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From Gavin Yue <yue.yuany...@gmail.com>
Subject Re: Big Data tech stack (was Spark vs. Storm)
Date Wed, 02 Jul 2014 23:10:00 GMT
Isn't this what Yarn or Mesos are trying to do?  Separate the resources
management and applications. Run whatever suitable above them.  Spark also
could run above yanr or mesos. Spark was designed for iteration intensive
computing like Machine learning algorithms.

Storm is quite different.  It is not designed for big data stored in the
hard disk. It is inspired by the stream data like tweets. On the other
side, Map-Reduce/HDFS was initially designed to handle stored webpage to
build up index.

Hadoop is on the way to become a generic Big Data analysis framework.
HontonWorks and Cloudera are trying to make it much easier on management
and deployment.



On Wed, Jul 2, 2014 at 4:25 PM, Adaryl "Bob" Wakefield, MBA <
adaryl.wakefield@hotmail.com> wrote:

>   You know what I’m really trying to do? I’m trying to come up with a
> best practice technology stack. There are so many freaking projects it is
> overwhelming. If I were to walk into an organization that had no Big Data
> capability, what mix of projects would be best to implement based on
> performance, scalability and easy of use/implementation? So far I’ve got:
> Ubuntu
> Hadoop
> Cassandra (Seems to be the highest performing NoSQL database out there.)
> Storm (maybe?)
> Python (Easier than Java. Maybe that shouldn’t be a concern.)
> Hive (For people to leverage their existing SQL skillset.)
>
> That would seem to cover transaction processing and warehouse storage and
> the capability to do batch and real time analysis. What am I leaving out or
> what do I have incorrect in my assumptions?
>
> B.
>
>
>
>  *From:* Stephen Boesch <javadba@gmail.com>
> *Sent:* Wednesday, July 02, 2014 3:07 PM
> *To:* user@hadoop.apache.org
> *Subject:* Re: Spark vs. Storm
>
>  Spark Streaming discretizes the stream by configurable intervals of no
> less than 500Milliseconds. Therefore it is not appropriate for true real
> time processing.So if you need to capture events in the low 100's of
> milliseonds range or less than stick with Storm (at least for now).
>
> If you can afford one second+ of latency then spark provides advantages of
> interoperability with the other Spark components and capabilities.
>
>
> 2014-07-02 12:59 GMT-07:00 Shahab Yunus <shahab.yunus@gmail.com>:
>
>> Not exactly. There are of course  major implementation differences and
>> then some subtle and high level ones too.
>>
>> My 2-cents:
>>
>> Spark is in-memory M/R and it simulated streaming or real-time
>> distributed process for large datasets by micro-batching. The gain in speed
>> and performance as opposed to batch paradigm is in-memory buffering or
>> batching (and I am here being a bit naive/crude in explanation.)
>>
>> Storm on the other hand, supports stream processing even at a single
>> record level (known as tuple in its lingo.) You can do micro-batching on
>> top of it as well (using Trident API which is good for state maintenance
>> too, if your BL requires that). This is more applicable where you want
>> control to a single record level rather than set, collection or batch of
>> records.
>>
>> Having said that, Spark Streaming is trying to simulate Storm's extreme
>> granular approach but as far as I recall, it still is built on top of core
>> Spark (basically another level of abstraction over core Spark constructs.)
>>
>> So given this, you can pick the framework which is more attuned to your
>> needs.
>>
>>
>> On Wed, Jul 2, 2014 at 3:31 PM, Adaryl "Bob" Wakefield, MBA <
>> adaryl.wakefield@hotmail.com> wrote:
>>
>>>   Do these two projects do essentially the same thing? Is one better
>>> than the other?
>>>
>>
>>
>
>

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