We extract flight schedules, dynamic pricing, hotel availability, bus routes, and package deals from Yatra. 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 Flights objects from yatra.com. All fields typed and schema-versioned.
"flight_number": "6E-2041", "airline": "IndiGo", "departure_airport": "DEL", "arrival_airport": "BOM", "price_economy": 5412.0, "yatra_prime_price": 4912.0, "duration": "2h 15m", "stops": 0
| # | flight_number | airline | departure_airport | arrival_airport | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hotels objects from yatra.com. All fields typed and schema-versioned.
"hotel_id": "HTL-8921", "hotel_name": "Taj Mahal Tower", "city": "Mumbai", "star_rating": 5, "user_rating": 4.6, "price_per_night": 14500.0, "is_yatra_assured": true, "review_count": 3104
| # | hotel_id | hotel_name | city | locality | star_rating | user_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Buses objects from yatra.com. All fields typed and schema-versioned.
"operator_name": "VRL Travels", "bus_type": "Volvo Multi-Axle Sleeper A/C", "departure_city": "Bangalore", "arrival_city": "Goa", "price": 1850.0, "available_seats": 12, "duration": "11h 30m"
| # | operator_name | bus_type | departure_city | arrival_city | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Holiday Packages objects from yatra.com. All fields typed and schema-versioned.
"package_id": "PKG-442", "package_name": "Mesmerizing Kerala", "destination": "Kerala", "duration_days": 6, "duration_nights": 5, "price_per_person": 24500.0, "flight_included": false, "hotel_included": true
| # | package_id | package_name | destination | duration_days | duration_nights | inclusions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Offers & Promos objects from yatra.com. All fields typed and schema-versioned.
"promo_code": "YATRASBI", "category": "Domestic Flights", "discount_type": "percentage", "discount_value": 12, "max_discount": 1500, "bank_partner": "SBI"
| # | promo_code | category | discount_type | discount_value | max_discount | min_booking_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Yatra scraper handles every layer of the platform: flight matrices, dynamic pricing, hotel inventory, and bus schedules, with session management and anti-bot circumvention built in.
Track dynamic pricing across economy, business, and Yatra Prime fares. Capture tax breakdowns and convenience fees.
Capture room availability, tax breakdowns, user ratings, and Yatra Assured tags across thousands of properties.
Scrape operator schedules, seat layouts, available seat counts, and boarding or dropping points.
Extract complex multi-leg flight data including layover durations, terminal changes, and operating airlines.
Monitor active bank offers, eCash benefits, coupon conditions, and maximum discount caps.
Extract structured rules for refunds, date changes, and luggage limits per fare class.
Parse day-by-day itineraries, inclusions, hotel categories, and per-person pricing for domestic and international tours.
Track remaining seats on specific flights or buses to gauge route demand and booking velocity.
Run daily inventory checks or high-frequency price monitoring with change-detection diffing.
Route requests through specific regional proxies to capture localised fares and currency conversions.
Brief in. Clean data out.
Provide routes, cities, or hotel lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for yatra.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket or BigQuery dataset on agreed cadence.
Travel aggregators invest heavily in scraping detection. Here is how we stay resilient and deliver clean data.
Yatra blocks data centre IPs and enforces strict rate limits. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
Flight prices change mid-session based on inventory and cookie history. We capture the final verifiable price before checkout, ensuring the data reflects actual bookable rates.
Yatra's frontend relies on complex JSON payloads for search. We interact directly with these endpoints where possible, mapping proprietary city codes and date formats to standard schemas.
Hotel and package pages have varying DOM structures. We use fallback chains and structured data extraction so a layout change does not break your data pipeline overnight.
For large flight matrices, we maintain a hash index of last-seen values per route. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Travel aggregators track Yatra's pricing and availability to adjust their own margins and stay competitive.
Airlines and bus operators monitor OTA display prices to optimise their revenue management systems.
Enterprises track historical route prices to negotiate better corporate rates and optimise travel budgets.
Analysts estimate booking volumes by tracking seat availability depletion over time across major routes.
Deal sites monitor promo codes and eCash offers to alert users to price drops and stacking opportunities.
Hoteliers track how their properties and competitors are priced, ranked, and reviewed on Yatra.
"Travel pricing is the ultimate dynamic dataset. Flights and hotels reprice constantly based on inventory, cookies, and time to departure, requiring continuous extraction."
Scraping Yatra requires navigating aggressive rate limits, session-dependent pricing, and complex search payloads. DataFlirt handles the proxy rotation, API reverse-engineering, and schema maintenance so your data science team can focus on pricing algorithms, not pipeline debugging.
Everything supported by our yatra.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 for complex search forms.
We maintain pools of residential ISP proxies across regions. 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 is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About yatra.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Yatra is generally permissible. DataFlirt targets only public, non-authenticated flight, hotel, and bus data. We do not extract personal data, circumvent authentication walls, or track individual user bookings.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
Yes. We can extract the advertised Yatra Prime prices and associated benefits displayed on the public search results pages alongside standard fares.
Real-time streaming pipelines achieve sub-15-minute latency for price and availability signals on a defined route set. Full catalogue refreshes operate on your required daily or hourly cadence.
Yes. We capture the base price, taxes, convenience fees, and total payable amount, ensuring your pricing models reflect the final consumer cost.
Our smallest packages start at a defined route list or hotel list with daily delivery. For larger matrices or custom schema requirements, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 100 routes or hotels as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off hotel catalogue dump or a continuous flight price feed across 10,000 routes, we scope, build, and operate the pipeline. Tell us what you need.
