We extract property details, room availability, dynamic pricing, and guest reviews from Treebo. 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 Listings objects from treebo.com. All fields typed and schema-versioned.
"hotel_id": "TRB-8492", "name": "Treebo Trend City Centre", "city": "Bengaluru", "locality": "Koramangala", "star_rating": 3, "review_score": 4.2, "total_reviews": 842, "latitude": 12.9352, "longitude": 77.6245
| # | hotel_id | name | city | locality | star_rating | review_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Room Pricing objects from treebo.com. All fields typed and schema-versioned.
"hotel_id": "TRB-8492", "check_in": "2026-10-12", "check_out": "2026-10-14", "room_type": "Oak Standard", "base_price": 2500.0, "discounted_price": 1850.0, "taxes": 222.0, "total_price": 2072.0, "availability_status": "available", "breakfast_included": true
| # | hotel_id | check_in | check_out | room_type | base_price | discounted_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Room Categories objects from treebo.com. All fields typed and schema-versioned.
"hotel_id": "TRB-8492", "room_type": "Maple Premium", "max_occupancy": 3, "bed_type": "King", "room_size_sqft": 250, "amenities": "['AC', 'TV', 'Mini Fridge', 'Geyser']", "smoking_allowed": false, "extra_bed_available": true
| # | hotel_id | room_type | max_occupancy | bed_type | room_size_sqft | amenities |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities & Facilities objects from treebo.com. All fields typed and schema-versioned.
"hotel_id": "TRB-8492", "parking": true, "wifi": true, "elevator": true, "gym": false, "pool": false, "restaurant": true, "security": true, "laundry": true
| # | hotel_id | parking | wifi | elevator | gym | pool |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Guest Reviews objects from treebo.com. All fields typed and schema-versioned.
"review_id": "REV-993821", "hotel_id": "TRB-8492", "guest_name": "Rahul M.", "rating": 4.5, "review_date": "2026-08-14", "review_text": "Clean rooms and excellent location in Koramangala.", "travel_type": "Business", "verified_stay": true
| # | review_id | hotel_id | guest_name | rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Treebo scraper handles dynamic date queries, pagination, and bot mitigation to deliver structured hotel data, pricing, and reviews.
Extract hotel names, addresses, coordinates, star ratings, and review aggregates across all Treebo locations.
Query pricing for specific check-in and check-out dates to capture base rates, discounts, and tax breakdowns.
Extract details for Acacia, Oak, Maple, and Mahogany room categories including occupancy limits and bed configurations.
Monitor inventory levels and sold-out status for specific room types on target dates.
Capture property-level and room-level amenities including parking, WiFi, dining, and accessibility features.
Extract latitude, longitude, and distances to major transit hubs and landmarks listed on the property page.
Paginate through guest reviews to extract text, ratings, dates, and verified stay indicators.
Crawl catalogues across all supported Indian cities to maintain a complete view of the Treebo network.
Run pipelines daily or hourly to capture pricing changes and new property additions without redundant data transfer.
Brief in. Clean data out.
Provide target cities, hotel IDs, or specific date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and bot mitigation for treebo.com.
Schema validation, null-rate checks, price-outlier detection, and sample payloads before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel aggregators heavily protect pricing endpoints. Here is how we maintain reliable access to Treebo's inventory.
Instead of parsing raw HTML for every price point, we reverse-engineer Treebo's internal APIs. This allows us to query hundreds of date combinations efficiently while maintaining strict schema typing for complex tax and discount structures.
Aggressive price scraping triggers rate limits and WAF blocks. We route requests through residential Indian IPs, rotating per request and managing TLS fingerprints to blend in with legitimate domestic travel searches.
Extracting rates requires precise payload construction for check-in and check-out parameters. Our pipeline orchestrates these permutations systematically, ensuring complete coverage of your requested booking windows.
For daily rate monitoring, we maintain a hash index of last-seen values per hotel-date combination. Subsequent runs only push diffs, reducing downstream processing load and storage costs.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing properties, and schema drift, responding before you notice data gaps.
Online travel agencies and aggregators monitor Treebo's direct booking rates to ensure contract compliance and price parity.
Budget hotel chains track Treebo's pricing strategies, discount depths, and seasonal rate fluctuations across key micro-markets.
Hospitality investors analyse Treebo's property density and review scores in specific localities to identify underserved areas.
Revenue managers ingest competitor availability and pricing to optimise their own dynamic pricing algorithms.
Customer experience teams aggregate guest feedback to benchmark service quality against Treebo's Acacia, Oak, and Maple tiers.
Metasearch engines enrich their catalogues with structured property metadata, amenities, and high-resolution image URLs.
"Treebo's dynamic pricing engine updates constantly based on occupancy and seasonality. Capturing this requires precise date-parameterised scraping schedules."
Extracting hotel rates at scale means querying thousands of property-date combinations. DataFlirt manages the concurrency, payload reverse-engineering, and proxy rotation so your engineers receive clean time-series pricing without managing the underlying bot-mitigation infrastructure.
Everything supported by our treebo.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 payload generation. Playwright handles complex token generation and interaction flows when APIs are heavily obfuscated.
We maintain pools of residential Indian IPs. Rotation happens per-request to avoid rate limits on pricing endpoints while ensuring accurate regional data.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for complex date-matrix queries and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About treebo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Treebo is generally permissible under applicable law. DataFlirt targets only public, non-authenticated hotel, pricing, and review data. We do not extract personal user data or circumvent authentication walls.
We parameterise our requests based on your required check-in and check-out dates, iterating through the combinations via Treebo's internal APIs or DOM structure to extract accurate rates for specific booking windows.
Yes. We can target specific micro-markets, entire cities, or the complete national catalogue of Treebo properties.
We configure pipeline cadences based on your requirements. Daily refreshes are standard, but higher frequency intra-day monitoring is available for specific priority markets.
Yes. We capture data for all available tiers including Acacia, Oak, Maple, and Mahogany, keeping attributes strictly mapped to the respective room type.
We begin building your time-series dataset from the day your pipeline is commissioned. We do not maintain a retroactive database of past Treebo prices.
Absolutely. We provide a sample run covering specific properties and dates during the scoping phase, allowing you to validate the schema and tax breakdowns before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or a continuous daily pricing feed across 800 locations, we scope, build, and operate the pipeline. Tell us what you need.
