We extract flight schedules, dynamic pricing tiers, route availability, and ancillary fees from Norwegian. 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 norwegian.com. All fields typed and schema-versioned.
"flight_number": "DY1348", "origin": "OSL", "destination": "LGW", "departure_time": "2026-08-14T08:00:00Z", "arrival_time": "2026-08-14T09:20:00Z", "duration": "130", "is_direct": true, "operating_carrier": "Norwegian Air Shuttle"
| # | flight_number | origin | destination | departure_time | arrival_time | duration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing Tiers objects from norwegian.com. All fields typed and schema-versioned.
"flight_number": "DY1348", "currency": "NOK", "lowfare_price": 499.0, "lowfare_plus_price": 799.0, "flex_price": 1499.0, "taxes_included": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | flight_number | date | currency | lowfare_price | lowfare_plus_price | flex_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Network objects from norwegian.com. All fields typed and schema-versioned.
"origin_airport": "OSL", "destination_airport": "ALC", "route_active": true, "seasonal_route": false, "days_of_week": "1,3,5,7", "frequency_weekly": 4, "route_distance": 2540
| # | origin_airport | destination_airport | route_active | seasonal_route | days_of_week | first_flight_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Fees objects from norwegian.com. All fields typed and schema-versioned.
"flight_number": "DY1348", "cabin_bag_fee": 150.0, "checked_bag_fee": 290.0, "seat_reservation_min": 99.0, "seat_reservation_max": 299.0, "fast_track_fee": 120.0
| # | flight_number | route | cabin_bag_fee | checked_bag_fee | seat_reservation_min | seat_reservation_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seat Availability objects from norwegian.com. All fields typed and schema-versioned.
"flight_number": "DY1348", "departure_date": "2026-08-14", "lowfare_seats_left": 4, "flex_seats_left": 9, "alert_level": "low_stock", "scrape_timestamp": "2026-05-12T09:14:33Z"
| # | flight_number | departure_date | lowfare_seats_left | lowfare_plus_seats_left | flex_seats_left | total_capacity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Norwegian scraper handles every layer of the platform: schedule grids, dynamic pricing, ancillary fees, and seat availability — with API interception, session management, and anti-bot circumvention built in.
Flight numbers, departure/arrival times, durations, and aircraft types across the entire Norwegian route network.
Capture exact fares for LowFare, LowFare+, and Flex ticket classes, timestamped per crawl.
Extract pricing in NOK, EUR, GBP, USD, and other supported currencies with precise conversion tracking.
Monitor dynamic pricing for cabin baggage, checked luggage, seat selection, and Fast Track services.
Track 'X seats left at this price' indicators to model demand and booking velocity.
Identify new route launches, seasonal suspensions, and frequency adjustments across all European hubs.
Extract the estimated Norwegian Reward Cash earning potential for each specific flight and fare class.
Aggregate monthly minimum price grids to build long-term pricing trend models.
Map complex multi-leg itineraries including layover durations and combined segment pricing.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, or full network requirements. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and rate-limit handling for norwegian.com.
Schema validation, null-rate checks, price-outlier detection, and timezone normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airlines employ strict rate limiting and dynamic API endpoints. Here is how we stay resilient.
Norwegian aggressively throttles high-frequency IP requests. We distribute traffic across thousands of European residential IPs, matching human search velocity.
Flight searches rely on internal GraphQL/REST APIs with ephemeral tokens. We intercept these backend calls directly, bypassing DOM parsing for faster, cleaner JSON extraction.
Search sessions require valid state tokens and cookie jars. Our Playwright orchestrator maintains valid sessions, rotating them before expiration to ensure uninterrupted data flow.
Flight times span multiple European timezones. We parse local departure and arrival times and normalise all timestamps to UTC, preventing downstream analytics errors.
Airlines heavily cache search results. We inject cache-busting headers and manipulate search parameters to force origin server responses, guaranteeing real-time pricing.
Rival airlines and OTAs monitor Norwegian's LowFare pricing to adjust their own revenue management algorithms.
Aviation analysts track seasonal route additions, frequency changes, and capacity deployment across Nordic hubs.
Analyse the dynamic pricing of baggage and seat selection to understand unbundled fare strategies.
Correlate 'seats left' indicators and price step-ups with booking curves to forecast route profitability.
Metasearch engines integrate direct pricing feeds to display accurate LowFare and Flex options to end users.
Audit corporate booking platforms against direct airline pricing to ensure negotiated rate compliance.
"Airlines update pricing millions of times per day. Capturing Norwegian's dynamic LowFare tiers requires infrastructure built for high-frequency polling, not just basic web scraping."
Most teams underestimate the investment required: reliable flight scraping requires intercepting undocumented APIs, managing ephemeral session tokens, bypassing aggressive CDN caching, and normalising complex timezone data. DataFlirt absorbs that complexity so your engineers can focus on yield analysis — not infrastructure.
Everything supported by our norwegian.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 brittle HTML parsing by reverse-engineering Norwegian's internal search APIs, extracting structured JSON directly for higher reliability and throughput.
Flight pricing often varies by point-of-sale. We route requests through region-specific ISP proxies to capture accurate, localised fares without triggering bot mitigation.
Airline pricing changes rapidly. Our Kubernetes-based workers execute highly concurrent search matrices to capture price fluctuations within minutes of publication.
Data delivered to where your team already works — no new tooling required.
About norwegian.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing is generally permissible. DataFlirt targets only public, non-authenticated route and fare data. We do not extract personal passenger data or circumvent authentication walls.
We distribute search queries across extensive pools of European residential proxies and pace requests to mimic normal user behaviour, avoiding IP bans and CAPTCHA triggers.
Yes. Every search query captures the complete pricing matrix, including all available fare classes and their specific inclusions.
Yes. We extract the dynamic pricing for cabin bags, checked bags, and seat reservations associated with specific flights.
For targeted routes, we can poll pricing at sub-hourly intervals. Full network sweeps are typically executed daily to balance comprehensiveness with compute costs.
All local departure and arrival times are parsed and converted to UTC in the final delivery payload, ensuring consistent downstream analytics.
Yes. Our pipeline constructs search queries with cache-busting parameters to ensure the pricing data reflects the live inventory system, not a stale CDN cache.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need competitive price monitoring on key routes or a full map of the Nordic aviation network — we scope, build, and operate the pipeline. Tell us what you need.
