We extract flight schedules, dynamic pricing, route matrices, and ancillary costs from SpiceJet. 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 Flight Schedules objects from spicejet.com. All fields typed and schema-versioned.
"flight_number": "SG-8709", "origin_code": "DEL", "destination_code": "BOM", "departure_time": "2026-08-14T06:30:00Z", "arrival_time": "2026-08-14T08:45:00Z", "duration_minutes": 135, "aircraft_type": "Boeing 737", "stops": 0, "flight_status": "Scheduled"
| # | flight_number | origin_code | destination_code | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from spicejet.com. All fields typed and schema-versioned.
"flight_number": "SG-8709", "departure_date": "2026-08-14", "cabin_class": "Economy", "fare_type": "SpiceSaver", "base_fare": 3500.0, "taxes_and_fees": 845.0, "total_price": 4345.0, "currency": "INR", "seats_remaining": 4, "price_timestamp": "2026-07-01T10:15:22Z"
| # | flight_number | departure_date | cabin_class | fare_type | base_fare | taxes_and_fees |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Ancillary Services objects from spicejet.com. All fields typed and schema-versioned.
"flight_number": "SG-8709", "baggage_allowance_kg": 15, "extra_baggage_fee_per_kg": 500.0, "meal_included": false, "meal_options": "['Hot Meal', 'Sandwich', 'Beverage']", "priority_boarding_fee": 400.0, "seat_selection_min_fee": 150.0, "seat_selection_max_fee": 1200.0, "wheelchair_access": true
| # | flight_number | baggage_allowance_kg | extra_baggage_fee_per_kg | meal_included | meal_options | priority_boarding_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from spicejet.com. All fields typed and schema-versioned.
"origin_airport": "Indira Gandhi International Airport", "origin_code": "DEL", "destination_airport": "Chhatrapati Shivaji Maharaj International Airport", "destination_code": "BOM", "distance_km": 1148, "weekly_frequency": 42, "seasonal_flag": false, "direct_flight": true, "last_updated": "2026-07-01T00:00:00Z"
| # | origin_airport | origin_code | destination_airport | destination_code | distance_km | weekly_frequency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Seat Maps objects from spicejet.com. All fields typed and schema-versioned.
"flight_number": "SG-8709", "aircraft_type": "Boeing 737", "total_capacity": 189, "available_seats": 42, "blocked_seats": 147, "spicemax_seats_available": 6, "standard_seats_available": 36, "seat_pitch_inches": 30, "layout": "3-3"
| # | flight_number | aircraft_type | total_capacity | available_seats | blocked_seats | spicemax_seats_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our SpiceJet pipeline manages session tokens, executes JavaScript rendering for dynamic fare displays, and bypasses WAF rate limits to deliver clean pricing and schedule data.
Capture flight numbers, departure times, arrival times, durations, and aircraft types across all domestic and international routes.
Extract base fares, taxes, and total prices for all fare buckets including SpiceSaver and SpiceMax.
Track costs for extra baggage, seat selection, priority boarding, and in-flight meals tied to specific flight numbers.
Monitor remaining seat counts per fare bucket to model flight load factors and demand curves.
Map the entire origin-destination network including direct flights, layovers, and seasonal route additions.
Run pipelines at hourly or daily cadences to capture intra-day yield management adjustments.
Bypass Akamai and other bot mitigation layers using residential proxies and human-like interaction patterns.
Automated handling of search session tokens and cookies required to access final pricing pages.
Separate base fare from user development fees, aviation security fees, and GST for accurate margin analysis.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and required data points. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and WAF handling for spicejet.com.
Schema validation, null-rate checks, price-outlier detection, and sample schedules before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines invest heavily in scraping detection to protect yield management strategies. Here is how we stay resilient.
SpiceJet uses web application firewalls to block data center IPs and rate-limit search queries. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain continuous access.
Flight pricing requires valid session tokens generated during the initial search request. We manage cookie jars and token lifecycles automatically, ensuring deep pricing pages render correctly without session timeouts.
SpiceJet's booking engine is a single-page application. Fares and taxes load asynchronously via API calls. We run full Playwright browser sessions to intercept network requests and capture the final rendered prices.
Airlines often obfuscate total prices until checkout. We extract the complete JSON payload from the booking engine API to separate base fares from user development fees, security fees, and GST.
For large route networks, we maintain a hash index of last-seen values per flight. Subsequent runs only push diffs, reducing compute cost and downstream processing load. You get a clean changelog of fare adjustments.
Other airlines and OTAs monitor SpiceJet fare buckets to adjust their own yield management algorithms in real time.
Travel aggregators use structured schedule and pricing feeds to populate meta-search engines without official API access.
Market analysts track route frequencies, capacity changes, and pricing trends to forecast regional travel demand.
Machine learning teams use historical fare datasets to train predictive pricing models and demand forecasting engines.
Enterprise procurement teams ingest fare data to audit travel management company performance and optimise corporate booking policies.
Aviation consultants analyse origin-destination matrices and seasonal route additions to identify underserved markets.
"SpiceJet operates one of the densest domestic networks in India, but tracking their highly dynamic fare buckets requires continuous session management and JS execution."
Airlines deploy aggressive rate limiting and session-based pricing. Extracting accurate fares requires full browser rendering, residential proxies, and token rotation. DataFlirt manages this infrastructure so you receive clean pricing arrays without maintaining complex scraper state.
Everything supported by our spicejet.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 handles JavaScript rendering, token generation, and interaction flows required by the booking engine.
We maintain pools of residential ISP proxies across India. Rotation happens per search request with sticky sessions to ensure multi-step booking flows complete successfully.
Pipelines run on AWS 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 spicejet.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data is generally permissible under applicable law. DataFlirt targets only public, non-authenticated route and fare data. We do not extract personal passenger information or circumvent authentication walls. Clients should review SpiceJet terms of service 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. We manage session tokens automatically to ensure searches complete without triggering security blocks.
Real-time streaming pipelines achieve sub-30-minute latency for fare updates on a defined route set. Full network refreshes at daily cadence complete within a 4-hour window depending on route volume.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per flight number for base fare, taxes, and seat availability from the date your pipeline starts.
Our smallest packages start at a defined route list (typically 50-200 origin-destination pairs) with daily delivery. For larger networks or intra-day frequency, we price based on compute volume and delivery cadence.
Yes. We intercept the JSON payloads from the booking engine to separate the base fare from user development fees, aviation security fees, and GST.
Absolutely. We provide a sample run of up to 20 routes across a 7-day departure window as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily schedule dump or continuous fare monitoring across the entire network, we scope, build, and operate the pipeline. Tell us what you need.
