So basically Storm’s core concept can be compared to pipes-and-filters-pattern 
BUT provides a more „user-friendly“ framework than e.g. a unix based 
pipes-and-filters processing.

btw: I haven’t come across with TRIDENT yet. Just starting to dive deeper into 
Storm as a potential technology for a real-time analytics architecture (e.g. 
KAFKA+STORM+NODE+D3).

> Am 16.11.2014 um 17:06 schrieb Nathan Leung <[email protected]>:
> 
> Storm supports fan outs, joins, various data groupings, and easier 
> scalability than the canonical Unix based pipes and filters processing.
> 
> On Nov 16, 2014 10:53 AM, "Andres Gomez Ferrer" <[email protected] 
> <mailto:[email protected]>> wrote:
> Firstly your thoughts are correct :), but do you know Trindet’s api? 
> 
> Trindet provides functions and filters equivalent to bolts
> 
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> Developer
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> 
> En 16 de noviembre de 2014 en 15:25:10, Patrick Wiener ([email protected] 
> <mailto:[email protected]>) escrito:
> 
>> 
>> Hey everybody,
>> 
>> I am working on a universities project towards a comparison towards 
>> pipes-and-filters-pattern and Storm.
>> Since I am new to Storm and its topology and operating mode I hope you can 
>> help me evaluating my train of thoughts.
>> 
>> Bolts can be considered as Filters, whereas the Spout equals the „Data 
>> Source“ (internal view) pushing tuples (data) to the downstream bolt.
>> Finally the last bolt within the topology pushes tuples into a Data Sink, 
>> e.g. Redis.
>> For the external view the Spout is also pulling (not shown in picture) from 
>> external sources such as Kafka. 
>> 
>> Overall, Spouts implement pull and push mechanism and Bolts only push 
>> mechanism
>> 
>> I know this might seem trivial to you guys but i really hope for some 
>> constructive help.
>> 
>> 
>> <34AC046A-F649-4A4E-9DA6-5FB7FE16868F>

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