# booking Published articles for booking. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Flight Booking & Airlines Quantitative UX: 3 High-Level Takeaways from 30+ Charts DevFeed: [Flight Booking & Airlines Quantitative UX: 3 High-Level Takeaways from 30+ Charts]() Original publisher: [Read original article]() Author: Richard Lam Published: 2026-06-24T08:04:00Z Content type: article Language: en Sources: [Baymard Institute]() Topics: [Flight](), [User experience (UX)](), [data](), [benchmarking](), [Mobile](), [A/B Testing]() Tags: [a-b-testing](), [airline](), [apps](), [article](), [booking](), [desktop](), [mobile](), [research](), [survey](), [travel](), [ux]() ### AI overview Baymard reports quantitative UX findings from a survey of 3,125 US online shoppers who use flight-booking and airline sites. The article highlights that shoppers use multiple sources when searching for flights, three quarters belong to at least one airline loyalty program, and business travelers are more likely to bundle travel-related bookings. ### Source excerpt (Note: Unfortunately, e-mail and RSS don't support advanced layouts and features. If the graphics in this article look strange, you may want to read the article in your web browser.) Key Stats & Takeaways 30+ new insights on Flight Booking & Airline shopper habits and preferences 3,125 US online shoppers surveyed in this quantitative UX study Flight Booking & Airline shoppers use a variety of sources when looking for flights, are highly likely to participate in loyalty programs, and commonly bundle other travel-related bookings at the same time, particularly when travelling for business. At Baymard, we've just released new Quantitative Insights into people who shop on "Flight Booking & Airlines" sites, expanding our understanding of the habits and preferences of online shoppers in this category. These insights are visualizations based on survey data that supplement and support our large-scale UX research findings and benchmarking of the Flight Booking & Airlines industry. The 30+ insights address the Flight Booking & Airlines online shopping experience, spanning a breadth of topics: sources for flight searching, types of add-ons purchased, usage of apps and external tools, importance of cancellations, sales and scarcity incentives, and motivations for bundling and loyalty programs. Some of the charts also explore sub-segments of the broader audience such as business travelers and whether they are more likely to purchase add-ons, or how device usage between desktop and mobile changes as flight price becomes more expensive. These Quantitative Insights empower you to align stakeholders through objective, survey-backed data, streamline A/B testing with high-potential hypotheses, and discover and address industry-specific UX challenges. In this article, we'll highlight 3 high-level findings that reflect how shoppers evaluate and purchase flights online: Flight shoppers use a variety of sources when looking for flights Three quarters of flight shoppers are part of at leas ## Kotlin Multiplatform in Production: Two Real-World Use Cases from Booking.com DevFeed: [Kotlin Multiplatform in Production: Two Real-World Use Cases from Booking.com]() Original publisher: [Read original article]() Author: Diego Gómez Olvera Published: 2026-06-05T15:09:18Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [Kotlin Multiplatform](), [compose-multiplatform](), [experiments](), [A/B Testing](), [Android](), [iOS](), [Design system](), [Mobile](), [Development]() Tags: [a-b-testing](), [android](), [booking](), [bookingcom](), [compose](), [compose-multiplatform](), [concepts](), [consistency](), [data](), [development](), [engineering](), [experiment](), [experimentation](), [experiments](), [ios](), [java](), [jetpack-compose](), [kotlin](), [kotlin-multiplatform](), [mobile](), [multiplatform](), [objective-c]() ### AI overview This article describes two Booking.com engineering use cases for Kotlin Multiplatform and Compose Multiplatform: a shared experimentation library for consistent experiment assignments across Android and iOS, and hosting an Android design system in a web browser. ### Source excerpt Introduction For the majority of Booking.com travelers, mobile is the primary channel for researching, planning, and booking trips. Recent data shows that over 80% of travelers rely on a mobile app during the research phase, with more than half of all bookings occurring on mobile devices. Consequently, the Android and iOS platforms are critical to the company's product strategy; engineering choices made here have significant repercussions for the entire organisation. To maintain agility at this scale, two elements must function in unison: Strict decision validation: At any time, Booking.com manages over 1,000 simultaneous experiments across its product suite, with hundreds active on mobile. Every minor adjustment undergoes A/B testing via our proprietary experimentation library before reaching the user. A unified design system ensures product consistency and makes design goals transparent to all contributors, not just maintenance engineers. This article examines two specific engineering challenges solved using Kotlin Multiplatform (KMP) and Compose Multiplatform (CMP): Developing a shared experimentation library to ensure uniform experiment assignments across Android and iOS. Using Compose Multiplatform to host our Android design system in a web browser, bridging the gap between design concepts and implementation. While both cases use the same underlying technology, each provides unique insights into multiplatform development. Use case 1: shared experimentation library on Android and iOSThe problem with two implementations Historically, our internal experimentation library, responsible for managing experiment assignments, evaluations, and tracking on mobile, was maintained as two distinct codebases: a mix of Java and Kotlin for Android and Objective-C for iOS. While intended to be identical, managing two languages with fluctuating team resources inevitably led to logic drift. Discrepancies in event-tracking and experiment-fetching behaviours emerged, though they wer ## A Story of Delayed AWS Pipelines DevFeed: [A Story of Delayed AWS Pipelines]() Original publisher: [Read original article]() Author: Vladimir Romashov Published: 2026-05-08T14:23:22Z Content type: article Language: en Sources: [Booking.com Development - Medium]() Topics: [Amazon Web Services](), [Terraform](), [AWS Organizations](), [Infrastructure as code](), [CI/CD](), [Security]() Tags: [aws](), [aws-organizations](), [booking](), [ci-cd](), [ci-cd-pipeline](), [infrastructure-as-code](), [pii](), [terraform]() ### AI overview The article investigates delays in Terraform CI/CD pipelines caused by repeated use of the aws_organizations_organization data source and interactions with the AWS Organizations API. Testing found that roughly 1 in 10 Terraform pipelines were affected, with delays ranging from 1 to 15 minutes, while comparable CDK pipelines were not delayed. ### Source excerpt How a seemingly simple AWS API call can silently slow down your CI/CD pipelines Review/co-researcher: Gonzalo Ulla The Mystery It started with a line in one of our team's CI/CD logs that nobody expected: module.project.module.user_buckets.module.s3_bucket.data.aws_organizations_organization.current: Still reading... [15m10s elapsed] 15 minutes and 10 seconds -- just to read organization data. A value that should return in milliseconds was holding up entire pipelines. After digging deeper, we discovered this wasn't a one-off issue. During testing, roughly 1 in 10 pipelines containing Terraform were affected, with delays ranging from 1 to 15 minutes each. At Booking.com, we deploy and manage our AWS infrastructure using two primary Infrastructure as Code (IaC) technologies: Terraform and AWS Cloud Development Kit (CDK). To standardize and enforce our compliance and security controls, we maintain a set of internal Terraform and CDK modules to provision resources that handle personally identifiable information (PII). Interestingly, only Terraform pipelines were affected by this issue -- CDK ones running in the same accounts and against the same AWS Organization were completed without any delays. [spoiler alert/] CDK uses CloudFormation under the hood, which doesn't make additional Organizations API calls directly. [/spoiler alert]. This ruled out a general AWS-side outage or account-level throttling and pointed us toward something specific to how Terraform interacts with the Organizations API. This is the story of how we tracked down the root cause -- and why the fix isn't as simple as you'd think. What is aws_organizations_organization? Terraform's aws_organizations_organization data source retrieves information about, guess what, your AWS Organization. On the surface, it maps to the AWS DescribeOrganization API call -- a flat request. No iteration. No pagination. Simple... Or so we thought. The First Clue: Reproducing the Issue The references to the Organizations API mostly c ## Highlights of Booking.com's publication in 2025 DevFeed: [Highlights of Booking.com's publication in 2025]() Original publisher: [Read original article]() Author: Yang Yang Published: 2026-01-20T09:20:13Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Machine learning](), [Artificial Intelligence](), [Natural language processing](), [Language models](), [Fine-tuning](), [recommendation systems](), [Optimization](), [Compression](), [Inference]() Tags: [2025](), [ai](), [booking](), [compression](), [data-science](), [fine-tuning](), [inference](), [language-models](), [latency](), [neurips](), [nlp](), [optimization](), [publication]() ### AI overview Booking.com highlights its 2025 machine learning publications, including papers accepted at major conferences and research on applying Medusa speculative decoding and knowledge distillation to travel-related language model tasks. ### Source excerpt At Booking.com, our mission is to make experiencing the world easier for everyone. We are committed to investing in cutting-edge technology that removes the barriers to travel, enabling seamless connections between millions of travelers and unforgettable experiences, diverse transportation options, and exceptional accommodations. The intersection of academic rigor and industry application is where true transformation happens. In 2025, our ML community bridged this gap more effectively than ever, contributing vital new insights to the global scientific community. With 8 out of 13 papers accepted at premier conferences -- including NeurIPS, SIGIR, KDD, and ACL -- our colleagues have demonstrated world-class expertise in AI, NLP, recommendation systems, uplift modeling, etc. These aren't just theoretical wins; they are the engines of innovation that allow us to push technological boundaries, ensuring our platform remains the most sophisticated and intuitive guide in the ever-evolving travel industry. Below, we highlight some of the key achievements and insights from these groundbreaking works. Speed Without Sacrifice: Fine-Tuning Language Models with Medusa and Knowledge Distillation in Travel Applications By Daniel Zagyva, Emmanouil Stergiadis, Laurens Van Der Maas, Aleksandra Dokic, Eran Fainman, Ilya Gusev, Moran Beladev Best paper award of 2025 ACL Industry Track https://aclanthology.org/2025.acl-industry.48/ In high-stakes industrial NLP applications, balancing generation quality with speed and efficiency presents significant challenges. We address them by investigating two complementary optimization approaches: Medusa for speculative decoding and knowledge distillation (KD) for model compression. We demonstrate the practical application of these techniques in real-world travel domain tasks, including trip planning, smart filters, and generating accommodation descriptions. We introduce modifications to the Medusa implementation, starting with base pre-trained models ## How Booking.com Orchestrated Their Service Architecture with Apollo Federation DevFeed: [How Booking.com Orchestrated Their Service Architecture with Apollo Federation]() Original publisher: [Read original article]() Author: Maylee Jacob Published: 2025-05-07T13:45:42Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [API](), [Orchestration]() Tags: [api](), [apollo](), [apollo-federation](), [architecture](), [booking](), [developer-experience](), [distributed](), [events](), [experiments](), [governance](), [graphql](), [performance](), [schema](), [services](), [travel](), [travel-industry]() ### AI overview This customer case study describes how Booking.com used Apollo Federation and GraphQL to move from a centralized API orchestration layer toward a distributed, federated service architecture. The supplied text reports that the earlier approach created performance, development, maintenance, and domain-specific optimization challenges, but the article text is incomplete. ### Source excerpt This post is part of our GraphQL Summit 2024 customer series, highlighting how leading teams are using Apollo to accelerate development and build world-class applications. Join hundreds of your peers this October as we return for GraphQL Summit 2025. Booking.com, a leader in the online travel industry, has significantly advanced its digital infrastructure by integrating Apollo Federation into its service architecture. ## Design a Ticket Booking Site Like Ticketmaster DevFeed: [Design a Ticket Booking Site Like Ticketmaster]() Original publisher: [Read original article]() Author: Evan King Published: 2024-11-27T05:34:17Z Content type: tutorial Language: en Sources: [Evan King]() Topics: [Requirements](), [Availability](), [consistency](), [Latency](), [Low Latency](), [Transactions](), [CI/CD]() Tags: [availability](), [booking](), [ci-cd](), [consistency](), [latency](), [low-latency](), [requirements](), [strategy]() ### AI overview A system-design tutorial for a Ticketmaster-like ticket booking site. It defines functional requirements for viewing, searching, and booking events, then outlines non-functional requirements including availability, consistency to prevent double booking, scalability, low-latency search, and high read throughput. ### Source excerpt With Ex-Meta Staff Engineer & co-founder of hellointerview.comUnderstanding the Problem🎟 What is Ticketmaster? Ticketmaster is an online platform that allows users to purchase tickets for concerts, sports events, theater, and other live entertainment.Functional Requirements Core Requirements Users should be able to view events Users should be able to search for events Users should be able to book tickets to events Below the line (out of scope): Users should be able to view their booked events Admins or event coordinators should be able to add events Popular events should have dynamic pricing Non-Functional Requirements Core Requirements The system should prioritize availability for searching & viewing events, but should prioritize consistency for booking events (no double booking) The system should be scalable and able to handle high throughput in the form of popular events (10 million users, one event) The system should have low latency search (< 500ms) The system is read heavy, and thus needs to be able to support high read throughput (100:1) Below the line (out of scope): The system should protect user data and adhere to GDPR The system should be fault tolerant The system should provide secure transactions for purchases The system should be well tested and easy to deploy (CI/CD pipelines) The system should have regular backups Here is how the requirements might look on the whiteboard: Adding features that are out of scope is a "nice to have". It shows product thinking and gives your interviewer a chance to help you reprioritize based on what they want to see in the interview. That said, it's very much a nice to have. If additional features are not coming to you quickly, don't waste your time and move on.Planning the Approach Before you move on to designing the system, it's important to start by taking a moment to plan your strategy. Fortunately, for these common user-facing product-style questions, the plan should be straightforward: build your design up sequent ## BookFresh joins Square DevFeed: [BookFresh joins Square]() Original publisher: [Read original article]() Author: Square Engineering Published: 2014-02-26T17:06:00Z Content type: release Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [Software]() Tags: [announce](), [booking](), [customers](), [engineering](), [experience](), [local](), [self-service]() ### AI overview Square announces its acquisition of BookFresh, whose software provides local sellers with a self-service appointment-booking experience connecting them with new and existing customers. BookFresh says its product and support will continue while the team joins Square. ### Source excerpt Helping sellers grow and creating a seamless experience for their customers