Trivago aggregates billions of price data points to provide accurate price context to its users. By leveraging historic price data and utilizing tools like Kafka streams and BigQuery, they ensure quick and scalable data aggregation. Despite challenges related to data volume, coverage issues, and outlier detection, trivago successfully implemented a system that delivers price trends and metrics for various use cases. The project highlights the importance of simplicity, explorative analysis, careful assumption management, and thorough documentation.
Table of contents
Why do we need aggregated price data?Our Solution: Price StreamChallengesCurrent features using Price StreamsLearningsNext stepsSort: