Scaling Real-Time Analytics: How Expedia Cut Costs by 40% While Supporting 450+ Concurrent Users
Learn how the Optics Framework enabled seamless data insights with <15-second latency for global teams
TL;DR
Situation
Expedia Group needed a scalable and cost-effective real-time analytics solution (<15 seconds latency) to process high-volume data (~4500 events/sec) and support global service partners in optimizing operations and enhancing performance.
Task
Design a solution to process and present real-time data with blazing-fast query speeds while addressing limitations of existing tools (e.g., Snowflake and Looker) in terms of scalability, latency, and user experience.
Action
Developed a new architecture using Apache Druid for real-time ingestion, optimized microservices for data processing, and built a custom modular UI library with a Data Resolver API to deliver tailored analytics based on user roles.
Result
The solution achieved a 5x increase in user base, 30-40% reduction in costs, 15-second data latency, and 99.9% SLA uptime. It supported 1,800 users with sub-1-second response times, enhancing decision-making and operational efficiency globally.
Use Cases
Real-Time Insights, Operational Efficiency, Scalability for Concurrent Users
Tech Stack/Framework
Python, Apache Druid, Apache Hive, Apache Kafka, Looker, Snowflake