I'm looking to compare Kafka Streams vs Akka Streams in two areas:

1. For ingesting between Kafka and HDFS/RDBMS

Requirements are mainly around performance and latency. A Kafka topic can
have several million events, each corresponding to a database change
capture. When ingesting this topic into HDFS I'm also looking to partition
the data by day, typically based on a timestamp field in the event record,
aggregations on-the-fly (say by a userid field) and parquetizing
(preferably on the fly to optimize performance by reducing two I/O
operations).

2. Low-latency processing

Experiences around performances of Storm/Flink and Akka Streams would be
*very* nice. Typical use-cases are de-duping and enrichment with metrics
computation (# duplicate events/records, aggregate metrics etc). Low
latency and scalability are main considerations

On Thu, Apr 27, 2017 at 8:31 PM, Viktor Klang <[email protected]>
wrote:

>
>
> On Thu, Apr 27, 2017 at 10:39 AM, Shiva Ramagopal <[email protected]>
> wrote:
>
>> Hi,
>>
>> I have read through multiple articles describing the SMACK stack but I'm
>> having difficulty understanding the role of Akka in the stack. How does
>> Akka fit in?
>>
>
> Akka is for building the application itself.
>
>
>>
>> Also I would like to know of experiences in using Akka Streams vs Kafka
>> Connect for ingesting from Kafka into HDFS (Hive) and RDBMS. Has anyone
>> used Akka Streams for say, dynamic partitioning of events from a Kafka
>> topic into HDFS?
>>
>
> Based on what requirements?
>
>
>>
>> TIA
>> -Shiva
>>
>> --
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>
>
>
> --
> Cheers,
> √
>
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>>>>>>>>>>      Read the docs: http://akka.io/docs/
>>>>>>>>>>      Check the FAQ: 
>>>>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>>>>>>>      Search the archives: https://groups.google.com/group/akka-user
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