We extract cruise itineraries, dynamic cabin pricing, ship details, and shore excursions from celebritycruises.com. 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 Itineraries objects from celebritycruises.com. All fields typed and schema-versioned.
"sailing_id": "CEL_7N_CARIB_20250412", "ship_name": "Celebrity Beyond", "nights": 7, "embark_port": "Fort Lauderdale, Florida", "sail_date": "2025-04-12", "min_base_price": 1249.0, "currency": "USD"
| # | sailing_id | ship_name | nights | destination_region | embark_port | disembark_port |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cabin Pricing objects from celebritycruises.com. All fields typed and schema-versioned.
"sailing_id": "CEL_7N_CARIB_20250412", "cabin_category": "Veranda", "cabin_code": "V2", "price_per_person": 1699.0, "taxes_fees": 185.5, "availability_status": "Available", "promo_applied": "BOGO_50"
| # | sailing_id | cabin_category | cabin_code | occupancy | price_per_person | taxes_fees |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Ships & Deck Plans objects from celebritycruises.com. All fields typed and schema-versioned.
"ship_name": "Celebrity Ascent", "ship_class": "Edge", "guest_capacity": 3260, "tonnage": 140600, "deck_count": 17, "inaugural_date": "2023-11-22", "crew_size": 1400
| # | ship_id | ship_name | ship_class | guest_capacity | tonnage | inaugural_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Shore Excursions objects from celebritycruises.com. All fields typed and schema-versioned.
"excursion_id": "CZM_SNKL_01", "port_name": "Cozumel, Mexico", "title": "Palancar Reef Snorkel & Beach Break", "duration_hours": 4.5, "activity_level": "Moderate", "price_adult": 89.0, "rating": 4.6
| # | excursion_id | port_name | title | duration_hours | activity_level | min_age |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Port Schedules objects from celebritycruises.com. All fields typed and schema-versioned.
"sailing_id": "CEL_7N_CARIB_20250412", "port_name": "Nassau, Bahamas", "day_number": 2, "arrive_time": "08:00", "depart_time": "17:00", "dock_type": "Docked", "is_tender": false
| # | sailing_id | port_name | day_number | arrive_time | depart_time | dock_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper handles the dynamic booking engine: session-based pricing, regional variations, cabin category mapping, and itinerary details - with full JavaScript rendering built in.
Parse sail dates, ports of call, ship assignments, and duration metrics across the entire global catalogue.
Capture live cabin prices, separating base fare from taxes, port expenses, and mandatory gratuities.
Track sold-out statuses, waitlist triggers, and remaining inventory indicators per stateroom category.
Extract activity details, pricing tiers, duration, and user ratings for every port of call.
Catalogue deck plans, guest capacity, tonnage, dining venues, and onboard amenities per vessel.
Simulate requests from different geographic IP addresses to monitor localized pricing and currency variations.
Monitor active promotions, onboard credit offers, and discount applications applied at checkout.
Extract exact arrival and departure times for each port, including tender versus docked status.
Run continuous pipelines at daily or weekly cadences to track price curves as sail dates approach.
Brief in. Clean data out.
Provide target regions, ship names, or date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, session management, and proxy rotation for celebritycruises.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.
Cruise booking engines rely on complex session management and dynamic pricing. Here is how we stay resilient.
Cruise pricing often requires navigating a multi-step booking funnel. We maintain strict cookie sessions and token passing to reach accurate final pricing screens without triggering bot defenses.
The Celebrity Cruises search interface is a single-page application. We run full Playwright browser sessions to hydrate dynamic pricing widgets and cabin availability grids.
Cruise lines alter pricing based on the user location. We route traffic through specific residential proxy regions to capture accurate regional fares and currency conversions.
Booking engine DOM structures update frequently. We use multiple fallback chains per field to ensure a layout change does not break your data pipeline.
We maintain a hash index of last-seen prices. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Online travel agencies sync itinerary and pricing data to power their own cruise booking engines.
Competing cruise lines monitor Celebrity Cruises pricing curves and availability to optimise their own yield management.
Analysts track deployment changes, new ship itineraries, and regional capacity shifts.
Tour operators combine live cruise pricing with flight and hotel data to create bundled holiday packages.
Destination management teams track ship arrival schedules and passenger capacity to plan local infrastructure.
B2B travel consortia build unified dashboards showing comparative cruise pricing across multiple brands.
"Celebrity Cruises holds highly dynamic pricing data that fluctuates based on occupancy and sail date - none of it is queryable unless you build the pipeline."
Most teams underestimate the investment required: reliable cruise scraping requires complex session handling, full JavaScript rendering for booking engines, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our celebritycruises.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 session flows for the booking engine.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions required for booking funnels.
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 celebritycruises.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from celebritycruises.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated itinerary and pricing data. We do not extract personal data or circumvent authentication walls.
We use full Playwright browser sessions with realistic fingerprints and stateful cookie management to navigate the multi-step booking flows and retrieve accurate final pricing.
Yes. We route requests through region-specific residential proxies to trigger localized pricing and currency displays on the target site.
We can configure pipelines to run daily or multiple times a day depending on your requirements and the volatility of the target sailings.
Yes. Our schema captures the base cabin price, mandatory taxes, port expenses, and total price as distinct fields.
Absolutely. We provide a sample run of up to 50 sailings as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off ship catalogue dump or a continuous price-monitoring feed across 4,000 sailings - we scope, build, and operate the pipeline. Tell us what you need.
