(upbeat music)
>> Announcer: From Berlin, Germany, it's The Cube,
covering DataWorks Summit Europe 2018.
Brought to you by Hortonworks.
>> Hi, welcome to The Cube, we're separating
the signal from the noise and tuning into the trends
in data and analytics.
Here at DataWorks Summit 2018 in Berlin, Germany.
This is the sixth year, I believe,
that DataWorks has been held in Europe.
Last year I believe it was at Munich, now it's in Berlin.
It's a great show.
The host is Hortonworks and our first interviewee today
is Scott Gnau, who is the chief technology officer
of Hortonworks.
Of course Hortonworks got established themselves
about seven years ago as one of the up and coming
start ups commercializing a then
brand new technology called Hadoop and MapReduce.
They've moved well beyond that in terms of their
go to market strategy,
their product portfolio, their partnerships.
So Scott, this morning, it's great to have ya'.
How are you doing?
>> Glad to be back and good to see you.
It's been awhile.
>> You know, yes, I mean, you're an industry veteran.
We've both been around the block a few times
but I remember you years ago.
You were at Teradata and I was at another analyst firm.
And now you're with Hortonworks.
And Hortonworks is really on a roll.
I know you're not Rob Bearden, so I'm not going to go
into the financials, but your financials look
pretty good, your latest.
You're growing, your deal sizes are growing.
Your customer base is continuing to deepen.
So you guys are on a roll.
So we're here in Europe,
we're here in Berlin in particular.
It's five weeks--you did the keynote this morning,
It's five weeks until GDPR.
The sword of Damacles, the GDPR sword of Damacles.
It's not just affecting European based companies,
but it's affecting North American companies
and others who do business in Europe.
So your keynote this morning, your core theme
was that, if you're in enterprise, your business strategy
is equated with your cloud strategy now,
is really equated with your data strategy.
And you got to a lot of that.
It was a really good discussion.
And where GDPR comes into the picture is the fact that
protecting data, personal data of your customers
is absolutely important, in fact it's imperative
and mandatory, and will be in five weeks
or you'll face a significant penalty if you're not
managing that data and providing customers with the
right to have it erased, or the right to withdraw
consent to have it profiled, and so forth.
So enterprises all over the world, especially in Europe,
are racing as fast as they can to get compliant with GDPR
by the May 25th deadline time.
So, one of the things you discussed this morning,
you had an announcement overnight that Hortonworks
has released a new solution in technical preview
called The Data Steward Studio.
And I'm wondering if you can tie that announcement to GDPR?
It seems like data stewardship would have a strong value
for your customers.
>> Yeah, there's definitely a big tie-in.
GDPR is certainly creating a milestone, kind of a trigger,
for people to really think about their data assets.
But it's certainly even larger than that, because when
you even think about driving digitization of a business,
driving new business models and connecting data
and finding new use cases, it's all about finding the
data you have, understanding what it is,
where it came from, what's the lineage of it,
who had access to it, what did they do to it?
These are all governance kinds of things, which are
also now mandated by laws like GDPR.
And so it's all really coming together in the context
of the new modern data architecture era that we live in,
where a lot of data that we have access to,
we didn't create.
And so it was created outside the firewall
by a device, by some application running with
some customer, and so capturing and interpreting
and governing that data is very different
than taking derivative transactions from an ERP system,
which are already adjudicated and understood,
and governing that kind of a data structure.
And so this is a need that's driven from many
different perspectives, it's driven from the new
architecture, the way IoT devices are connecting
and just creating a data bomb, that's one thing.
It's driven by business use cases, just saying what
are the assets that I have access to,
and how can I try to determine patterns between
those assets where I didn't even create some of them,
so how do I adjudicate that?
>> Discovering and cataloging your data--
>> Discovering it, cataloging it, actually even...
When I even think about data, just think the files
on my laptop, that I created, and I don't remember
what half of them are.
So creating the metadata, creating that trail
of bread crumbs that lets you piece together what's
there, what's the relevance of it, and how, then,
you might use it for some correlation.
And then you get in, obviously, to the regulatory piece
that says sure, if I'm a new customer and I ask to be
forgotten, the only way that you can guarantee
to forget me is to know where all of my data is.
>> If you remember that they are your customer in the
first place and you know where all that data is,
if you're even aware that it exists, that's the first
and foremost thing for an enterprise to be able
to assess their degree of exposure to GDPR.
>> So, right.
It's like a whole new use case.
It's a microcosm of all of these really big things
that are going on.
And so what we've been trying to do is really leverage
our expertise in metadata management using
the Apache Atlas project.
>> Interviewer: You and IBM have done some major work--
>> We work with IBM and the community on Apache Atlas.
You know, metadata tagging is not the most interesting
topic for some people, but in the context that I
just described, it's kind of important.
And so I think one of the areas where we can really
add value for the industry is leveraging our lowest
common denominator, open source, open community kind of
development to really create a standard infrastructure,
a standard open infrastructure for metadata tagging,
into which all of these use cases can now plug.
Whether it's I want to discover data and create metadata
about the data based on patterns that I see in the data,
or I've inherited data and I want to ensure that
the metadata stay with that data through its life cycle,
so that I can guarantee the lineage of the data,
and be compliant with GDPR--
>> And in fact, tomorrow we will have Mandy Chessell
from IBM, a key Hortonworks partner,
discussing the open metadata framework you're describing
and what you're doing.
>> And that was part of this morning's keynote close also.
It all really flowed nicely together.
Anyway, it is really a perfect storm.
So what we've done is we've said, let's leverage
this lowest common denominator, standard metadata
tagging, Apache Atlas, and uplevel it, and not have it
be part of a cluster, but actually have it be a
cloud service that can be in force across
multiple data stores, whether they're in the cloud
or whether they're on prem.
>> Interviewer: That's the Data Steward Studio?
>> Well, Data Plane and Data Steward Studio really enable
those things to come together.
>> So the Data Steward Studio is the second service
>> Like an app.
>> under the Hortonworks DataPlane service.
>> Yeah, so the whole idea is to be able to tie
those things together, and when you think about it
in today's hybrid world, and this is where I really
started, where your data strategy is your cloud strategy,
they can't be separate, because if they're separate,
just think about what would happen.
So I've copied a bunch of data out to the cloud.
All memory of any lineage is gone.
Or I've got to go set up manually another set
of lineage that may not be the same as the lineage
it came with.
And so being able to provide that common service
across footprint, whether it's multiple data centers,
whether it's multiple clouds, or both, is a really
huge value, because now you can sit back and through that
single pane, see all of your data assets and understand
how they interact.
That obviously has the ability then to provide value
like with Data Steward Studio, to discover assets,
maybe to discover assets and discover duplicate assets,
where, hey, I can save some money if I get rid of
this cloud instance, 'cause it's over here already.
Or to be compliant and say yeah, I've got
these assets here, here, and here, I am now
compelled to do whatever: delete, protect, encrypt.
I can now go do that and keep a record through the
metadata that I did it.
>> Yes, in fact that is very much at the heart of
compliance, you got to know what assets there are out there.
And so it seems to me that Hortonworks is increasingly...
the H-word rarely comes up these days.
>> Scott: Not Hortonworks, you're talking about Hadoop.
>> Hadoop rarely comes up these days.
When the industry talks about you guys, it's known
that's your core, that's your base,
that's where HDP and so forth, great product, great distro.
In fact, in your partnership with IBM, a year or more ago,
I think it was IBM standardized on HDP in lieu of
their distro, 'cause it's so well-established, so mature.
But going forward, you guys in many ways,
Hortonworks, you have positioned yourselves now.
Wikibon sees you as being the premier solution provider
of big data governance solutions specifically focused on
multi-cloud, on structured data, and so forth.
So the announcement today of the Data Steward Studio
very much builds on that capability you already have there.
So going forward, can you give us a sense to your
roadmap in terms of building out DataPlane's service?
'Cause this is the second of these services
under the DataPlane umbrella.
Give us a sense for how you'll continue to deepen
your governance portfolio in DataPlane.
>> Really the way to think about it, there are a
couple of things that you touched on that I think
are really critical, certainly for me,
and for us at Hortonworks to continue to repeat,
just to make sure the message got there.
Number one, Hadoop is definitely at the core of
what we've done, and was kind of the secret sauce.
Some very different stuff in the technology, also the
fact that it's open source and community, all those
kinds of things.
But that really created a foundation that allowed
us to build the whole beginning of big data data management.
And we added and expanded to the traditional Hadoop stack
by adding Data in Motion.
And so what we've done is--
>> Interviewer: NiFi, I believe, you made
a major investment.
>> Yeah, so we made a large investment in Apache NiFi,
as well as Storm and Kafka as kind of
a group of technologies.
And the whole idea behind doing that was to expand
our footprint so that we would enable our customers
to manage their data through its entire lifecycle,
from being created at the edge, all the way
through streaming technologies, to landing,
to analytics, and then even analytics being pushed
back out to the edge.
So it's really about having that common management
infrastructure for the lifecycle of all the data,
including Hadoop and many other things.
And then in that, obviously as we discuss whether
it be regulation, whether it be, frankly,
future functionality, there's an opportunity to uplevel
those services from an overall security
and governance perspective.
And just like Hadoop kind of upended traditional thinking...
and what I mean by that was not the economics of it,
specifically, but just the fact that you could land data
without describing it.
That seemed so unimportant at one time, and now it's
like the key thing that drives the difference.
Think about sensors that are sending in data
that reconfigure firmware, and those streams change.
Being able to acquire data and then assess the data
is a big deal.
So the same thing applies, then, to how we apply governance.
I said this morning, traditional governance was
hey, I started this employee, I have access to
this file, this file, this file, and nothing else.
I don't know what else is out there.
I only have access to what my job title describes.
And that's traditional data governance.
In the new world, that doesn't work.
Data scientists need access to all of the data.
Now, that doesn't mean we need to give away PII.
We can encrypt it, we can tokenize it,
but we keep referential integrity.
We keep the integrity of the original structures,
and those who have a need to actually see the PII
can get the token and see the PII.
But it's governance thought inversely as it's been
thought about for 30 years.
>> It's so great you've worked governance into
an increasingly streaming,
real-time in motion data environment.
Scott, this has been great.
It's been great to have you on The Cube.
You're an alum of The Cube.
I think we've had you at least two or three times
over the last few years.
>> It feels like 35.
Nah, it's pretty fun..
>> Yeah, you've been great.
So we are here at Dataworks Summit in Berlin.
(upbeat music)