We extract live flight schedules, dynamic fare pricing, seat availability, and 6E ancillary costs from IndiGo. 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 indigo.in. All fields typed and schema-versioned.
"flight_number": "6E-2051", "origin_code": "BLR", "destination_code": "DEL", "departure_time": "06:00", "arrival_time": "08:45", "duration_minutes": 165, "aircraft_type": "A320neo", "stops": 0
| # | flight_number | origin_code | destination_code | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Live Pricing objects from indigo.in. All fields typed and schema-versioned.
"flight_number": "6E-2051", "flight_date": "2024-11-12", "fare_class": "Saver", "base_fare": 4500.0, "taxes_and_fees": 845.0, "total_fare": 5345.0, "currency": "INR", "seats_remaining": 4
| # | flight_number | flight_date | fare_class | base_fare | taxes_and_fees | total_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for 6E Ancillaries objects from indigo.in. All fields typed and schema-versioned.
"flight_number": "6E-2051", "seat_selection_fee_min": 150.0, "seat_selection_fee_max": 1500.0, "fast_forward_fee": 450.0, "excess_baggage_fee_per_kg": 500.0, "prime_bundle_cost": 899.0, "cancellation_fee": 3000.0
| # | flight_number | seat_selection_fee_min | seat_selection_fee_max | fast_forward_fee | tiffin_cost_avg | excess_baggage_fee_per_kg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Flight Status objects from indigo.in. All fields typed and schema-versioned.
"flight_number": "6E-2051", "flight_date": "2024-10-24", "status": "Delayed", "scheduled_departure": "2024-10-24T06:00:00Z", "estimated_departure": "2024-10-24T06:45:00Z", "departure_terminal": "T1", "departure_gate": "12A"
| # | flight_number | flight_date | status | scheduled_departure | estimated_departure | scheduled_arrival |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from indigo.in. All fields typed and schema-versioned.
"origin_code": "BLR", "destination_code": "DEL", "direct_flight_available": true, "frequency_per_week": 112, "first_flight_time": "05:15", "last_flight_time": "23:45", "active_route": true, "seasonal_route": false
| # | origin_code | destination_code | distance_km | direct_flight_available | frequency_per_week | first_flight_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our IndiGo scraper navigates complex booking flows, session token expiration, and Akamai bot mitigation to deliver structured flight data at high frequency.
Extract flight numbers, departure times, arrival times, aircraft types, and layover durations across IndiGo's entire domestic and international network.
Capture base fares, taxes, total costs, and currency data across all fare classes (Saver, Flexi Plus, Super 6E) with timestamped precision.
Track 'seats remaining' indicators to model booking velocity and flight load factors for competitive intelligence.
Scrape dynamic pricing for 6E Prime bundles, Fast Forward boarding, seat selection tiers, and excess baggage fees.
Monitor real-time delays, estimated departure times, terminal allocations, and gate changes for operational dashboards.
Extract pricing and schedule data for complex multi-city routes and connecting flights, including layover constraints.
Our infrastructure handles IndiGo's aggressive Akamai bot mitigation using residential proxies and TLS fingerprint spoofing.
Maintain hash indexes of last-seen fares. Subsequent runs only push price diffs, reducing downstream processing load.
Extract international route pricing in local currencies (AED, SGD, THB) with exact tax breakdowns.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and frequency requirements. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for indigo.in.
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.
Airlines invest heavily in scraping detection to protect their pricing data. Here is how we maintain reliable extraction.
IndiGo uses Akamai to block automated traffic. We route requests through residential ISP proxies with realistic TLS fingerprints, matching legitimate browser behaviour to avoid IP bans and CAPTCHA walls.
Flight searches on indigo.in require complex session state and short-lived tokens. Our Playwright instances handle cookie generation, token refresh cycles, and header signing required to maintain search continuity.
The DOM structure of airline booking engines changes frequently during A/B testing. We use multi-layer fallback chains targeting API responses and structured data, rather than relying solely on fragile CSS selectors.
For high-frequency fare monitoring, we maintain a hash index of last-seen prices. We only emit records when a fare class price changes, saving you compute cost and storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, fare outliers, and coverage drops, responding before you notice missing data.
Online travel agencies monitor direct-channel pricing to adjust their own markups, discounts, and promotional displays.
Rival airlines track IndiGo's route frequencies, fare adjustments, and ancillary pricing to optimise their own yield management systems.
Meta-search engines ingest scheduled and live pricing data to populate their flight comparison matrices without relying solely on expensive GDS APIs.
Enterprise travel managers analyse historical fare data on frequent corporate routes to negotiate better bulk rates and optimise booking windows.
Machine learning teams use historical fare fluctuations, seat availability, and seasonality data to train predictive pricing algorithms.
Aviation analysts track new route launches, frequency changes, and seasonal adjustments to model market share and capacity utilisation.
"IndiGo operates over 2,000 daily flights - tracking their dynamic pricing and route adjustments requires infrastructure, not just a script."
Aviation pricing is highly volatile and protected by aggressive bot mitigation. Scraping indigo.in requires residential proxy rotation, session token management, and continuous schema monitoring to prevent pipeline failure. DataFlirt handles the infrastructure so your analysts can focus on yield management and competitor benchmarking.
Everything supported by our indigo.in 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 IN regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 indigo.in scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing from indigo.in is generally permissible for non-disruptive, public data collection. DataFlirt targets only public, non-authenticated search results. We do not extract personal passenger data, circumvent PNR authentication walls, or violate data privacy laws. Clients should review airline terms of service and consult legal counsel for specific commercial use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and dynamic session token management. Our infrastructure is designed to mimic legitimate user search behaviour, preventing IP bans and Akamai blocks.
Yes. We extract dynamic costs for 6E Prime bundles, seat selection (window, aisle, extra legroom), fast forward boarding, and excess baggage fees associated with specific flights.
Real-time streaming pipelines can achieve sub-15-minute latency for specific high-priority routes. Full network sweeps typically run at hourly or daily cadences depending on your budget and data requirements.
We capture the 'seats remaining' indicator displayed during the booking flow, which provides a proxy for flight load factors and booking velocity on specific routes.
Our smallest packages start at a defined route list (typically 50-500 origin-destination pairs) with daily delivery. For larger network monitoring or high-frequency intra-day scraping, we price based on compute volume and proxy bandwidth.
Absolutely. We provide a sample run of up to 20 routes for a specific date range 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 daily schedule dump or continuous intra-day fare monitoring across 1,000 routes, we scope, build, and operate the pipeline. Tell us what you need.
