The software industry has learned that the world’s data can be represented as unbounded queues of changes. It can be sliced into sliding windows. It can be aggregated, rolled up, and analyzed. We can choose a number of ways to do this work such as using Kafka Streams or Spark Streaming. We can opt for Apache Beam, Storm, Samza, Flume, or Flink. We have a large pool of options on which we can build powerful systems, but there is accidental complexity lurking in any of the choices:

• What if I need to rebuild all the data?
• How do I know when my system is not healthy?
• How do I reason about time in this system?
• What if things arrive out of order?
• How do I know things have arrived?

This track walks through uses of streaming technologies at large, the problems encountered, and how teams are coping with the state of this new world. As we approach maturity in streaming systems the companies using these systems are growing ecosystems and best practices around building and operating them. They are discovering new ways to reason about monitoring, testing, performance, and failure. This track is an opportunity to learn from their experiences.