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From Kailis Dewer <kailistrea...@gmail.com>
Subject Re: Stream Processing Meetup@LinkedIn (Dec 4th)
Date Tue, 05 Dec 2017 02:05:53 GMT
Can some one post the link to the stream, please.

Thanks.

On Fri, Nov 17, 2017 at 3:49 PM, Becket Qin <becket.qin@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 <ppatierno@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.qin@gmail.com>
> > Sent: Friday, November 17, 2017 8:33:04 PM
> > To: dev@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.
> >
>

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