Most of the stream processing in the Data Pipeline happens inside of an internal project called PaaStorm, which is storm-like. It was built to take advantage of our platform as a service (http://engineeringblog.yelp.com/2015/11/introducing-paasta-a...), which handles process scheduling really well. Architecturally, it's pretty similar to Samza, with distributed processes communicating using Kafka.
We do use Spark streaming, and are starting to use Kafka Streams and Data Flow, where they're a better fit. I'm personally most excited about Beam/Flink. We'll probably end up replacing the PaaStorm internals with some other tool, when one with good python support matures. Beam's event-time handling and windowing seem really promising at this point. https://www.oreilly.com/ideas/the-world-beyond-batch-streami... is a great overview of the different concerns for stream processing.
We do use Spark streaming, and are starting to use Kafka Streams and Data Flow, where they're a better fit. I'm personally most excited about Beam/Flink. We'll probably end up replacing the PaaStorm internals with some other tool, when one with good python support matures. Beam's event-time handling and windowing seem really promising at this point. https://www.oreilly.com/ideas/the-world-beyond-batch-streami... is a great overview of the different concerns for stream processing.