US Travel App Scales Fast with Flight & Hotel Datasets

Introduction

In the highly competitive U.S. travel tech market, launching a mobile app that compares flights and hotel deals in real-time requires more than just a great UI—it demands reliable, scalable data infrastructure. A U.S.-based travel startup set out to disrupt the OTA space but lacked access to structured datasets for pricing, availability, and user reviews. Building scrapers in-house was slow and resource-intensive. That’s where ArcTechnolabs stepped in with pre-scraped travel datasets , hotel price review datasets, and hotel reviews data scraping services to deliver a fast, cost-efficient, and scalable data solution that powered their app launch in record time.

Client Overview

A US-based travel tech startup aimed to launch an all-in-one mobile app to help users compare flights, hotel deals, and local stays in one place. Competing with major OTAs like Expedia and Kayak, they wanted to offer real-time price comparison, booking redirection, and personalized travel alerts—but they needed structured data to power it. Their core challenge: no access to scalable, real-time datasets for flights and hotels across platforms.

Key Challenges

Building their own scrapers and APIs from scratch was becoming increasingly costly and time-consuming. The team faced several obstacles, including the complexity of extracting data from global travel platforms like Expedia, Booking.com, and Skyscanner. They also needed to handle constantly fluctuating flight and hotel prices, which required high-frequency updates. Additionally, normalizing and categorizing properties and airlines across multiple sources proved challenging. Managing availability tracking with location-based filtering—such as geo-coordinates and hotel classifications—was another hurdle, all while meeting the real-time UX demands of their mobile app.

ArcTechnolabs Solution:

ArcTechnolabs equipped the startup with pre-scraped, continuously updated datasets across two essential modules. For flights—both domestic and international—the dataset included airline names, flight numbers, origin and destination airports, departure and arrival times, historical and current fare data, seat availability across tiers (economy, premium, business), layover durations, carrier mixes, and discount indicators along with fare class codes. For hotels in major U.S. cities, the dataset featured hotel names, star ratings, latitude and longitude, nightly prices (with weekday vs. weekend comparisons), room availability, amenities, and aggregated listings from Booking.com, Hotels.com, and Expedia. It also included user ratings, review summaries, and promotional deal tags.

Sample Data: Flights (NYC → Miami)

Airline	Flight No.	Depart	Arrive	Price ($)	Class	Stops
Delta	DL 1395	08:00	10:55	165	Economy	Nonstop
United	UA 204	10:00	13:00	149	Economy	1 Stop
JetBlue	B6 88	12:15	15:10	158	Economy	Nonstop

Sample Data: Hotels in Miami Beach

Hotel Name	Rating	Price/Night ($)	Available Rooms	Source
Loews Miami Beach	4.5	289	Yes	Expedia
The Palms Hotel & Spa	4.3	250	Yes	Booking.com
Fontainebleau	4.6	315	Yes	Hotels.com

Client Testimonial

"ArcTechnolabs fast-tracked our entire product roadmap. Their pre-scraped flight and hotel datasets saved us months of backend development and gave us instant access to real-time pricing and availability. We were able to launch our MVP 3x faster, offer dynamic travel deals, and hit 65,000+ downloads in just two months. Their hotel reviews data scraping was especially useful in building trust signals for users. Reliable, scalable, and incredibly responsive—ArcTechnolabs was a true data partner."

— Founder & CEO, US-Based Travel App Startup

Conclusion

Launching a travel app in the US is tough—especially when speed, pricing accuracy, and performance are key. With pre-scraped flight and hotel datasets from ArcTechnolabs , this travel tech startup cut development time, gained early users, and built smarter booking tools from Day 1. From MVP to market dominance, real-time data was the foundation.

Source >> https://www.arctechnolabs.com/travel-app-growth-using-us-flight-hotel-datasets.php

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