*[bcc: (users,dev)@kafka.apache.org <http://kafka.apache.org>]*
Hello, The Streams Infra team invites you to attend the Streams Processing meetup to be held on Wednesday, February 5, 2020. This meetup will focus on Apache Kafka, Apache Samza and related streaming technologies. *Where*: Unify Conference room, 950 W Maude Ave, Sunnyvale *When*: 5:00 - 8:00 PM *RSVP*: Please RSVP here (only if attending in person) https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/267283444/ A streaming link will be posted approximately 30 minutes prior to the event. *Agenda:* - 5:00 PM: Doors open and catered food available 5:00 - 6:00 PM: Networking 6:00 - 6:30 PM: *High-performance data replication at Salesforce with MirusPaul Davidson, Salesforce* At Salesforce we manage high-volume Apache Kafka clusters in a growing number of data centers around the globe. In the past we relied on Kafka's Mirror Maker tool for cross-data center replication but, as the volume and variety of data increased, we needed a new solution to maintain a high standard of service reliability. In this talk, we will describe Mirus, our open-source data replication tool based on Kafka Connect. Mirus was designed for reliable, high-performance data replication at scale. It successfully replaced MirrorMaker at Salesforce and has now been running reliably in production for more than a year. We will give an overview of the Mirus design and discuss the lessons we learned deploying, tuning, and operating Mirus in a high-volume production environment. 6:30 - 7:00 PM: *Defending users from Abuse using Stream Processing at LinkedIn, Bhargav Golla, LinkedIn * When there are more than half a billion users, how can one effectively, reliably and scalably classify them as good and bad users? This talk will highlight how the Anti-Abuse team at LinkedIn leverages Streams Processing technologies like Samza and Brooklin to keep the good users in a trusted environment devoid of bad actors. 7:00 - 7:30 PM: *Enabling Mission-critical Stateful Stream Processing with Samza, Ray Manpreet Singh Matharu, LinkedIn* Samza powers a variety of large-scale business-critical stateful stream processing applications at LinkedIn. Their scale necessitates using persistent and replicated local state. Unfortunately, hard failures can cause a loss of this local state, and re-caching it can incur downtime ranging from a few minutes to hours! In this talk, we describe the systems and protocols that we've devised that bound the down time to a few seconds. We detail the tradeoffs our approach brings and how we tackle them in production at LinkedIn. 7:30 - 8:00 PM: Additional networking and Q&A Hope to see you there! *[Streams Infra team @ LinkedIn]*