We extract route pricing, error fares, airline sales, and weekend getaway data from Airfarewatchdog. 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 Deals objects from airfarewatchdog.com. All fields typed and schema-versioned.
"deal_id": "AFW-773829", "origin_airport": "JFK", "destination_airport": "LHR", "airline": "British Airways", "price": 348.0, "currency": "USD", "travel_dates": "Oct 12 - Oct 19", "cabin_class": "Economy", "scraped_at": "2026-05-12T09:14:00Z"
| # | deal_id | origin_airport | destination_airport | airline | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Error Fares objects from airfarewatchdog.com. All fields typed and schema-versioned.
"route_code": "LAX-NRT", "airline": "Japan Airlines", "normal_price": 1200.0, "error_price": 250.0, "discount_pct": 79, "discovery_time": "2026-05-12T08:30:00Z", "status": "Active", "verification_status": "Confirmed"
| # | route_code | airline | normal_price | error_price | discount_pct | discovery_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Airline Sales objects from airfarewatchdog.com. All fields typed and schema-versioned.
"airline": "Southwest", "sale_title": "Fall Getaway Sale", "origin_regions": "US Domestic", "valid_from_date": "2026-09-01", "valid_to_date": "2026-11-15", "min_discount_pct": 30, "blackout_dates": "Nov 22 - Nov 26", "terms_url": "https://example.com/terms"
| # | airline | sale_title | origin_regions | destination_regions | valid_from_date | valid_to_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Weekend Getaways objects from airfarewatchdog.com. All fields typed and schema-versioned.
"origin_city": "Chicago", "weekend_date": "2026-06-12", "destination_city": "Miami", "avg_historical_price": 450.0, "current_lowest_price": 198.0, "flight_duration_mins": 185, "direct_flight_available": true, "airline_options": "American, United"
| # | origin_city | weekend_date | destination_city | avg_historical_price | current_lowest_price | flight_duration_mins |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route History objects from airfarewatchdog.com. All fields typed and schema-versioned.
"origin_iata": "SFO", "dest_iata": "CDG", "date_checked": "2026-05-12", "lowest_fare": 480.0, "median_fare": 750.0, "highest_fare": 1800.0, "active_airlines": 4, "price_volatility_index": 0.85, "trend_direction": "down"
| # | origin_iata | dest_iata | date_checked | lowest_fare | median_fare | highest_fare |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Airfarewatchdog scraper handles dynamic inventory, geo fenced pricing, and fast expiring deals with JavaScript rendering and session management built in.
Extract origin, destination, pricing, travel dates, and booking links for all active deals.
Detect and extract anomalous pricing drops and mistake fares before they expire.
Capture carrier specific promotional events, blackout dates, and valid travel windows.
Extract curated short trip data based on specific origin airports and upcoming weekend dates.
Track lowest available fares across specific origin destination pairs over time.
Separate economy, premium economy, business, and first class deal pricing.
Route requests through specific regional proxies to capture localised fare differences.
Run sub minute checks on highly volatile routes to capture fast expiring inventory.
Build time series datasets of route pricing to train predictive fare models.
Brief in. Clean data out.
Provide origin destination pairs, airline lists, or region codes. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for airfarewatchdog.com.
Schema validation, null rate checks, and price outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel aggregators deploy aggressive rate limiting. Here is how we stay resilient and why teams choose managed infrastructure.
Travel aggregators block aggressive polling. We use residential ISP proxies with realistic browser fingerprints and full cookie session management trained on real user behaviour patterns.
Fares change per session based on perceived user intent. We normalise requests using clean browser profiles to extract baseline pricing without triggering artificial surge pricing.
Error fares and flash sales vanish in minutes. Our pipeline supports high frequency sub minute polling on priority routes to capture deals before inventory clears.
Airlines show different prices based on user location. We route traffic through specific regional nodes to capture accurate point of sale pricing data.
DOM changes break DIY scrapers. We use fallback chains including CSS selectors, XPath, and structured data extraction so layout changes do not break your data pipeline.
Online travel agencies monitor competitor pricing and deal velocity to adjust their own promotional strategies.
Data science teams ingest historical fare data to build predictive pricing models for future travel dates.
Metasearch engines enrich their own flight results with curated error fares and weekend getaway deals.
Finance teams track average route costs to set realistic per diem and flight budgets for travelling employees.
Airlines monitor aggregator visibility to ensure their promotional fares are surfacing correctly against competitors.
Subscription services use webhook delivery to instantly forward error fares to their premium members.
"Airfarewatchdog surfaces the most volatile pricing anomalies in aviation, but capturing that data requires sub minute execution before inventory vanishes."
Most teams underestimate the infrastructure required to track flight deals. Travel aggregators deploy aggressive rate limiting, geo fenced pricing, and dynamic inventory models. DataFlirt handles the proxy rotation, session management, and parsing logic so you receive clean fare data directly into your warehouse.
Everything supported by our airfarewatchdog.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 and interaction flows. Combined via middleware.
We maintain pools of residential ISP proxies. Rotation happens per request with sticky sessions where required to bypass travel bot detection.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About airfarewatchdog.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and route data is generally permissible. DataFlirt targets only public, non authenticated flight deals. We do not extract personal data or circumvent authentication walls. Clients should review 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 monitor for rate limit spikes in real time and trigger pool rotation automatically.
Real time streaming pipelines achieve sub minute latency for error fares and flash sales. Daily catalogue refreshes complete within a 4 hour window depending on route volume.
Yes. Provide a list of IATA codes and we configure the pipeline to poll those exact routes at your required frequency.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time series table per route for lowest fare, median fare, and volatility from the date your pipeline starts.
Our smallest packages start at a defined route list with daily delivery. For high frequency polling on volatile routes, we price based on compute volume. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 100 routes 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 one off route analysis or a continuous error fare monitor across thousands of airports, we scope, build, and operate the pipeline. Tell us what you need.
