Akka Streams is an excellent choice for this use case. I would even say
that it is the canonical use case for Akka Streams. We have probably not
highlighted that enough, yet.

Cheers,
Patrik
tors 14 juli 2016 kl. 13:22 skrev James Matlik <james.mat...@gmail.com>:

> Using Akka streams for ETL is our primary use case. The back pressure
> support has been extremely useful in helping us maximize throughout while
> at the same time avoid overwhelming the multiple external rest services we
> query against. By maintaining dedicated, fixed sized dispatcher pools, we
> can easily use our legacy blocking client SDKs over a fixed max number of
> concurrent connections/requests. Then the ETL can process as fast as it
> possibly can within those constraints.
>
> We found the learning curve to be on the steep side, but once it clicks,
> the power and ease of use Streams provides is... impressive...
> refreshing... exhilarating... addicting.. take your pick.
>
> On Jul 14, 2016 12:28 AM, "Beno" <andy.zelin...@gmail.com> wrote:
>
>> Ive been using Akka streaming for a use case which I dont see much about
>> - that of a small/moderate scale ETL or simple processing pipeline. Im
>> relatively new to it all, so I just wanted to see if I might be missing
>> something that would change my opinion, which is that Akka Streams is among
>> the best tools for data cleaning--  the graph dsl is so easy to code with
>> and reason about.
>>
>> The details: Batch processing to clean and curate data, with external
>> calls RESTful requests as part of the flow.
>>
>> Source[A] (read from file or DB) ~> Flow[A,B](some transformation
>> function) ~>  Flow[B,C] (by way of a RESTful request/response) ~>
>> Flow[C,D](graph query) ~> Sink[D](to DB)
>>
>> Where Source might be 50,000 lines in a file or rows in a table.
>>
>> Thanks for any feedback
>>
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