_stayed": "2024-05-10", "traveler_type": "Business"
| # | review_id | property_id | guest_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Fabhotels scraper handles every layer of the platform: property listings, dynamic pricing, forward-looking availability, and guest reviews. Built with session management and proxy rotation to bypass WAF blocks.
Extract all properties across FabHotels, FabExpress, and FabHotels Prime tiers with complete metadata.
Capture base rates, discounted rates, and taxes across different booking windows and room types.
Monitor room inventory and sell-out indicators per date and property to build supply models.
Extract exact latitude, longitude, and proximity to landmarks or transit hubs for spatial analysis.
Map standard amenities, check-in policies, and cancellation rules across the entire portfolio.
Collect guest scores, written feedback, and management responses to benchmark service quality.
Scrape inventory across Delhi, Mumbai, Bengaluru, and tier-2 or tier-3 cities simultaneously.
Run hourly or intra-day price checks for revenue management and competitor benchmarking models.
Emit only changed prices or availability statuses to reduce storage bloat and downstream processing.
Detect when Fabhotels updates their DOM structure or internal API response formats.
Brief in. Clean data out.
Provide city lists, property IDs, or date ranges. We map the required fields and extraction logic.
We configure Scrapy crawlers, proxy rotation, and session management for fabhotels.com.
Null-rate checks, price-outlier detection, and schema validation before production launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Extracting hotel pricing at scale requires heavy concurrency and date-matrix iteration. Here is how we build resilience.
Hotel aggregators use strict rate limiting. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to bypass WAF blocks.
Prices are loaded dynamically. We reverse engineer internal API endpoints to capture accurate rates directly, avoiding brittle DOM scraping where possible.
Extracting 30-day forward availability requires extensive querying. We orchestrate date-matrix loops efficiently to prevent timeout errors and ensure complete coverage.
Using regional proxies ensures the prices we capture match the localised rates presented to users in specific geographic markets.
We maintain hash indexes to push only pricing and availability diffs, providing a clean changelog rather than redundant full re-dumps.
OTAs and independent hotels track Fabhotels pricing to optimise their own daily rates and promotional discounts.
Real estate and hospitality funds monitor property additions across cities to gauge market expansion.
Auditing cross-platform pricing to ensure direct booking discounts match or beat aggregator listings.
Analysing sold-out dates and price surges to predict localised travel demand around events or holidays.
Aggregating reviews to benchmark service quality against competing budget hotel chains.
Building internal booking portals with live availability data for corporate travel desks.
"Budget hospitality pricing is highly volatile. Accessing Fabhotels data at scale requires infrastructure that can handle continuous date matrices without triggering rate limits."
Most teams underestimate the compute required for hotel scraping. Checking 1,000 properties across a 30-day forward window generates 30,000 distinct queries per run. DataFlirt manages the proxy rotation, API reverse engineering, and concurrency limits so your team receives clean, normalised pricing datasets.
Everything supported by our fabhotels.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. Rotation happens per request with sticky sessions where required to maintain search context.
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 fabhotels.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Fabhotels is generally permissible. DataFlirt targets only public, non-authenticated property, pricing, and review data. We do not extract personal data or circumvent authentication walls.
Yes. Our pipeline supports extraction across all brand tiers and cities listed on the public platform.
We iterate through date matrices based on your required forward-looking window, generating distinct queries for each check-in and check-out combination.
Intra-day pipelines achieve sub-60-minute latency for specific property subsets, providing near real-time visibility into inventory changes.
We begin tracking time-series data from the moment your pipeline is commissioned, allowing you to build historical pricing models over time.
We primarily target the web application and its underlying APIs, which provide parity with the data exposed on the mobile application.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or continuous price monitoring across all cities, we scope, build, and operate the pipeline.
