>> Narrator: Live from San Jose, California
It's the CUBE,
covering Big Data Silicon Valley 2017.
>> Okay welcome back everyone, live in Silicon Valley,
this is the CUBE coverage of Big Data Week,
Big Data Silicon Valley,
our event, in conjunction with Strata Hadoop.
This is the CUBE for two days of wall-to-wall coverage.
I'm John Furrier with Analyst from Wikibon, George Gilbert
our Big Data as well as Peter Buress,
covering all of the angles.
And our next guest is Wei Wang,
Senior Director of Product Market at Hortonworks,
a CUBE alumni, and Oliver Chiu,
Senior Product Marketing Manager
for Big Data and Microsoft Cloud at Azure.
Guys, welcome to the CUBE, good to see you again.
>> Yes.
>> John: On the CUBE, appreciate you coming on.
>> Thank you very much.
>> So Microsoft and Hortonworks,
you guys are no strangers.
We have covered you guys many times on the CUBE,
on HD insights.
You have some stuff happening, here,
and I was just talking about you guys this morning
on another segment, like, saying
hey, you know the distros need a Cloud strategy.
So you have something happening tomorrow.
Blog post going out.
>> Wei: Yep.
>> What's the news with Microsoft?
>> So essentially I think that
we are truly adopting the CloudFirst.
And you know that we have been really acquiring
a lot of customers in the Cloud.
We have that announced in our earnings
that more than a quarter of our customers
actually already have a Cloud strategy.
I want to give out a few statistics
that Gardner told us actually last year.
The increase for their end users went up 57%
just to talk about Hadoop and Microsoft Azure.
So what we're here, is to talk about the next generation.
We're putting our latest and greatest innovation
in which comes in in the package of the release
of HDP2.6, that's our last release.
I think our last conversation was on 2.5.
So 2.6's great latest and newest innovations
to put on CloudFirst, hence our partner, here, Microsoft.
We're going to put it on Microsoft HD Insight.
>> That's super exciting.
And, you know, Oliver, one of the things
that we've been really fascinated with
and covering for multiple years now
is the transformation of Microsoft.
Even prior to Satya, who's a CUBE alumni by the way,
been on the CUBE, when we were at XL event at Stanford.
So, CEO of Microsoft, CUBE alumni, good to have that.
But, it's interesting, right?
I mean, the Open Compute Project.
They donated a boatload of IP into the open-source.
Heavily now open-source, Brendan Burns works for Microsoft.
He's seeing a huge transformation of Microsoft.
You've been working with Hortonworks for a while.
Now, it's kind of coming together,
and one of the things that's interesting
is the trend that's teasing out on the CUBE
all the time now is integration.
He's seeing this flash point where
okay, I've got some Hadoop,
I've got a bunch of other stuff in the enterprise equation
that's kind of coming together.
And you know, things like IOT, and AIs
all around the corner as well.
How are you guys getting this all packaged together?
'Cause this kind of highlights some of the things that are now
integrated in with the tools you have.
Give us an update.
>> Yeah, absolutely.
So for sure, just to kind of respond to the trend,
Microsoft kind of made that transformation
of being CloudFirst,
you know, many years ago.
And, it's been great to partner
with someone like Hortonworks
actually for the last four years of bringing HD Insight
as a first party Microsoft Cloud service.
And because of that, as we're building other Cloud services
around in Azure, we have over 60 services.
Think about that.
That's 60 PAZ and IAZ services in Microsoft,
part of the Azure ecosystem.
All of this is starting to get completely integrated
with all of our other services.
So HD Insight, as an example,
is integrated with all of our relational investments,
our BI investments, our machine learning investments,
our data science investments.
And so, it's really just becoming part of the fabric
of the Azure Cloud.
And so that's a testament to the great partnership
that we're having with Hortonworks.
>> So the inquiry comment from Gardner,
and we're seeing similar things on the Wikibon site
on our research team,
is that now the legitimacy of say,
of seeing how Hadoop fits into the bigger picture,
not just Hadoop being the pure-play Big Data platform
which many people were doing.
But now they're seeing a bigger picture
where I can have Hadoop,
and I can have some other stuff all integrating.
Is that all kind of where this is going
from you guys' perspective?
>> So yeah, it's again, some statistics we have done
tech-validate service that our customers are telling us
that 43% of the responders are actually using
that integrated approach, the hybrid.
They're using the Cloud.
They're using our stuff on-premise to actually provide
integrated end-to-end processing workload.
They are now, I think, people are less think about,
I would think, a couple years ago,
people probably think a little bit
about what kind of data they want to put in the Cloud.
What kind of workload they want to actually execute
in the Cloud, versus their own premise.
I think,
what we see is that line starting to blur a little bit.
And given the partnership we have with Microsoft,
the kind of, the enterprise-ready functionalities,
and we talk about that for a long time
last time I was here.
Talk about security, talk about governance,
talk about just layer of,
integrated layer to manage the entire thing.
Either on-premise, or in the Cloud.
I think those are some of the functionalities
or some of the innovations
that make people a lot more at ease
with the idea of putting
the entire mission-critical applications in the Cloud,
and I want to mention that,
especially with our blog going out tomorrow
that we will actually announce the Spark 2.1.
In which, in Microsoft Azure HD Insight,
we're actually going to guarantee 99.9% of SLA.
Right, so it's, for that, it's for enterprise customers.
In which many of us have together
that is truly an insurance outfield,
that people are not just only feel at ease about their data,
that where they're going to locate,
either in the Cloud or within their data center,
but also the kind of speed and response and reliability.
>> Oliver, I want to queue off something you said
which was interesting,
that you have 60 services,
and that they're increasingly integrated with each other.
The idea that Hadoop itself
is made up of many projects or services
and I think in some amount of time,
we won't look at it as a discrete project or product,
but something that's integrated with together
makes a pipeline, a mix-and-match.
I'm curious if you can share with us a vision
of how you see Hadoop fitting in
with a richer set of Microsoft services,
where it might be SQL server,
it might be streaming analytics,
what that looks like
and so the issue of sort of a mix-and-match toolkit
fades into a more seamless set of services.
>> Yeah, absolutely.
And you're right,
Hadoop and Wei will definitely reiterate this,
is that Hadoop is a platform right,
and certainly there is multiple different workloads
and projects on that platform
that do a lot of different things.
You have Spark that can do machine learning and streaming,
and SQL-like queries,
and you have Hadoop itself that can do badge, interactive,
streaming as well.
So, you see kind of a lot of workloads
being built on open-source Hadoop.
And as you bring it to the Cloud,
it's really for customers that what we found,
and kind of this new Microsoft that is often talked about,
is it's all about choice and flexibility
for our customers.
And so, some customers want to be
100% open-source Apache Hadoop,
and if they want that, HD Insight is the right offering,
and what we can do is we can surround it
with other capabilities that are outside
of maybe core Hadoop-type capabilities.
Like if you want to media services,
all the way down to, you know, other technologies
nothing related to, specifically to data and analytics.
And so they can combine that with the Hadoop offering,
and blend it into a combined offering.
And there are some customers
that will blend open-source Hadoop
with some of our Azure data services as well,
because it offers something unique or different.
But it's really a choice for our customers.
Whatever they're open to,
whatever their kind of their strategy
for their organization.
>> Is there, just to kind of then compare it
with other philosophies,
do you see that notion that Hadoop now becomes
a set of services that might or might not be
mixed and matched with native services.
Is that different from how Amazon or Google,
you know, you perceive them
to be integrating Hadoop into their
sort of pipelines and services?
>> Yeah, it's different because I see Amazon and Google,
like, for instance, Google kind of is starting
to change their philosophy a little bit
with introduction of dataproc.
But before, you can see them as an organization
that was really focused on
bringing some of the internal learnings of Google
into the marketplace with their own,
you can say proprietary-type services
with some of the offerings that they have.
But now, they're kind of realizing the value
that Hadoop, that Apache Hadoop ecosystem brings.
And so, with that comes the introduction
of their own manage service.
And for AWS, their roots is IAZ, so to speak,
is kind of the roots of their Cloud,
and they're starting to bring kind of other systems,
very similar to, I would say Microsoft Strategy.
For us, we are all about making things enterprise-ready.
So that's what the unique differentiator
and kind of what you alluded to.
And so for Microsoft, all of our data services
are backed by 99.9% service-level agreement
including our relationship with Hortonworks.
So that's kind of one,
>> Just say that again, one more time.
>> 99.9% up-time, and if, >> SLA.
>> Oliver: SLA and so that's a guarantee to our customers.
So if anything we're,
>> John: One more time.
>> It's a guarantee to our customers.
>> No, this is important.
SLA, I mean Google Next didn't talk much about last week
their Cloud event.
They talked about speed thieves,
>> Exactly >> Not a lot of SLAs.
This is mandate for the enterprise.
They care more about SLA so,
not that they don't care about price,
but they'd much rather have solid,
bulletproof SLAs than the best price.
'Cause the total cost of ownership.
>> Right.
And that's really the heritage
of where Microsoft comes from,
is we have been serving our on-premises customers
for so long, we understand what they want
and need and require
for a mission-critical enterprise-ready deployment.
And so, our relationship with Hortonworks
absolutely 99.9% service-level agreement
that we will guarantee to our customers
and across all of the Hadoop workloads,
whether it would be Hive, whether it would be Spark,
whether it'd be Kafka,
any of the workloads that we have on HD Insight,
is enterprise-ready by virtue, mission-critical, built-in,
all that stuff that you would expect.
>> Yeah, you guys certainly have a
great track record with enterprise.
No debate about that, 100%.
Um, back to you guys, I want to take a step back
and look at some things we've been observing
kicking off this week at the Strata Hadoop.
This is our eighth year covering,
Hadoop world now has evolved into a whole huge thing
with Big Data SV and Big Data NYC
that we run as well.
The bets that were made.
And so, I've been intrigued by HD Insights from day one.
>> Yep.
>> Especially the relationship with Microsoft.
Got our attention right away,
because of where we saw the dots connecting,
which is kind of where we are now.
That's a good bet.
We're looking at what bets were made
and who's making which bets when,
and how they're panning out,
so I want to just connect the dots.
Bets that you guys have made,
and the bets that you guys have made
that are now paying off,
and certainly we've done before camera revolution analytics.
Obviously, now, looking real good
middle of the fairway as they say.
Bets you guys have made that
hey, that was a good call.
>> Right, and we think that first and foremost,
we are sworn to work to support machine learning,
we don't call it AI,
but we are probably the one that first
to always put the Spark, right, in Hadoop.
I know that Spark has gained a lot of traction,
but I remember that in the early days,
we are the ones that as a distro that,
going out there not only just
verbally talk about support of Spark,
but truly put it in our distribution
as one of the component.
We actually now in the last version,
we are actually allows also flexibility.
You know Spark, how often they change.
Every six weeks they have a new version.
And that's kind of in the sense of running into paradox
of what actually enterprise-ready is.
Within six weeks, they can't even roll out
an entire process, right?
If they have a workload,
they probably can't even get everyone to adopt that yet,
within six weeks.
So what we did, actually, in the last version,
in which we will continue to do,
is to essentially support multiple versions of Spark.
Right, we essentially to talk about that.
And the other bet we have made is about Hive.
We truly made that as kind of an initiative behind
project Stinger initiative, and also have ties now with LAP.
We made the effort to join in
with all the other open-source developers
to go behind this project that make sure
that SQL is becoming truly available
for our customers, right.
Not only just affordable,
but also have the most comprehensive coverage
for SQL, and C20-11.
But also now having that almost
sub-second interactive query.
So I think that's the kind of bet we made.
>> Yeah, I guess the compatibility of SQL,
then you got the performance advantage going on,
and this database is where it's in memory or it's SSD,
That seems to be the action.
>> Wei: Yeah.
>> Oliver, you guys made some good bets.
So, let's go down the list.
>> So let's go down memory lane.
I always kind of want to go back
to our partnership with Hortonworks.
We partnered with Hortonworks really early on,
in the early days of Hortonworks' existence.
And the reason we made that bet
was because of Hortonworks' strategy
of being completely open.
Right, and so that was
a key decision criteria for Microsoft.
That we wanted to partner with someone
whose entire philosophy was open-source,
and committing everything back to the Apache ecosystem.
And so that was a very strategic bet that we made.
>> John: It was bold at the time, too.
>> It was very bold, at the time, yeah.
Because Hortonworks at that time
was a much smaller company than they are today.
But we kind of understood of where the ecosystem was going,
and we wanted to partner with people
who were committing code back into the ecosystem.
So that, I would argue, is definitely
one really big bet that was a very successful one
and continues to play out even today.
Other bets that we've made
and like we've talked about prior
is our acquisition of Revolution Analytics
a couple years ago and that's,
>> R just keeps on rolling,
it keeps on rolling, rolling, rolling.
Awesome.
>> Absolutely. Yeah.
>> Alright, final words.
Why don't we get updated
on the data science experiences you guys have.
Is there any update there?
What's going on, what seems to be,
the data science tools are accelerating fast.
And, in fact, some are saying that looks like
the software tools years and years ago.
A lot more work to do.
So what's happening with the data science experience?
>> Yeah absolutely and just tying back
to that original comment around R, Revolution Analytics.
That has become Microsoft, our server.
And we're offering that, available on-premises
and in the Cloud.
So on-premises, it's completely integrated
with SQL server.
So all SQL server customers will now be able to do
in-database analytics with R built-in-to-the-core database.
And that we see as a major win for us,
and a differentiator in the marketplace.
But in the Cloud,
in conjunction with our partnership with Hortonworks,
we're making Microsoft R server,
available as part of our integration with Azure HD Insights.
So we're kind of just tying back
all that integration that we talked about.
And so that's built in,
and so any customer can take R,
and paralyze that across any number of Hadoop and Sparknotes
in a managed service within minutes.
Clusters will spin up,
and they can just run all their data science models
and train them across any number of Hadoop and Sparknotes.
And so that is,
>> John: That takes the heavy lifting away
on the cluster management side,
so they can focus on their jobs.
>> Oliver: Absolutely.
>> Awesome.
Well guys, thanks for coming on.
We really appreciate Wei Wang with Hortonworks,
and we have Oliver Chiu from Microsoft.
Great to get the update,
and tomorrow 10:30, the CloudFirst news hits.
CloudFirst, Hortonworks with Azure,
great news, congratulations,
good Cloud play for Hortonworks.
To CUBE, I'm John Furrier with George Gilbert.
More coverage live in Silicon Valley after this short break.