We extract flight schedules, dynamic pricing signals, fare tiers, and route availability from Fastjet. 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 fastjet.com. All fields typed and schema-versioned.
"flight_number": "FN 8331", "origin_code": "HRE", "destination_code": "JNB", "departure_time": "2026-08-14T07:20:00Z", "arrival_time": "2026-08-14T09:05:00Z", "duration_minutes": 105, "aircraft_type": "Embraer ERJ 145", "stops": 0
| # | flight_number | origin_code | destination_code | departure_time | arrival_time | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fares objects from fastjet.com. All fields typed and schema-versioned.
"flight_number": "FN 8331", "departure_date": "2026-08-14", "base_fare": 1850.0, "taxes": 450.0, "total_price": 2300.0, "currency": "ZAR", "fare_class": "Value Plus", "seats_remaining": 4, "price_timestamp": "2026-05-12T10:14:00Z"
| # | flight_number | departure_date | base_fare | taxes | total_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Route Availability objects from fastjet.com. All fields typed and schema-versioned.
"origin_code": "HRE", "destination_code": "JNB", "departure_date": "2026-08-14", "flights_available": 3, "lowest_price": 2300.0, "highest_price": 3850.0, "direct_flights_only": true, "currency": "ZAR", "last_updated": "2026-05-12T10:15:00Z"
| # | origin_code | destination_code | departure_date | flights_available | lowest_price | highest_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ancillary Fees objects from fastjet.com. All fields typed and schema-versioned.
"flight_number": "FN 8331", "baggage_fee_excess": 350.0, "seat_selection_fee_standard": 50.0, "seat_selection_fee_premium": 150.0, "change_fee": 400.0, "cancellation_fee": 1000.0, "currency": "ZAR", "effective_date": "2026-05-12"
| # | flight_number | baggage_fee_excess | seat_selection_fee_standard | seat_selection_fee_premium | priority_boarding_fee | change_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tax Breakdown objects from fastjet.com. All fields typed and schema-versioned.
"flight_number": "FN 8331", "departure_date": "2026-08-14", "base_fare": 1850.0, "passenger_service_charge": 200.0, "airport_tax": 150.0, "regulatory_fee": 50.0, "vat": 50.0, "total_tax": 450.0, "total_price": 2300.0, "currency": "ZAR"
| # | flight_number | departure_date | base_fare | passenger_service_charge | airport_tax | regulatory_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our airline scraper handles the entire Fastjet booking engine: route schedules, dynamic fare tiers, multi-currency pricing, and tax breakdowns, with full session management built in.
Departure times, arrival times, flight numbers, aircraft types, and operating days across all Fastjet African routes.
Capture real-time pricing across all fare tiers: Value, Value Plus, and Flexi, recorded with exact timestamps.
Extract base fares separated from passenger service charges, airport taxes, and VAT for accurate margin analysis.
Extract pricing in ZAR, USD, and other supported regional currencies based on point of origin.
Monitor low-stock indicators and remaining seat counts on specific flights and fare classes.
Track costs for excess baggage, premium seat selection, and ticket change penalties per route.
Scrape 30-day or 90-day pricing matrix calendars to map seasonal pricing curves and demand spikes.
Run extractions hourly for high-volume routes or daily for long-term schedule monitoring.
Raw booking engine responses are normalised into structured tables, removing HTML and standardising date formats.
Brief in. Clean data out.
Provide origin-destination pairs, date ranges, and target currencies. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for fastjet.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Airline booking engines restrict high-frequency search traffic. Here is how we maintain data flow.
Fastjet's search flow requires strict cookie handling and session state progression from search to results. We maintain isolated browser contexts per search to prevent session contamination and blocked requests.
Frequent searches from data centre IPs trigger immediate rate limits. We route traffic through South African and Zimbabwean residential proxies to mimic legitimate regional passenger traffic.
The flight selection interface relies heavily on JavaScript. We use headless Playwright instances to wait for network idle and DOM hydration, ensuring complete fare data is captured.
Airline booking flows update frequently. We deploy multiple fallback selectors for critical elements like total price and tax breakdowns to prevent pipeline failure during minor site updates.
We normalise all date strings to ISO 8601 and map disparate currency symbols to standard ISO 4217 codes, ensuring the data is immediately usable in your warehouse.
Online travel agencies ingest schedule and pricing data to populate regional African flight search results.
Rival regional airlines track Fastjet's pricing curves on shared routes to optimise their own revenue management systems.
Data providers aggregate route availability and pricing to build macro-level reports on southern African travel demand.
Enterprise travel desks monitor specific high-frequency routes to secure optimal booking windows for staff movement.
Revenue management teams train machine learning models on historical fare data to predict future price fluctuations.
Aviation consultants analyse schedule frequency and fare tiers to estimate route yields and operational performance.
"Fastjet pricing fluctuates dynamically across African regional routes based on demand and fuel surcharges, but none of it is queryable unless you build the pipeline."
Most teams underestimate the investment required: reliable airline scraping requires residential proxies, full JavaScript rendering for booking engines, session management, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our fastjet.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 manages JavaScript rendering and booking engine session state.
We maintain pools of regional residential proxies. Rotation happens per-search with sticky sessions to maintain booking flow integrity.
Pipelines run on AWS 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 fastjet.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available flight schedules and pricing data is generally permissible. DataFlirt targets only public, non-authenticated route and fare data. We do not extract personal passenger details or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use regional residential proxies, strict session management, and request timing modelled on legitimate user search behaviour. We monitor for blocking in real time and trigger pool rotation automatically.
We can cover any route operated by Fastjet, including domestic flights within Zimbabwe and regional flights connecting Zimbabwe, South Africa, and Botswana.
Pipelines can be configured to run hourly for highly volatile routes or daily for broader schedule monitoring. Delivery happens immediately upon crawl completion.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per flight number and departure date from the date your pipeline starts.
Our packages start at a defined route list (e.g., 50 origin-destination pairs) with daily delivery. For higher frequency requirements, we price based on compute volume.
Absolutely. We provide a sample run of up to 10 routes across a 30-day forward calendar as part of the scoping process so you can validate schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off schedule dump or continuous price monitoring across all routes — we scope, build, and operate the pipeline. Tell us what you need.
