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Real-Time Exactly-Once Event Processing at Uber with Apache Flink, Kafka, and Pinot
Uber faced some challenges after introducing ads on UberEats. The events they generated had to be processed quickly, reliably and accurately. These requirements were fulfilled by a system based on Apache Flink, Kafka, and Pinot that can process streams of ad events in real-time with exactly-once semantics. An article describing its architecture was published recently in the Uber Engineering blog.
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Data Collection, Standardization and Usage at Scale in the Uber Rider App
Uber Engineering recently published how it collects, standardises and uses data from the Uber Rider app. Rider data comprises all the rider's interactions with the Uber app. This data accounts for billions of events from Uber's online systems every day. Uber uses this data to deal with top problem areas such as increasing funnel conversion, user engagement, etc.
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The Evolution of Uber’s 100+ Petabyte Big Data Platform
Uber’s engineering team wrote about how their big data platform evolved from traditional ETL jobs with relational databases to one based on Hadoop and Spark. A scalable ingestion model, standard transfer format and a custom library for incremental updates are the key components of the platform.