We extract error fares, route matrices, pricing signals, and booking links from Secretflying. 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 secretflying.com. All fields typed and schema-versioned.
"deal_id": "sf-84921", "title": "New York to Paris for $210 roundtrip", "origin": "JFK", "destination": "CDG", "price": 210.0, "currency": "USD", "airline": "Air France", "is_error_fare": false
| # | deal_id | title | origin | destination | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Booking Links objects from secretflying.com. All fields typed and schema-versioned.
"deal_id": "sf-84921", "ota_name": "Skyscanner", "booking_url": "https://skyscanner.com/transport/flights/...", "price_at_ota": 210.0, "platform": "web", "is_active": true, "scraped_at": "2023-10-24T14:30:00Z"
| # | deal_id | ota_name | booking_url | price_at_ota | referral_params | platform |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Matrix objects from secretflying.com. All fields typed and schema-versioned.
"origin_airport": "JFK", "dest_airport": "CDG", "origin_city": "New York", "dest_city": "Paris", "region": "Europe", "flight_type": "roundtrip", "stopovers": 0
| # | origin_airport | dest_airport | origin_city | dest_city | region | country |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Deal Metadata objects from secretflying.com. All fields typed and schema-versioned.
"deal_id": "sf-84921", "post_author": "Secret Flying Team", "publish_timestamp": "2023-10-24T12:00:00Z", "tags": "['Europe', 'Non-stop', 'SkyTeam']", "categories": "['USA Deals', 'Economy']", "expiry_status": "active"
| # | deal_id | post_author | publish_timestamp | tags | categories | image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Error Fares objects from secretflying.com. All fields typed and schema-versioned.
"deal_id": "sf-84922", "normal_price": 1200.0, "error_price": 150.0, "discount_pct": 87.5, "risk_level": "high", "airline_involved": "British Airways", "status": "expired"
| # | deal_id | normal_price | error_price | discount_pct | risk_level | honoring_probability |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Secretflying scraper parses unstructured travel deals into normalised route matrices, extracting exact dates, origins, destinations, and pricing before the deals expire.
Identify pricing anomalies and mistake fares immediately upon publication, delivered via low-latency webhooks.
Parse unstructured text to map origins and destinations to standard IATA airport codes.
Extract complex date ranges (e.g., 'Jan - Mar 2024') into structured ISO-8601 timestamps for database ingestion.
Capture outbound booking links to Skyscanner, Kayak, and direct airlines, including referral parameters.
Identify operating carriers, codeshares, and alliance networks associated with each published deal.
Categorise deals into Economy, Premium Economy, Business, and First Class based on post metadata.
Monitor active deals and update status flags when prices jump or error fares are corrected.
Filter and route deals based on departure regions: Euro, US, Asia, or global feeds.
Run continuous extraction at sub-minute intervals to ensure no flash sale is missed.
Brief in. Clean data out.
Provide target regions, deal types, or specific alert criteria. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for secretflying.com.
Schema validation, null-rate checks, price-outlier detection, and sample deal parsing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting unstructured travel deals requires more than simple HTTP requests. Here is how we normalise the data.
Secretflying relies on Cloudflare to block automated traffic. We route requests through residential proxies and use Playwright to solve JS challenges, ensuring uninterrupted deal flow.
Deals are often posted as unstructured text ('Fly from London to Tokyo for £300'). We use custom NER models to extract accurate IATA codes, prices, and travel date windows.
Booking links often pass through multiple redirect chains. We follow the HTTP redirects to capture the final destination URL and pricing parameters on the OTA site.
Error fares exist for hours, sometimes minutes. Our pipelines poll the feed continuously, using conditional requests (ETags) to minimise overhead while delivering alerts instantly.
WordPress DOM structures change frequently with theme updates. We use multi-layer fallback chains targeting structured data (JSON-LD) and CSS to maintain pipeline integrity.
Integrate error fares and flash sales directly into consumer-facing meta-search platforms to drive conversion.
Travel agencies monitor error fares to build high-margin package deals before airlines correct the pricing.
Revenue management teams track competitor flash sales and unfiled discount fares to adjust their own pricing models.
Mobile applications ingest our webhook feed to send push notifications to users for specific route combinations.
Analysts track historical discount trends to predict seasonal sales and route-specific price drops.
ML teams use historical deal text and parsed outcomes to train travel-specific NLP extraction models.
"Secretflying surfaces the most volatile pricing anomalies in aviation — but error fares vanish in hours unless you capture them programmatically."
Most teams underestimate the required infrastructure: capturing transient flight deals requires sub-minute polling, residential proxies to bypass Cloudflare, and custom NLP to parse unstructured travel dates. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our secretflying.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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across IN/US/UK/DE 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 secretflying.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight deals is generally permissible. DataFlirt targets only public, non-authenticated deal data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies and full Playwright browser sessions with realistic TLS fingerprints to pass JS challenges and maintain high success rates.
Yes. We use custom NLP models to convert text descriptions like 'Jan-Mar' or 'Late November' into structured date fields suitable for database querying.
Our high-frequency pipelines poll the feed continuously. Webhook delivery ensures you receive the data within seconds of the deal being published on the site.
Yes. We follow the HTTP redirect chains to extract the final OTA or airline URL, allowing you to bypass affiliate networks if required.
Absolutely. Pipelines can be configured to only extract and deliver deals originating from specific regions, such as the US or Europe.
Our smallest packages start with continuous monitoring of the global feed with webhook delivery. Contact us with your latency requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical archive of flight deals or a real-time webhook for error fares — we scope, build, and operate the pipeline. Tell us what you need.
