Stream Processing Meetup at LinkedIn (Sunnyvale) on Feb 5th

[Prateek Maheshwari <pr...@utexas.edu>]

Hi folks,

The next Bay Area Stream Processing meetup will 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: link
<https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/267283444/>

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 Mirus
Paul 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 Anti-Abuse team at LinkedIn leverages Streams Processing
techniques 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

If you are interested in attending, please RSVP via this meetup.com link
<https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/267283444/>
.

Hope to see you there!
Prateek