Thank you I will look into that. 

-----Original Message-----
From: Svante Karlsson [mailto:svante.karls...@csi.se] 
Sent: Friday, March 2, 2018 1:50 PM
To: users@kafka.apache.org
Subject: Re: Consultant Help

try https://www.confluent.io/ - that's what they do

/svante

2018-03-02 21:21 GMT+01:00 Matt Stone <mst...@nexeohr.com>:

> We are looking for a consultant or contractor that can come onsite to 
> our Ogden, Utah location in the US, to help with a Kafka set up and 
> maintenance project.  What we need is someone with the knowledge and 
> experience to build out the Kafka environment from scratch.
>
> We are thinking they would need to be onsite for 6-12 months  to set 
> it up, and mentor some of our team so they can get up to speed to do 
> the maintenance once the contractor is gone.  If anyone has the 
> experience setting up Kafka from scratch in a Linux environment, 
> maintain node clusters, and help train others on the team how to do 
> it, and you are interested in a long term project working at the 
> client site, I would love to start up  a discussion, to see if we could use 
> you for the role.
>
> I would also be interested in hearing about any consulting firms that 
> might have resources that could help with this role.
>
> Matt Stone
>
>
> -----Original Message-----
> From: Matt Daum [mailto:m...@setfive.com]
> Sent: Friday, March 2, 2018 1:11 PM
> To: users@kafka.apache.org
> Subject: Re: Kafka Setup for Daily counts on wide array of keys
>
> Actually it looks like the better way would be to output the counts to 
> a new topic then ingest that topic into the DB itself.  Is that the 
> correct way?
>
> On Fri, Mar 2, 2018 at 9:24 AM, Matt Daum <m...@setfive.com> wrote:
>
> > I am new to Kafka but I think I have a good use case for it.  I am 
> > trying to build daily counts of requests based on a number of 
> > different attributes in a high throughput system (~1 million 
> > requests/sec. across all  8 servers).  The different attributes are 
> > unbounded in terms of values, and some will spread across 100's of 
> > millions values.  This is my current through process, let me know 
> > where I could be more efficient or if there is a better way to do it.
> >
> > I'll create an AVRO object "Impression" which has all the attributes 
> > of the inbound request.  My application servers then will on each 
> > request create and send this to a single kafka topic.
> >
> > I'll then have a consumer which creates a stream from the topic.  
> > From there I'll use the windowed timeframes and groupBy to group by 
> > the attributes on each given day.  At the end of the day I'd need to 
> > read out the data store to an external system for storage.  Since I 
> > won't know all the values I'd need something similar to the 
> > KVStore.all() but for WindowedKV Stores.  This appears that it'd be 
> > possible in 1.1 with this
> > commit: https://github.com/apache/kafka/commit/
> > 1d1c8575961bf6bce7decb049be7f10ca76bd0c5 .
> >
> > Is this the best approach to doing this?  Or would I be better using 
> > the stream to listen and then an external DB like Aerospike to store 
> > the counts and read out of it directly end of day.
> >
> > Thanks for the help!
> > Daum
> >
>

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