>> Announcer: Live from San Jose, California,
it's The Cube.
Covering Big Data, Silicon Valley, 2017.
(electronic music)
>> Okay, welcome back everyone,
live at Silicon Valley for the big The Cube coverage,
I'm John Furrier, with me Wikibon analyst George Gilbert,
Bruno Aziza, who's on the CMO of AtScale,
Cube alumni,
and Josh Klahr VP at AtScale, welcome to the Cube.
>> Welcome back. >> Thank you.
>> Thanks, Brian.
>> Bruno, great to see you.
You look great, you're smiling as always.
Business is good?
>> Business is great. >> Give us the update
on AtScale, what's up since we last saw you
in New York?
>> Well, thanks for having us, first of all.
And, yeah, business is great, we- I think
Last time I was here on The Cube we talked about the
Hadoop Maturity Survey and at the time
we'd just launched the company.
And, so now you look about a year out
and we've grown about 10x.
We have large enterprises across
just about any vertical you can think of.
You know, financial services,
your American Express, healthcare,
think about ETNA, SIGNA, GSK,
retail, Home Depot, Macy's and so forth.
And, we've also done a lot of work with our
partner Ecosystem, so Mork's- OEM's AtScale
technology which is a great way for us to
get you AtScale across the US, but also
internationally.
And then our customers are getting recognized
for the work that they are doing with AtScale.
So, last year, for instance, Yellowpages
got recognized by Cloudera, on their leadership award.
And Macy's got a leadership award as well.
So, things are going the right trajectory,
and I think we're also benefitting from the fact
that the industry is changing,
it's maturing on the the big data side,
but also there's a right definition of
what business intelligence means.
This idea that you can have analytics
on large-scale data without having to change
your visualization tools and make that work
with existing stock you have in place.
And, I think that's been helping us in growing-
>> How did you guys do it?
I mean, you know, we've talked many times
in there's some secret sauce there,
but, at the time when you guys were first starting
it was kind of crowded field, right?
>> Bruno: Yeah.
>> And all these BI tools were out there,
you had front end BI tools-
>> Bruno: Yep.
But everyone was still separate from
the whole batch back end.
So, what did you guys do to break out?
>> So, there's two key differentiators with AtScale.
The first one is we are the only platform
that does not have a visualization tool.
And, so people think about this as,
that's a bug, that's actually a feature.
Because, most enterprises have already that stuff made
with traditional BI tools.
And so our ability to talk to MDX and SQL
types of BI tools, without any changes
is a big differentiator.
And then the other piece of our technology,
this idea that you can get the speed,
the scale and security on large data sets
without having to move the data.
It's a big differentiation for our
enterprise to get value out of the data.
They already have in Hadoop as well as
non-Hadoop systems, which we cover.
>> Josh, you're the VP of products,
you have the roadmaps,
give us a peek into what's happening
with the current product.
And, where's the work areas?
Where are you guys going?
What's the to-do list, what's the check box,
and what's the innovation coming around the corner?
>> Yeah, I think, to follow up on what Bruno said
about how we hit the sweet spot.
I think- we made a strategic choice, which is
we don't want to be in the business
of trying to be Tableu or Excel or
be a better front end.
And there's so much diversity on the back end
if you look at the ecosystem right now,
whether it's Spark Sequel, or Hive, or Presto,
or even new cloud based systems,
the sweet spot is really how do you
fit into those ecosystems and support
the right level of BI
on top of those applications.
So, what we're looking at,
from a road map perspective is
how do we expand and support
the back end data platforms
that customers are asking about?
I think we saw a big white space
in BI on Hadoop in particular.
And that's- I'd say, we've nailed it
over the past year and a half.
But, we see customers now that are asking us about
Google Big Query.
They're asking us about Athena.
I think these server-less data platforms
are really, really compelling.
They're going to take a while to get adoption.
So, that's a big investment area for us.
And then, in terms of supporting
BI front ends, we're kind of doubling down
on making sure our Tableau integration is great,
Power BI is I think getting really big traction.
>> Well, two great products, you've got
Microsoft and Tableau, leaders in that area.
>> The self-service BI revolution has,
I would say, has won.
And the business user wants their tool of choice.
Where we come in is the folks responsible
for data platforms on the back end,
they want some level of control and consistency
and so they're trying to figure out,
where do you draw the line?
Where do you provide standards?
Where do you provide governance,
and where do you let the business lose?
>> All right, so, Bruno and Josh,
I want you to answer the questions,
be a good quiz.
So, define next generation BI platforms
from a functional standpoint and then
under the hood.
>> Yeah, there's a few things you can look at.
I think if you were at the Gartner BI conference
last week you saw that there was
24 vendors in the magic quadrant
and I think in general people
are now realizing that this is a
space that is extremely crowded
and it's also sitting on technology
that was built 20 years ago.
Now, when you talk to enterprises
like the ones we work with,
like, as I named earlier,
you realize that they all have multiple BI tools.
So, the visualization war, if you will,
kind of has been set up and almost won
by Microsoft and Tableau at this point.
And, the average enterprise is 15
different BI tools.
So, clearly, if you're trying to innovate
on the visualization side, I would say
you're going to have a very hard time.
So, you're dealing with that level of complexity.
And then, at the back end standpoint,
you're now having to deal with
database from the past - that's
the Teradata of this world -
data sources from today - Hadoop - and data sources
from the future, like Google Big Query.
And, so, I think the CIO answer of
what is the next gen BI platform I want
is something that is enabling me to simplify
this very complex world.
I have lots of BI tools, lots of data,
how can I standardize in the middle
in order to provide security, provide scale,
provide speed to my business users
and, you know, that's really radically
going to change the space, I think.
If you're trying to sell a full stack
that's integrated from the bottom
all the way to visualization, I don't think
that's what enterprises want anymore
>> Josh, under the hood, what's the next generation-
you know, key leverage for the tech,
and, just the enabler.
>> Yeah, so, for me the end state
for the next generation GI platform is
a user can log in, they can point to their data,
wherever that data is, it's on Prime,
it's in the cloud, it's in a relational database,
it's a flat file,
they can design their business model.
We spend a lot of time making sure we
can support the creation of business models,
what are the key metrics, what are the hierarchies,
what are the measures,
it may sound like I'm talking about OLAP.
You know, that's what our history is steeped in.
>> Well, faster data is coming, that's- streaming
and data is coming together.
>> So, I should be able to just point
at those data sets and turn around
and be able to analyze it immediately.
On the back end that means we need to have
pretty robust modeling capabilities.
So that you can define those complex metrics,
so you can functionally do what are traditional
business analytics, period over period comparisons,
rolling averages, navigate up and down
business hierarchies.
The optimizations should be built in.
It shouldn't be the responsibility
of the designer to figure out,
do I need to create indeces, do I need
to create aggregates, do I need
to create summarization?
That should all be handled for you automatically.
Shouldn't think about data movement.
And so that's really what we've built in
from an AtScale perspective on the back end.
Point to data, we're smart about creating
optimal data structure so you get
fast performance.
And then, you should be able to connect
whatever BI tool you want.
You should be able to connect Excel,
we can talk the MDX Query language.
We can talk Sequel, we can talk Dax,
whatever language you want to talk.
>> So, take the syntax out of the hands
of the user.
>> Yeah. >> Yeah.
>> And getting in the weeds on that stuff.
Make it easier for them-
>> Exactly.
>> And the key word I think,
for the future of BI is open, right?
We've been buying tools over the last-
>> What do you mean by that, explain.
>> Open means that you can choose
whatever BI tool you want,
and you can choose whatever data you want.
And, as a business user there's
no real compromise.
But, because you're getting an open platform
it doesn't mean that you have to trade off complexity.
I think some of the stuff that Josh
was talking about, period analysis,
the type of multidimensional analysis that you need,
calendar analysis, historical data,
that's still going to be needed,
but you're going to need to provide this in a world
where the business, user,
and IT organization expects that
the tools they buy are going to be open to
the rest of the ecosystem,
and that's new, I think.
>> George, you want to get a question in, edgewise?
Come on.
(group laughs)
>> You know, I've been sort of a single-issue candidate,
I guess, this week on machine learning
and how it's sort of touching
all the different sectors.
And, I'm wondering, are you-
how do you see yourselves as part of a broader pipeline
of different users adding different types of value to data?
>> I think maybe on the machine learning topic
there is a few different ways to look at it.
The first is we do use machine learning
in our own product.
I talked about this concept of
auto-optimization.
One of the things that AtScale does
is it looks at end-user query patterns.
And we look at those query patterns
and try to figure out
how can we be smart about anticipating
the next thing they're going to ask
so we can pre-index, or pre-materialize that data?
So, there's machine learning
in the context of making AtScale
a better product.
>> Reusing things that are already done,
that's been the whole machine-learning-
>> Yes.
>> Demos, we saw Google Next with the
video editing and the video recognition stuff,
that's been-
>> Exactly.
>> Huge part of it.
>> You've got users giving you signals,
take that information and be smart with it.
I think, in terms of the customer work flow -
Comcast, for example, a customer of ours -
we are in a data discovery phase,
there's a data science group
that looks at all of their set top box data,
and they're trying to discover programming
patterns.
Who uses the Yankees' network for example?
And where they use AtScale is what I would call
a descriptive element,
where they're trying to figure out
what are the key measures and trends,
and what are the attributes that contribute to that.
And then they'll go in and they'll use
machine learning tools on top of that same data set
to come up with predictive algorithms.
>> So, just to be clear there,
they're hypotehsizing about, like, say,
either the pattern of users that might be-
have an affinity for a certain channel or channels,
or they're looking for pathways.
>> Yes. And I'd say our role in that right now
is a descriptive role.
We're supporting the descriptive element
of that analytics life cycle.
I think over time our customers
are going to push us to build in more
of our own capabilities, when it comes to,
okay, I discovered something descriptive,
can you come up with a model that helps me predict
it the next time around?
Honestly, right now people want BI.
People want very traditional BI
on the next generation data platform.
>> Just, continuing on that theme,
leaving machine learning aside, I guess,
as I understand it, when we talked about
the old school vendors, Care Data,
when they wanted to support data scientists
they grafted on some machine learning, like
a parallel version of our- in the
core Teradata engine.
They also bought Astro Data, which was,
you know, for a different audience.
So, I guess, my question is,
will we see from you, ultimately,
a separate product line to support
a new class of users?
Or, are you thinking about new functionality
that gets integrated into the core product.
I think it's more of the latter.
So, the way that we view it-
and this is really looking at,
like I said, what people are asking for today
is, kind of, the basic, traditional BI.
What we're building is essentially a business model.
So, when someone uses AtScale, they're designing
and they're telling us, they're asserting,
these are the things I'm interested in measuring,
and these are the attributes that I think
might contribute to it.
And, so that puts us in a pretty good position
to start using, whether it's Spark on the back end,
or built in machine learning algorithms
on the Hadoop cluster,
let's start using our knowledge of that business model
to help make predictions on behalf of the customer.
So, just a follow-up, and this really leaves out
the machine learning part, which is,
it sounds like, we went- in terms of big data
we we first to archive it- supported more
data retension than could do affordably
with the data warehouse.
Then we did the ETL offload,
now we're doing more and more of the visualization,
the ad-hoc stuff.
>> That's exactly right.
So, what-
in a couple years time,
what remains in the classic data warehouse,
and what's in the Hadoop category?
>> Well, so there is, I think what you're describing
is the pure evolution, of, you know,
any technology where you start with the infrastructure,
you know, we've been in this for over ten years,
now, you've got cloud.
They are going APO and then going
into the data science workbench.
>> That's not official yet.
>> I think we read about this,
or at least they filed.
But I think the direction is showing-
now people are relying on the platform,
the Hadoop platform, in order to build applications
on top of it.
And, so, I think, just like Josh is saying,
the mainstream application on top of
the database - and I think this is true
for non-Hadoop systems as well - is always
going to be analytics.
Of course, data science is something
that provides a lot of value,
but it typically provides a lot of value
to a few set of people that will then
scale it out to the rest of their organization.
I think if you now project out to
what does this mean for the CIO
and their environment, I don't think
any of these platforms, Teradata
or Hadoop, or Google, or Amazon
or any of those, I don't think
do 100% replace.
And, I think that's where it becomes interesting,
because you're now having to deal with
a hetergeneous environment, where the business user
is up, they're using Excel, they're using
they're standard net application,
they might be using the result of
machine learning models,
but they're also having to deal with the
heterogeneous environment at the data level.
Hadoop on Prime, Hadoop in the cloud,
non-Hadoop in the cloud and
non-Hadoop on Prime.
And, of course that's a market that I think is
very interesting for us as a
simplification platform for that world.
>> I think you guys are really thinking about it
in a new way, and I think that's kind of
a great, modern approach, let the freedom-
and by the way, quick question on the
Microsoft tool and Tableau, what percentage share
do you think they are of the market? 50?
Because you mentioned those are the two top ones.
>> Are they? >> Yeah, I mentioned them,
because if you look at the magic quadrant,
clearly Microsoft, Power BI and Tableau
have really shot up all the way to the right.
>> Because it's easy to use,
and it's easy to work with data.
>> I think so, I think-
look, from a functionality standpoint,
you see Tableau's done a very good job
on the visualization side.
I think, from a business standpoint,
and a business model execution,
and I can talk from my days at Microsoft,
it's a very great distribution model
to get thousands and thousands of users
to use power BI.
Now, the guys that we didn't talk about
on the last magic quadrant.
People who are like Google Data Studio,
or Amazon Quicksite, and I think that
will change the ecosystem as well.
Which, again, is great news for AtScale.
>> More muscle coming in.
>> That's right. >> For you guys, just more
rising tide floats all boats.
>> That's right.
>> So, you guys are powering it.
>> That's right.
>> Modern BI would be safe to say?
>> That's the idea.
The idea is that the visualization
is basically commoditized at this point.
And what business users want and
what enterprise leaders want is
the ability to provide freedom and openness
to their business users
and never have to compromise
security, speed and also the complexity
of those models, which is what we-
we're in the business of.
>> Get people working, get people productive faster.
>> In whatever tool they want.
>> All right, Bruno. Thanks so much.
Thanks for coming on. AtScale.
Modern BI here in The Cube.
Breaking it down.
This is The Cube covering bid data
SV strata Hadoop.
Back with more coverage after this short break.
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