Uber’s real-time infrastructure handles petabytes of data daily, ensuring smooth operations. The company’s engineering team uses several technologies, including Apache Samza, Apache Flink, and Apache Kafka, to process this massive flow of information. Samza’s stream processing capabilities allow Uber to handle millions of events per second, while Flink offers a robust, scalable data processing framework. Kafka, on the other hand, acts as a distributed messaging system, facilitating the transfer of data between Uber’s various services.
The data processed includes GPS locations, trip details, and estimated time of arrivals (ETAs). This information is used to match drivers with riders, calculate fares, and predict ETAs. Uber’s engineers have also developed a custom in-memory store, named Catalyst, to handle real-time geospatial queries.
Uber’s real-time infrastructure faces several challenges, such as maintaining low latency, ensuring data accuracy, and managing the high volume of data. The team addresses these issues by using a combination of open-source technologies, custom-built solutions, and innovative engineering practices. Their efforts ensure Uber’s services remain reliable and efficient, even under the heavy load of continuous real-time data processing.
Go to source article: https://blog.det.life/how-does-uber-build-real-time-infrastructure-to-handle-petabytes-of-data-every-day-ddf5fe9b5d2c