We extract IRCTC train schedules, waitlist prediction percentages, seat availability across quotas, and alternative route suggestions from Confirmtkt. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Train Schedules objects from confirmtkt.com. All fields typed and schema-versioned.
"train_number": "12951", "train_name": "Mumbai Rajdhani", "source_station": "MMCT", "destination_station": "NDLS", "departure_time": "17:00", "arrival_time": "08:32"
| # | train_number | train_name | source_station | destination_station | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Seat Availability objects from confirmtkt.com. All fields typed and schema-versioned.
"train_number": "12951", "date": "2024-11-20", "class_type": "3A", "quota": "GN", "availability_status": "WL/14", "prediction_percentage": 84
| # | train_number | date | class_type | quota | availability_status | total_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Waitlist Predictions objects from confirmtkt.com. All fields typed and schema-versioned.
"pnr_number": "8492018341", "current_status": "WL/12", "booking_status": "WL/45", "chart_prepared": false, "confirmation_probability": 78, "class_type": "SL"
| # | pnr_number | train_number | passenger_count | current_status | booking_status | chart_prepared |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Alternative Routes objects from confirmtkt.com. All fields typed and schema-versioned.
"source": "SBC", "destination": "NDLS", "date": "2024-11-20", "break_station": "SC", "total_duration": "34h 15m", "combined_fare": 3450, "availability_status": "AVAILABLE"
| # | source | destination | date | primary_train | alternate_train_1 | alternate_train_2 |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Bus Inventory objects from confirmtkt.com. All fields typed and schema-versioned.
"operator_name": "VRL Travels", "bus_type": "Volvo Multi-Axle Sleeper", "departure_time": "21:30", "available_seats": 14, "ticket_price": 1850, "source_city": "Bangalore"
| # | operator_name | bus_type | source_city | destination_city | departure_time | arrival_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our scraper handles Confirmtkt's internal API endpoints, rate limits, and dynamic availability checks to deliver structured train, bus, and flight data at scale.
Extract real-time seat availability across GN, Tatkal, and Premium Tatkal quotas for any train and date combination.
Capture Confirmtkt's proprietary confirmation probability percentages for waitlisted tickets.
Scrape suggested break-journey combinations and alternate trains when direct routes are sold out.
Break down base fares, reservation charges, superfast charges, and dynamic pricing across all classes.
Track live train locations, delay durations, and expected arrival times at upcoming stations.
Poll PNR statuses at scale to track movement from WL to RAC to CNF over time.
Extract private and state-run bus operators, seat layouts, and pricing for specific intercity routes.
Map all intermediate stops, halt durations, and distances for complete railway networks.
Execute concurrent checks during Tatkal opening windows without triggering rate limits.
Brief in. Clean data out.
Provide station pairs, train numbers, PNR lists, or bus routes. We map the extraction requirements.
We configure Scrapy crawlers, proxy rotation, and session management for confirmtkt.com.
Schema validation, null-rate checks, and availability-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Travel aggregators heavily throttle availability checks. Here is how we maintain high throughput and data fidelity.
Confirmtkt aggressively throttles availability checks. We distribute requests across Indian residential IP pools to maintain high throughput and avoid data-center IP bans.
Instead of parsing HTML, we intercept Confirmtkt's internal XHR requests for JSON payloads, ensuring faster execution, lower latency, and schema stability.
Availability endpoints require valid session tokens. Our infrastructure maintains a pool of active tokens, rotating them before expiry to prevent 401 unauthorised errors.
During the 10:00 AM and 11:00 AM Tatkal windows, we scale Lambda concurrency to handle massive polling volumes within minutes.
We map internal Confirmtkt IDs to standard IRCTC station codes and normalise timestamps for immediate warehouse ingestion.
Online travel agencies monitor Confirmtkt's waitlist prediction models and alternative routing algorithms to benchmark their own features.
Bus operators track train availability on parallel routes to dynamically adjust bus ticket pricing when trains sell out.
Meta-search engines ingest schedule and fare data to provide comprehensive multi-modal travel options.
Enterprise travel desks track PNR confirmation probabilities to re-book executives on flights if train waitlists stall.
Urban planners and researchers analyse train delay patterns and route congestion across the railway network.
Supply chain operators monitor train running statuses to estimate transit times for parcel and freight movements.
"Confirmtkt holds the most accurate waitlist prediction models for Indian travel — extracting this data at scale requires bypassing aggressive API throttling."
Scraping travel availability requires handling highly dynamic endpoints, strict rate limits, and time-sensitive Tatkal windows. DataFlirt manages the proxy rotation and session tokens so your team receives clean, warehouse-ready availability data without the operational overhead.
Everything supported by our confirmtkt.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.
We bypass DOM parsing by intercepting Confirmtkt's internal GraphQL and REST endpoints directly, reducing latency and schema breakage.
Travel aggregators block data-center IPs. We route requests through highly distributed Indian residential networks to blend with organic traffic.
Pipelines are triggered via Airflow schedules or API webhooks, executing on AWS Lambda for instant scale during high-demand windows.
Data delivered to where your team already works — no new tooling required.
About confirmtkt.com scraping, legality, and pipeline operations.
Ask us directly →Scraping public train schedules and availability data is generally permissible. DataFlirt targets non-authenticated endpoints and does not execute automated bookings or bypass payment gateways.
Yes. We configure high-frequency polling pipelines for specific PNR lists and push state changes via Webhook or API.
We distribute request volume across thousands of Indian residential proxies and scale compute horizontally on AWS Lambda to ensure high throughput.
Yes. While primarily known for trains, we also extract their aggregated bus inventory and flight pricing data.
We capture snapshots from the day your pipeline is commissioned. We do not maintain historical archives of past availability prior to pipeline activation.
For continuous polling pipelines, PNR updates and availability changes are delivered within seconds via Webhook. Batch exports run on your defined schedule.
20-minute scoping call. Pilot dataset within the week. Production within two. Define your routes and frequency. We handle the proxies and API interception to deliver clean Confirmtkt data.
