Can some one post the link to the stream, please. Thanks.
On Fri, Nov 17, 2017 at 3:49 PM, Becket Qin <becket....@gmail.com> wrote: > Hi Paolo, > > Yes, we will stream the meetup. Usually the link will be posted to the > meetup website a couple of hours before the meetup. Feel free to ping me if > you don't see it. > > Thanks, > > Jiangjie (Becket) Qin > > On Fri, Nov 17, 2017 at 11:59 AM, Paolo Patierno <ppatie...@live.com> > wrote: > > > Hi Becket, > > I watched some of these meetups on the related YouTube channel in the > past. > > Will be it available in streaming or just recorded for watching it later > ? > > > > Thanks > > Paolo > > ________________________________ > > From: Becket Qin <becket....@gmail.com> > > Sent: Friday, November 17, 2017 8:33:04 PM > > To: d...@kafka.apache.org; users@kafka.apache.org > > Subject: Stream Processing Meetup@LinkedIn (Dec 4th) > > > > Hi Kafka users and developers, > > > > We are going to host our quarterly Stream Processing Meetup@LinkedIn on > > Dec > > 4. There will be three speakers from Slack, Uber and LinkedIn. Please > check > > the details below if you are interested. > > > > Thanks, > > > > Jiangjie (Becket) Qin > > > > *Stream Processing with Apache Kafka & Apache Samza* > > > > - Meetup Link: here > > <https://www.meetup.com/Stream-Processing-Meetup- > > LinkedIn/events/244889719/> > > - When: Dec 4th 2017 @ 6:00pm > > - Where: LinkedIn Building F , 605 West Maude Avenue, Sunnyvale, CA > > (edit > > map > > <https://www.meetup.com/Stream-Processing-Meetup- > > LinkedIn/events/244889719/> > > ) > > > > > > *Abstract* > > > > 1. Stream processing using Samza-SQL @ LinkedIn > > > > *Speaker: Srinivasulu Punuru, LinkedIn* > > Imagine if you can develop and run a stream processing job in few minutes > > and Imagine if a vast majority of your organization (business analysts, > > Product manager, Data scientists) can do this on their own without a need > > for a development team. > > Need for real time insights into the big data is increasing at a rapid > > pace. The traditional Java based development model of developing, > deploying > > and managing the stream processing application is becoming a huge > > constraint. > > With Samza SQL we can simplify application development by enabling users > to > > create stream processing applications and get real time insights into > their > > business using SQL statements. > > > > In this talk we try to answer the following questions > > > > 1. How SQL language can be used to perform stream processing? > > 2. How is Samza SQL implemented - Architecture? > > 3. How can you deploy Samza SQL in your company? > > > > > > 2. Streaming data pipeline @ Slack > > *Speaker:- Ananth Packkildurai, Slack* > > *Abstract: *Slack is a communication and collaboration platform for > teams. > > Our millions of users spend 10+ hrs connected to the service on a typical > > working day. They expect reliability, low latency, and extraordinarily > rich > > client experiences across a wide variety of devices and network > conditions. > > It is crucial for the developers to get the realtime insights on Slack > > operational metrics. > > In this talk, I will talk about how our data platform evolves from the > > batch system to near realtime. I will also touch base on how Samza helps > us > > to build low latency data pipelines & Experimentation framework. > > > > 3. Improving Kafka at-least-once performance > > *Speaker: Ying Zheng, Uber* > > *Abstract:* > > Abstract: > > At Uber, we are seeing an increased demand for Kafka at-least-once > > delivery. So far, we are running a dedicated at-least-once Kafka cluster > > with special settings. With a very low workload, the dedicated > > at-least-once cluster has been working well for more than a year. Now, > when > > we want to turn on at-least-once producing on all the Kafka clusters, the > > at-least-once producing performance is one of the concerns. I have > worked a > > couple of months to investigate the Kafka performance issues. With Kafka > > code changes and Kafka / Java configuration changes, I have reduced > > at-least-once producing latency by about 60% to 70%. Some of those > > improvements can also improve the general Kafka throughput or reducing > > end-to-end Kafka latency, when ack = 0 or ack = 1. > > >
