Join us for an Apache Kafka meetup on October 29th from 6:00pm, hosted by Uber in Palo Alto. The address, agenda and speaker information can be found below. See you there!
6:00pm: Doors open
6:00pm - 6:30pm: Pizza, Drinks and Networking
6:30pm - 7:15pm: Matthias J. Sax, Confluent
7:15pm - 8:00pm: Xiaoman Dong, Uber
8:00pm - 8:30pm: Additional Q&A & Networking
Speaker: Matthias J. Sax
Bio: Matthias is a Kafka PMC member and software engineer at Confluent working mainly on Kafka’s Streams API. Prior to Confluent, he was a PhD student at Humboldt-University of Berlin, conducting research on data stream processing systems. Matthias is also a committer at Apache Flink and Apache Storm.
Title: What’s the Time? …and Why?
Abstract: Data stream processing is built on the core concept of time. However, understanding time semantics and reasoning about time is not simple, especially if deterministic processing is expected. In this talk, we explain the difference between processing, ingestion, and event time and what their impact is on data stream processing. Furthermore, we explain how Kafka clusters and stream processing applications must be configured to achieve specific time semantics. Finally, we deep dive into the time semantics of the Kafka Streams DSL and KSQL operators, and explain in detail how the runtime handles time. Apache Kafka offers many ways to handle time on the storage layer, ie, the brokers, allowing users to build applications with different semantics. Time semantics in the processing layer, ie, Kafka Streams and KSQL, are even richer, more powerful, but also more complicated. Hence, it is paramount for developers, to understand different time semantics and to know how to configure Kafka to achieve them. Therefore, this talk enables developers to design applications with their desired time semantics, help them to reason about the runtime behavior with regard to time, and allow them to understand processing/query results.
Talk 2: (LAST MINUTE CHANGE)
Speaker: Xiaoman Dong
Title: Federated Apache Kafka® at Uber
Abstract: Uber has one of the largest Kafka deployments in the industry. To improve the scalability and availability, we developed and deployed a novel federated Kafka cluster setup which hides the cluster details from producers/consumers. Users do not need to know which cluster a topic resides and the clients view a “logical cluster”. The federation layer will map the clients to the actual physical clusters, and keep the location of the physical cluster transparent from the user. Cluster federation brings us several benefits to support our business growth and ease our daily operation.
Don't forget to join our Community Slack Team (https://launchpass.com/confluentcommunity) !
If you would like to speak or host our next event please let us know! [masked]
NOTE: We are unable to cater for any attendees under the age of 18. Please do not sign up for this event if you are under 18.