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From Becket Qin <becket....@gmail.com>
Subject Re: Stream Processing Meetup@LinkedIn (Dec 4th)
Date Fri, 17 Nov 2017 20:49:02 GMT
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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