>> Announcer: From San Jose, California,
it's The Cube, covering Big Data Silicon Valley 2017.
>> Welcome back everyone.
We are at Big Data Silicon Valley,
running in conjunction with Strata + Hadoop World
in San Jose.
I'm George Gilbert and I'm joined by Raymie Stata,
and Raymie was most recently CEO and Founder
of Altiscale.
Hadoop is a service vendor.
One of the few out there,
not part of one of the public clouds.
And in keeping with all of the great work they've done,
they got snapped up by SAP.
So, Rami, since we haven't seen you, I think on The Cube
since then, why don't you catch us up with all that,
the good work that's gone on between you and SAP
since then.
>> Sure, so the acquisition closed back in September,
so it's been about six months.
And it's been a very busy six months.
You know, there's just a lot of blocking and tackling
that needs to happen.
So, you know, getting people on board.
Getting new laptops, all that good stuff.
But certainly a huge effort for us
was to open up a data center in Europe.
We've long had demand to have that European presence,
both because I think there's a lot of interest
over in Europe itself, but also large, multi-national
companies based in the US, you know, it's important for them
to have that European presence as well.
So, it was a natural thing to do as part of SAP,
so kind of first order of business was to expand
over into Europe.
So that was a big exercise.
We've actually had some good traction on the sales side,
right, so we're getting new customers,
larger customers, more demanding customers,
which has been a good challenge too.
>> So let's pause for a minute on, sort of unpack for folks,
what Altiscale offered, the core services.
>> Sure.
>> That were, you know, here in the US,
and now you've extended to Europe.
>> Right.
So our core platform is kind of Hadoop, Hive, and Spark,
you know, as a service in the cloud.
And so we would offer HDFS and YARN for Hadoop.
Spark and Hive kind of well-integrated.
And we would offer that as a cloud service.
So you would just, you know, get an account,
login, you know, store stuff in HDFS,
run your Spark programs, and the way we encourage people
to think about it is, I think very often vendors
have trained folks in the big data space
to think about nodes.
You know, how many nodes am I going to get?
What kind of nodes am I going to get?
And the way we really force people to think twice
about Hadoop and what Hadoop as a service means is,
you know, they don't, why are you asking that?
You don't need to know about nodes.
Just store stuff, run your jobs.
We worry about nodes.
And that, you know, once people kind of understood,
you know, just how much complexity that takes out of
their lives and how that just enables them to truly focus
on using these technologies to get business value,
rather that operating them.
You know, there's that aha moment in the sales cycle,
where people say yeah, that's what I want.
I want Hadoop as a service.
So that's been our value proposition from the beginning.
And it's remained quite constant, and even coming into SAP
that's not changing, you know, one bit.
>> So, just to be clear then, it's like
a lot of the operational responsibilities sort of,
you took control over, so that when you say,
like don't worry about nodes,
it's customer pours x amount of data into storage,
which in your case would be HDFS,
and then compute is independent of that.
They need, you spin up however many, or however much
capacity they need, with Spark for instance,
to process it, or Hive.
Okay, so.
>> And all on demand.
>> Yeah so it sounds like it's,
how close to like the Big Query or Athena services,
Athena on AWS or Big Query on Google?
Where you're not aware of any servers,
either for storage or for compute?
>> Yeah I think that's a very good comparable.
It's very much like Athena and Big Query where you just
store stuff in tables and you issue queries and you don't
worry about how much compute, you know, and managing it.
I think, by throwing, you know, Spark in the equation,
and YARN more generally, right,
we can handle a broader range of these cases.
So, for example, you don't have to store data in tables,
you can store them into HDFS files which is good
for processing log data, for example.
And with Spark, for example, you have access to a lot
of machine learning algorithms that are a little bit harder
to run in the context of, say, Athena.
So I think it's the same model, in terms of,
it's fully operated for you.
But a broader platform in terms of its capabilities.
>> Okay, so now let's talk about what SAP brought
to the table and how that changed the use cases
that were appropriate for Altiscale.
You know, starting at the data layer.
>> Yeah, so, I think the, certainly the,
from the business perspective, SAP brings a large,
very engaged customer base that, you know,
is eager to embrace, kind of a data-driven mindset
and culture and is looking for a partner
to help them do that, right.
And so that's been great to be in that environment.
SAP has a number of additional technologies
that we've been integrating into the Altiscale offering.
So one of them is Vora, which is kind of an interactive
sequel engine, it also has time series capabilities
and graph capabilities and search capabilities.
So it has a lot of additive capabilities, if you will,
to what we have at Altiscale.
And it also integrates very deeply into HANA itself.
And so we now have that for a technology available
as a service at Altiscale.
>> Let me make sure, so that everyone understands,
and so I understand too, is that so you can issue
queries from HANA and they can, you know,
beyond just simple sequel queries, they can handle
the time series, and predictive analytics,
and access data sort of seamlessly that's in Hadoop,
or can it go the other way as well?
>> It's both ways.
So you can, you know, from HANA you can essentially
federate out into Vora.
And through that access data that's in a Hadoop cluster.
But it's also the other way around.
A lot of times there's an analyst who really lives
in the big data world, right, they're in the Hadoop world,
but they want to join in data that's sitting
in a HANA database, you know.
Might be dimensions in a warehouse or, you know,
customer details even in a transactional system.
And so, you know, that Hadoop-based analyst now has
access to data that's out in those HANA databases.
>> Do you have some Lighthouse accounts that are working
with this already?
>> Yes, we do.
(laughter)
>> Yes we do, okay.
I guess that was the diplomatic way of saying yes.
But no comment.
Alright, so tell us more about SAPs big data stack today
and how that might evolve.
>> Yeah, of course now, especially that now we've got
the Spark, Hadoop, Hive offering that we have.
And then four sitting on top of that.
There's an offering called Predictive Analytics,
which is Spark-based predictive analytics.
>> Is that something that came from you,
or is that,
>> That's an SAP thing, so this is what's been great
about the acquisition is that SAP does have
a lot of technologies that we can now integrate.
And it brings new capabilities to our customer base.
So those three are kind of pretty key.
And then there's something called Data Services
as well, which allows us to move data easily
in and out of, you know, HANA and other data stores.
>> Is it, is this ability to federate queries between
Hadoop and HANA and then migration of the data
between the stores, does that, has that changed
the economics of how much data people, SAP customers,
maintain and sort of what types of apps they can build on it
now that they might, it's economically feasible to
store a lot more data.
>> Well, yes and no.
I think the context of Altiscale,
both before and after the acquisition is very often there's,
what you might call a big data source, right.
It could be your web logs, it could be some IOT generated
log data, it could be social media streams.
You know, this is data that's, you know,
doesn't have a lot of structure coming in.
It's fairly voluminous.
It doesn't, very naturally, go into a sequel database,
and that's kind of the sweet spot for the big data
technologies like Hadoop and Spark.
So, those datas come into your big data environment.
You can transform it, you can do some data quality on it.
And then you can eventually stage it out into something like
HANA data mart, where it, you know, to make it available
for reporting.
But obviously there's stuff that you can do
on the larger dataset in Hadoop as well.
So, in a way, yes, you can now tame, if you will,
those huge data sources that, you know,
weren't practical to put into HANA databasing.
>> If you were to prioritize, in the context of,
sort of, the applications SAP focuses on,
would you be, sort of, with the highest priority use case
be IOT related stuff, where, you know,
it was just prohibitive to put it in HANA
since it's mostly in memory.
But, you know, SAP is exposed to tons of that type of data,
which would seem to most naturally
have an afinity to Altiscale.
>> Yeah, so, I mean, IOT is a big initiative.
And is a great use case for big data.
But, you know, financial-to-financial services industry,
as another example, is fairly down the path using
Hadoop technologies for many different use cases.
And so, that's also an opportunity for us.
>> So, let me pop back up, you know,
before we have to wrap.
With Altiscale as part of the SAP portfolio,
have the two companies sort of gone to customers
with a more, with more transformational options,
that, you know, you'll sell together?
>> Yeah, we have.
In fact, Altiscale actually is no longer
called Altiscale, right?
We're part of a portfolio of products, you know,
known as the SAP Cloud Platform.
So, you know, under the cloud platform
we're the big data services.
The SAP Cloud Platform is all about business transformation.
And business innovation.
And so, we bring to that portfolio the ability to now bring
the types of data sources that I've just discussed,
you know, to bear on these transformative efforts.
And so, you know, we fit into some momentum SAP already has,
right, to help companies drive change.
>> Okay.
So, along those lines, which might be, I mean,
we know the financial services has done a lot of work with,
and I guess telcos as well,
what are some of the other verticals that look like
they're primed to fall, you know, with this type
of transformational network?
>> So you mentioned one, which I kind of call manufacturing,
right, and there tends to be two kind of
different use cases there.
One of them I call kind of the shop floor thing.
Where you're collecting a lot of sensor data,
you know, out of a manufacturing facility
with the goal of increasing yield.
So you've got the shop floor.
And then you've got the, I think, more commonly discussed
measuring stuff out in the field.
You've got a product, you know, out in the field.
Bringing the telemetry back.
Doing things like predictive meetings.
So, I think manufacturing is a big sector
ready to go for big data.
And healthcare is another one.
You know, people pulling together
electronic medical records, you know trying to
combine that with clinical outcomes,
and I think the big focus there is to drive towards,
kind of, outcome-based models, even on the payment side.
And big data is really valuable to drive and assess,
you know, kind of outcomes in an aggregate way.
>> Okay.
We're going to have to leave it on that note.
But we will tune back in at I guess Sapphire
or TechEd, whichever of the SAP shows is coming up next
to get an update.
>> Sapphire's next.
Then TechEd.
>> Okay.
With that, this is George Gilbert, and Raymie Stata.
We will be back in few moments with another segment.
We're here at Big Data Silicon Valley.
Running in conjunction with Strata + Hadoop World.
Stay tuned, we'll be right back.