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From Simon Ball <sb...@hortonworks.com>
Subject Re: Metron-265 Model as a Service
Date Thu, 07 Jul 2016 22:33:31 GMT
There is an interesting division of concerns here that might impact the design.

If we're looking to cache things like dga which operate on a subset of the enriched Metron
data model, then we essentially need to push the feature selection, or at least feature projection
elements of the model to the edge (the bolt) to produce a cache key. This seems to make sense
in the context of the function call to the model proposed, but means that the model call does
not apply to a whole Metron data record, but a subset determined by that call on the dsl.
This implicitly pushes model related concerns (feature selection) outside of the canonical
scope for defining the models themselves (the model service), which loses model encapsulation.

In essence you would be embedding the feature selection (projection) of the model engine in
the storm bolts in order to make caching possible, which would need some sort of central control,
and rationalisation to avoid cache misses between multiple models with slightly different
feature sets. This could add complexity, or reduce cache utilisation really quickly with model
scale. 

Simon 


> On 7 Jul 2016, at 18:51, Casey Stella <cestella@gmail.com> wrote:
> 
> Great questions Andrew.  Thanks for the interest. :)
> 
> RE:: "which is why there would be a caching layer set in front of it at the
> Storm bolt level"
> 
> Right now we have a LRU caching layer in front of the HBase enrichment
> adapters, so it would work similarly.  You can imagine, the range of inputs
> is likely not perfectly random, so it's reasonable for the cache to have a
> non-empty working set.  Take for instance a DGA model; the input would be a
> domain and most organizations will have an uneven distribution of domains
> they access with a heavy skew toward a small number.
> 
> RE: In this scenario, you can at least scale out via load balancing (i.e.
> multiple model services serving the same model) since the models are
> immutable.
> 
> I am talking about model execution here.  The endpoints are distributed
> across the cluster and the storm bolt chooses a service to use (with a bias
> toward using one that is local to that bolt) and the request is made to the
> endpoint, which scores the input and returns the response.
> 
> Model service, if that term means what I think it means, is almost entirely
> done inside of zookeeper.  For clarity, I'm talking about service discovery
> (bolt discovers which endpoints serve which models) and model updates.  We
> are not sending the model around to any bolts or any such thing, just for
> clarity sake.
> 
> 
> 
> On Thu, Jul 7, 2016 at 9:47 AM, Andrew Psaltis <psaltis.andrew@gmail.com>
> wrote:
> 
>> Thanks Casey! Couple of quick questions.
>> 
>> RE:: "which is why there would be a caching layer set in front of it at the
>> Storm bolt level"
>> Hmm, would this be of the results of model execution? Would this really
>> work when each tuple may contain totally different data? Or is the caching
>> going to be smart enough that it will look at all the data passed in and
>> determine that an identical tuple has already been evaluated so serve the
>> result out of cache?
>> 
>> RE: "Also, we would prefer local instances of the service when and where
>> possible"
>> Perfect makes sense.
>> 
>> RE: Serving many models from every storm bolt is also fairly expensive.
>> I can see how it could be, but couldn't  we can make sure that not all
>> models live in every bolt?
>> 
>> RE: In this scenario, you can at least scale out via load balancing (i.e.
>> multiple model services serving the same model) since the models are
>> immutable.
>> This seems to address the model serving, not model execution service.
>> Having yet one more layer to scale and mange also seems like it
>> would further complicate things. Could we not just also scale the bolts?
>> 
>> Thanks,
>> Andrew
>> 
>> 
>> 
>> 
>>> On Thu, Jul 7, 2016 at 12:37 PM, Casey Stella <cestella@gmail.com> wrote:
>>> 
>>> So, regarding the expense of communication; I tend to agree that it is
>>> expensive, which is why there would be a caching layer set in front of it
>>> at the Storm bolt level.  Also, we would prefer local instances of the
>>> service when and where possible.  Serving many models from every storm
>> bolt
>>> is also fairly expensive.  In this scenario, you can at least scale out
>> via
>>> load balancing (i.e. multiple model services serving the same model)
>> since
>>> the models are immutable.
>>> 
>>> On Thu, Jul 7, 2016 at 9:24 AM, Andrew Psaltis <psaltis.andrew@gmail.com
>>> 
>>> wrote:
>>> 
>>>> OK that makes sense. So the doc attached to this JIRA[1] just speaks to
>>> the
>>>> Model serving. Is there a doc for the model service? And by making
>> this a
>>>> separate service we are saying that for every  “MODEL_APPLY(model_name,
>>>> param_1, param_2, …, param_n)” we are potentially going to go across
>> the
>>>> wire and have a model executed? That seems pretty expensive, no?
>>>> 
>>>> Thanks,
>>>> Andrew
>>>> 
>>>> [1] https://issues.apache.org/jira/browse/METRON-265
>>>> 
>>>>> On Thu, Jul 7, 2016 at 12:20 PM, Casey Stella <cestella@gmail.com>
>>>> wrote:
>>>> 
>>>>> The "REST" model service, which I place in quotes because there is
>> some
>>>>> strong discussion about whether REST is a reasonable transport for
>>> this,
>>>> is
>>>>> responsible for providing the model.  The scoring/model application
>>>> happens
>>>>> in the model service and the results get transferred back to the
>> storm
>>>> bolt
>>>>> that calls it.
>>>>> 
>>>>> Casey
>>>>> 
>>>>> On Thu, Jul 7, 2016 at 9:17 AM, Andrew Psaltis <
>>> psaltis.andrew@gmail.com
>>>>> 
>>>>> wrote:
>>>>> 
>>>>>> Trying to make sure I grok this thread and the word doc attached
to
>>> the
>>>>>> JIRA. The word doc and JIRA speak to a Model Service Service and
>> that
>>>> the
>>>>>> REST service will be responsible for serving up models. However,
>> part
>>>> of
>>>>>> this conversation seems to suggest that the model execution will
>>>> actually
>>>>>> occur at the REST service .. in particular this comment from James:
>>>>>> 
>>>>>> "There are several reasons to decouple model execution from Storm:"
>>>>>> 
>>>>>> If the model execution is decoupled from Storm then it appears that
>>> the
>>>>>> REST service will be executing the model, not just serving it up,
>> is
>>>> that
>>>>>> correct?
>>>>>> 
>>>>>> Thanks,
>>>>>> Andrew
>>>>>> 
>>>>>> 
>>>>>> 
>>>>>>> On Thu, Jul 7, 2016 at 11:51 AM, Casey Stella <cestella@gmail.com>
>>>>>> wrote:
>>>>>> 
>>>>>>> Regarding the performance of REST:
>>>>>>> 
>>>>>>> Yep, so everyone seems to be worried about the performance
>>>> implications
>>>>>> for
>>>>>>> REST.  I made this comment on the JIRA, but I'll repeat it here
>> for
>>>>>> broader
>>>>>>> discussion:
>>>>>>> 
>>>>>>> My choice of REST was mostly due to the fact that I want to
>> support
>>>>>>>> multi-language (I think that's a very important requirement)
>> and
>>>>> there
>>>>>>> are
>>>>>>>> REST libraries for pretty much everything. I do agree, however,
>>>> that
>>>>>> JSON
>>>>>>>> transport can get chunky. How about a compromise and use
REST,
>>> but
>>>>> the
>>>>>>>> input and output payloads for scoring are Maps encoded in
>> msgpack
>>>>>> rather
>>>>>>>> than JSON. There is a msgpack library for pretty much every
>>>> language
>>>>>> out
>>>>>>>> there (almost) and certainly all of the ones we'd like to
>> target.
>>>>>>> 
>>>>>>> 
>>>>>>>> The other option is to just create and expose protobuf bindings
>>>>> (thrift
>>>>>>>> doesn't have a native client for R) for all of the languages
>> that
>>>> we
>>>>>> want
>>>>>>>> to support. I'm perfectly fine with that, but I had some
>> worries
>>>>> about
>>>>>>> the
>>>>>>>> maturity of the bindings.
>>>>>>> 
>>>>>>> 
>>>>>>>> The final option, as you suggest, is to just use raw sockets.
I
>>>> think
>>>>>> if
>>>>>>>> we went that route, we might have to create a layer for each
>>>> language
>>>>>>>> rather than relying on model creators to create a TCP server.
I
>>>>> thought
>>>>>>>> that might be a bit onerous for a MVP.
>>>>>>> 
>>>>>>> 
>>>>>>>> Given the discussion, though, what it has made me aware of
is
>>> that
>>>> we
>>>>>>>> might not want to dictate a transport mechanism at all, but
>>> rather
>>>>>> allow
>>>>>>>> that to be pluggable and extensible (so each model would
be
>>>>> associated
>>>>>>> with
>>>>>>>> a transport mechanism handler that would know how to
>> communicate
>>> to
>>>>> it.
>>>>>>> We
>>>>>>>> would provide default mechanisms for msgpack over REST, JSON
>> over
>>>>> REST
>>>>>>> and
>>>>>>>> maybe msgpack over raw TCP.) Thoughts?
>>>>>>> 
>>>>>>> 
>>>>>>> Regarding PMML:
>>>>>>> 
>>>>>>> I tend to agree with James that PMML is too restrictive as to
>>> models
>>>> it
>>>>>> can
>>>>>>> represent and I have not had great experiences with it in
>>> production.
>>>>>>> Also, the open source libraries for PMML have licensing issues
>>> (jpmml
>>>>>>> requires an older version to accommodate our licensing
>>> requirements).
>>>>>>> 
>>>>>>> Regarding workflow:
>>>>>>> 
>>>>>>> At the moment, I'd like to focus on getting a generalized
>>>>> infrastructure
>>>>>>> for model scoring and updating put in place.   This means, this
>>>>>>> architecture takes up the baton from the point when a model is
>>>>>>> trained/created.  Also, I have attempted to be generic in terms
>> of
>>>>> output
>>>>>>> of the model (a map of results) so it can fit any type of model
>>> that
>>>> I
>>>>>> can
>>>>>>> think of.  If that's not the case, let me know, though.
>>>>>>> 
>>>>>>> For instance, for clustering, you would probably emit the cluster
>>> id
>>>>>>> associated with the input and that would be added to the message
>> as
>>>> it
>>>>>>> passes through the storm topology.  The model is responsible
for
>>>>>> processing
>>>>>>> the input and constructing properly formed output.
>>>>>>> 
>>>>>>> Casey
>>>>>>> 
>>>>>>> 
>>>>>>> On Tue, Jul 5, 2016 at 3:45 PM, Debo Dutta (dedutta) <
>>>>> dedutta@cisco.com>
>>>>>>> wrote:
>>>>>>> 
>>>>>>>> Following up on the thread a little late …. Awesome start
>> Casey.
>>>> Some
>>>>>>>> comments:
>>>>>>>> * Model execution
>>>>>>>> ** I am guessing the model execution will be on YARN only
for
>>> now.
>>>>> This
>>>>>>> is
>>>>>>>> fine, but the REST call could have an overhead - depends
on the
>>>>> speed.
>>>>>>>> * PMML: won’t we have to choose some DSL for describing
models?
>>>>>>>> * Model:
>>>>>>>> ** workflow vs a model -  do we care about the “workflow"
that
>>>> leads
>>>>> to
>>>>>>>> the models or just the “model"? For example, we might start
>> with
>>> n
>>>>>>> features
>>>>>>>> —> do feature selection to choose k (or apply a transform
>>> function)
>>>>> —>
>>>>>>>> apply a model etc
>>>>>>>> * Use cases - I can see this working for n-ary classification
>>> style
>>>>>>> models
>>>>>>>> easily. Will the same mechanism be used for stuff like
>> clustering
>>>> (or
>>>>>>>> intermediate steps like feature selection alone).
>>>>>>>> 
>>>>>>>> Thx
>>>>>>>> debo
>>>>>>>> 
>>>>>>>> 
>>>>>>>> 
>>>>>>>> 
>>>>>>>>> On 7/5/16, 3:24 PM, "James Sirota" <jsirota@apache.org>
wrote:
>>>>>>>>> 
>>>>>>>>> Simon,
>>>>>>>>> 
>>>>>>>>> There are several reasons to decouple model execution
from
>>> Storm:
>>>>>>>>> 
>>>>>>>>> - Reliability: It's much easier to handle a failed service
>> than
>>> a
>>>>>> failed
>>>>>>>> bolt.  You can also troubleshoot without having to bring
down
>> the
>>>>>>> topology
>>>>>>>>> - Complexity: you de-couple the model logic from Storm
logic
>> and
>>>> can
>>>>>>>> manage it independently of Storm
>>>>>>>>> - Portability: you can swap the model guts (switch from
Spark
>> to
>>>>>> Flink,
>>>>>>>> etc) and as long as you maintain the interface you are good
to
>> go
>>>>>>>>> - Consistency: since we want to expose our models the
same way
>>> we
>>>>>> expose
>>>>>>>> threat intel then it makes sense to expose them as a service
>>>>>>>>> 
>>>>>>>>> In our vision for Metron we want to make it easy to uptake
and
>>>> share
>>>>>>>> models.  I think well-defined interfaces and programmatic
ways
>> of
>>>>>>>> deployment, lifecycle management, and scoring via well-defined
>>> REST
>>>>>>>> interfaces will make this task easier.  We can do a few things
>> to
>>>>>>>>> 
>>>>>>>>> With respect to PMML I personally had not had much luck
with
>> it
>>> in
>>>>>>>> production.  I would prefer models as POJOs.
>>>>>>>>> 
>>>>>>>>> Thanks,
>>>>>>>>> James
>>>>>>>>> 
>>>>>>>>> 04.07.2016, 16:07, "Simon Ball" <sball@hortonworks.com>:
>>>>>>>>>> Since the models' parameters and execution algorithm
are
>>> likely
>>>> to
>>>>>> be
>>>>>>>> small, why not have the model store push the model changes
and
>>>>> scoring
>>>>>>>> direct to the bolts and execute within storm. This negates
the
>>>>> overhead
>>>>>>> of
>>>>>>>> a rest call to the model server, and the need for discovery
of
>>> the
>>>>>> model
>>>>>>>> server in zookeeper.
>>>>>>>>>> 
>>>>>>>>>> Something like the way ranger policies are updated
/ cached
>> in
>>>>>> plugins
>>>>>>>> would seem to make sense, so that we're distributing the
model
>>>>>> execution
>>>>>>>> directly into the enrichment pipeline rather than collecting
>> in a
>>>>>> central
>>>>>>>> service.
>>>>>>>>>> 
>>>>>>>>>> This would work with simple models on single events,
but may
>>>>>> struggle
>>>>>>>> with correlation based models. However, those could be handled
>> in
>>>>> storm
>>>>>>> by
>>>>>>>> pushing into a windowing trident topology or something of
the
>>> sort,
>>>>> or
>>>>>>> even
>>>>>>>> with a parallel spark streaming job using the same method
of
>>>>>> distributing
>>>>>>>> models.
>>>>>>>>>> 
>>>>>>>>>> The real challenge here would be stateful online
models,
>> which
>>>>> seem
>>>>>>>> like a minority case which could be handled by a shared state
>>> store
>>>>>> such
>>>>>>> as
>>>>>>>> HBase.
>>>>>>>>>> 
>>>>>>>>>> You still keep the ability to run different languages,
and
>>>>>> platforms,
>>>>>>>> but wrap managing the parallelism in storm bolts rather than
>> yarn
>>>>>>>> containers.
>>>>>>>>>> 
>>>>>>>>>> We could also consider basing the model protocol
on a a
>> common
>>>>> model
>>>>>>>> language like pmml, thong that is likely to be highly limiting.
>>>>>>>>>> 
>>>>>>>>>> Simon
>>>>>>>>>> 
>>>>>>>>>>> On 4 Jul 2016, at 22:35, Casey Stella <cestella@gmail.com
>>> 
>>>>> wrote:
>>>>>>>>>>> 
>>>>>>>>>>> This is great! I'll capture any requirements
that anyone
>>> wants
>>>>> to
>>>>>>>>>>> contribute and ensure that the proposed architecture
>>>>> accommodates
>>>>>>>> them. I
>>>>>>>>>>> think we should focus on a minimal set of requirements
and
>>> an
>>>>>>>> architecture
>>>>>>>>>>> that does not preclude a larger set. I have found
that the
>>>> best
>>>>>>>> driver of
>>>>>>>>>>> requirements are installed users. :)
>>>>>>>>>>> 
>>>>>>>>>>> For instance, I think a lot of questions about
how often
>> to
>>>>>> update a
>>>>>>>> model
>>>>>>>>>>> and such should be represented in the architecture
by the
>>>>> ability
>>>>>> to
>>>>>>>>>>> manually update a model, so as long as we have
the ability
>>> to
>>>>>>> update,
>>>>>>>>>>> people can choose when and where to do it (i.e.
time based
>>> or
>>>>> some
>>>>>>>> other
>>>>>>>>>>> trigger). That being said, we don't want to cause
too much
>>>>> effort
>>>>>>> for
>>>>>>>> the
>>>>>>>>>>> user if we can avoid it with features.
>>>>>>>>>>> 
>>>>>>>>>>> In terms of the questions laid out, here are
the
>> constraints
>>>>> from
>>>>>>> the
>>>>>>>>>>> proposed architecture as I see them. It'd be
great to get
>> a
>>>>> sense
>>>>>> of
>>>>>>>>>>> whether these constraints are too onerous or
where they're
>>> not
>>>>>>>> opinionated
>>>>>>>>>>> enough :
>>>>>>>>>>> 
>>>>>>>>>>>  - Model versioning and retention
>>>>>>>>>>>  - We do have the ability to update models, but
the
>>> training
>>>>> and
>>>>>>>> decision
>>>>>>>>>>>     of when to update the model is left up to
the user.
>> We
>>>> may
>>>>>> want
>>>>>>>> to think
>>>>>>>>>>>     deeply about when and where automated model
updates
>> can
>>>> fit
>>>>>>>>>>>     - Also, retention is currently manual. It
might be an
>>>>> easier
>>>>>>> win
>>>>>>>> to
>>>>>>>>>>>     set up policies around when to sunset models
(after
>>> newer
>>>>>>>> versions are
>>>>>>>>>>>     added, for instance).
>>>>>>>>>>>  - Model access controls management
>>>>>>>>>>>  - The architecture proposes no constraints around
this.
>> As
>>>> it
>>>>>>> stands
>>>>>>>>>>>     now, models are held in HDFS, so it would
inherit the
>>>> same
>>>>>>>> security
>>>>>>>>>>>     capabilities from that (user/group permissions
+
>>> Ranger,
>>>>> etc)
>>>>>>>>>>>  - Requirements around concept drift
>>>>>>>>>>>  - I'd love to hear user requirements around
how we could
>>>>>>>> automatically
>>>>>>>>>>>     address concept drift. The architecture as
it's
>>> proposed
>>>>>> let's
>>>>>>>> the user
>>>>>>>>>>>     decide when to update models.
>>>>>>>>>>>  - Requirements around model output
>>>>>>>>>>>  - The architecture as it stands just mandates
a JSON map
>>>> input
>>>>>> and
>>>>>>>> JSON
>>>>>>>>>>>     map output, so it's up to the model what
they want to
>>>> pass
>>>>>>> back.
>>>>>>>>>>>     - It's also up to the model to document its
own
>> output.
>>>>>>>>>>>  - Any model audit and logging requirements
>>>>>>>>>>>  - The architecture proposes no constraints around
this.
>>> I'd
>>>>> love
>>>>>>> to
>>>>>>>> see
>>>>>>>>>>>     community guidance around this. As it stands,
we just
>>> log
>>>>>> using
>>>>>>>> the same
>>>>>>>>>>>     mechanism as any YARN application.
>>>>>>>>>>>  - What model metrics need to be exposed
>>>>>>>>>>>  - The architecture proposes no constraints around
this.
>>> I'd
>>>>> love
>>>>>>> to
>>>>>>>> see
>>>>>>>>>>>     community guidance around this.
>>>>>>>>>>>     - Requirements around failure modes
>>>>>>>>>>>  - We briefly touch on this in the document,
but it is
>>>> probably
>>>>>> not
>>>>>>>>>>>     complete. Service endpoint failure will result
in
>>>>>> blacklisting
>>>>>>>> from a
>>>>>>>>>>>     storm bolt perspective and node failure should
result
>>> in
>>>> a
>>>>>> new
>>>>>>>> container
>>>>>>>>>>>     being started by the Yarn application master.
Beyond
>>>> that,
>>>>>> the
>>>>>>>>>>>     architecture isn't explicit.
>>>>>>>>>>> 
>>>>>>>>>>>> On Mon, Jul 4, 2016 at 1:49 PM, James Sirota
<
>>>>> jsirota@apache.org
>>>>>>> 
>>>>>>>> wrote:
>>>>>>>>>>>> 
>>>>>>>>>>>> I left a comment on the JIRA. I think your
design is
>>>> promising.
>>>>>> One
>>>>>>>>>>>> other thing I would suggest is for us to
crowd source
>>>>>> requirements
>>>>>>>> around
>>>>>>>>>>>> model management. Specifically:
>>>>>>>>>>>> 
>>>>>>>>>>>> Model versioning and retention
>>>>>>>>>>>> Model access controls management
>>>>>>>>>>>> Requirements around concept drift
>>>>>>>>>>>> Requirements around model output
>>>>>>>>>>>> Any model audit and logging requirements
>>>>>>>>>>>> What model metrics need to be exposed
>>>>>>>>>>>> Requirements around failure modes
>>>>>>>>>>>> 
>>>>>>>>>>>> 03.07.2016, 14:00, "Casey Stella" <cestella@gmail.com>:
>>>>>>>>>>>>> Hi all,
>>>>>>>>>>>>> 
>>>>>>>>>>>>> I think we are at the point where we
should try to
>> tackle
>>>>> Model
>>>>>>> as a
>>>>>>>>>>>>> service for Metron. As such, I created
a JIRA and
>> proposed
>>>> an
>>>>>>>>>>>> architecture
>>>>>>>>>>>>> for accomplishing this within Metron.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> My inclination is to be data science
language/library
>>>> agnostic
>>>>>> and
>>>>>>>> to
>>>>>>>>>>>>> provide a general purpose REST infrastructure
for
>> managing
>>>> and
>>>>>>>> serving
>>>>>>>>>>>>> models trained on historical data captured
from Metron.
>>> The
>>>>>>>> assumption is
>>>>>>>>>>>>> that we are within the hadoop ecosystem,
so:
>>>>>>>>>>>>> 
>>>>>>>>>>>>>  - Models stored on HDFS
>>>>>>>>>>>>>  - REST Model Services resource-managed
via Yarn
>>>>>>>>>>>>>  - REST Model Services discovered via
Zookeeper.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> I would really appreciate community comment
on the JIRA
>> (
>>>>>>>>>>>>> https://issues.apache.org/jira/browse/METRON-265).
The
>>>>> proposed
>>>>>>>>>>>>> architecture is attached as a document
to that JIRA.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> I look forward to feedback!
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Best,
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Casey
>>>>>>>>>>>> 
>>>>>>>>>>>> -------------------
>>>>>>>>>>>> Thank you,
>>>>>>>>>>>> 
>>>>>>>>>>>> James Sirota
>>>>>>>>>>>> PPMC- Apache Metron (Incubating)
>>>>>>>>>>>> jsirota AT apache DOT org
>>>>>>>>> 
>>>>>>>>> -------------------
>>>>>>>>> Thank you,
>>>>>>>>> 
>>>>>>>>> James Sirota
>>>>>>>>> PPMC- Apache Metron (Incubating)
>>>>>>>>> jsirota AT apache DOT org
>>>>>> 
>>>>>> 
>>>>>> 
>>>>>> --
>>>>>> Thanks,
>>>>>> Andrew
>>>>>> 
>>>>>> Subscribe to my book: Streaming Data <http://manning.com/psaltis>
>>>>>> <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
>>>>>> twiiter: @itmdata <
>>> http://twitter.com/intent/user?screen_name=itmdata>
>>>> 
>>>> 
>>>> 
>>>> --
>>>> Thanks,
>>>> Andrew
>>>> 
>>>> Subscribe to my book: Streaming Data <http://manning.com/psaltis>
>>>> <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
>>>> twiiter: @itmdata <http://twitter.com/intent/user?screen_name=itmdata>
>> 
>> 
>> 
>> --
>> Thanks,
>> Andrew
>> 
>> Subscribe to my book: Streaming Data <http://manning.com/psaltis>
>> <https://www.linkedin.com/pub/andrew-psaltis/1/17b/306>
>> twiiter: @itmdata <http://twitter.com/intent/user?screen_name=itmdata>
>> 
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