High Performance Travel Websites Using In-Memory Computing

The travel reservation industry has consistently been one of the first to leverage new technology innovations to deliver great user experiences. The rise of the internet and digital business has led most travelers to expect a seamless, personalized and real-time experience whenever they book travel. But it is not easy to deliver that experience. Consumers expect real-time responsiveness for every search and transaction. However, query volumes have grown 100-1000x over the last decade and the data used to deliver each personalized result has grown 50x or more.

The GridGain® in-memory computing platform is used by companies worldwide to deliver in-memory speed and unlimited system scalability for real-time reservation systems. Several of the innovative reservation systems in online travel such as those for HomeAway and TUI Group rely on GridGain travel technology to deliver a real-time, personalized, seamless end-to-end experience to their customers.

Travel Technology to Accelerate Existing Travel Reservation Systems with No Rip-and-Replace

GridGain is widely used as in-memory data grid (IMDG) to add speed and scalability to online travel reservation systems. The GridGain IMDG is deployed between the existing application and data layers without having to re-code applications or replace databases. It is the only in-memory data grid that provides support for ANSI-99 SQL and ACID transactions and supports all leading RDBMSs, NoSQL (such as Cassandra and MongoDB) or Hadoop data stores. Companies use GridGain to lower latency up to 100x, achieve linear horizontal scalability, and consistently deliver real-time performance while supporting gigabytes or even petabytes of data with no rip and replace of their underlying database.

Some Travel Clients

HomeAway, a part of the Expedia Group family of brands and a world leader in the vacation rental industry, has more than two million places to stay in 190 countries. The site makes it easy to find and book the perfect vacation rental for any getaway. View the HomeAway presentation from the In-Memory Computing Summit North America.

TUI is the world’s leading tourism group. The broad portfolio gathered under the Group umbrella consists of strong tour operators, 1,600 travel agencies and leading online portals, six airlines with around 150 aircraft, over 380 hotels, 16 cruise liners and many incoming agencies in all major holiday destinations around the globe.

Deliver a More Personalized, Seamless, End-to-End Travel Booking Experience

To improve the travel booking experience for customers, you have to be able to track, analyze and automate decisions in real-time during each step of the customer’s journey across any offline or digital channel. The GridGain in-memory computing platform offers an IMDG, a distributed in-memory database (IMDB), streaming analytics, and continuous learning including machine and deep learning features which are a perfect complement to your existing travel technology. Companies can use all of these capabilities as part of each transaction or interaction:

Dynamic pricing that improves conversion rates

Automation that increases self-service and reduces end-to-end processes

Advanced real-time analytics that improve cross-selling and promotions

APIs that expose these capabilities for new applications, devices and partners


This white paper reviews why IMC makes sense for today’s fast-data and big-data applications, dispels common myths about IMC, and clarifies the distinctions among IMC product categories to make the process of choosing the right IMC solution for a specific use case much easier.
This white paper covers the architecture, key capabilities, and features of GridGain®, as well as its key integrations for leading RDBMSs, Apache Spark™, Apache Cassandra™, MongoDB® and Apache Hadoop™. It describes how GridGain adds speed and unlimited horizontal scalability to existing or new OLTP or OLAP applications, HTAP applications, streaming analytics, and continuous learning use cases for machine or deep learning.