>> Announcer: Live from midtown Manhattan, it's theCUBE,
covering BigData New York City 2017,
brought to you by SiliconANGLE Media
and its ecosystem sponsors.
>> Hello everyone, welcome back to theCUBE's
special BigData NYC coverage of theCUBE
here in Manhattan in New York City,
we're in Hell's Kitchen.
I'm John Furrier, with my cohost Jim Kobielus,
whose Wikibon analyst for BigData.
In conjunction with Strata Data going on
right around the corner, this is our annual event
where we break down the big data, the AI, the cloud,
all the goodness of what's going on in big data.
Our next guest is Tendu Yogurtcu
who's the Chief Technology Officer at Syncsort.
Great to see you again, CUBE alumni, been on multiple times.
Always great to have you on, get the perspective,
a CTO perspective and the Syncsort update,
so good to see you.
>> Good seeing you John and Jim.
It's a pleasure being here too.
Again the pulse of big data is in New York,
and it's a great week with a lot of happening.
>> I always borrow the quote from Pat Gelsinger,
who's the CEO of VMware, he said on theCUBE
in I think 2011, before he joined VMware as CEO
he was at EMC.
He said if you're not out in front of that next wave,
you're driftwood.
And the key to being successful is to ride the waves,
and the big waves are coming in now with AI,
certainly big data has been rising tide for its own bubble
but now the aperture of the scale of data's larger,
Syncsort has been riding the wave with us,
we've been having you guys on multiple times.
And it was important to the mainframe in the early days,
but now Syncsort just keeps on
adding more and more capabilities,
and you're riding the wave, the big wave,
the big data wave.
What's the update now with you guys,
where are you guys now in context of today's
emerging data landscape?
>> Absolutely.
As organizations progress with their
modern data architectures and building the next generation
analytics platforms, leveraging machine learning,
leveraging cloud elasticity,
we have observed that data quality and data governance
have become more critical than ever.
Couple of years we have been seeing this trend,
I would like to create a data lake, data as a service,
and enable bigger insights from the data,
and this year, really every enterprise is trying to
have that trusted data set created,
because data lakes are turning into data swamps,
as Dave Vellante refers often
(John laughs)
and collection of this diverse data sets,
whether it's mainframe, whether it's messaging queues,
whether it's relational data warehouse environments
is challenging the customers,
and we can take one simple use case
like Customer 360, which we have been talking
for decades now, right?
Yet still it's a complex problem.
Everybody is trying to get that trusted single view
of their customers so that they can serve the customer
needs in a better way, offer better solutions and products
to customers, get better insights about
the customer behavior, whether leveraging deep learning,
machine learning, et cetera.
However, in order to do that, the data has to be
in a clean, trusted, valid format,
and every business is going global.
You have data sets coming from Asia, from Europe,
from Latin America, and many different places,
in different formats and it's becoming challenge.
We acquired Trillium Software in December 2016,
and our vision was really to bring that world leader
enterprise grade data quality into
the big data environments.
So last week we announced our
Trillium Quality for Big Data product.
This product brings unmatched capabilities
of data validation, cleansing, enrichment,
and matching, fuzzy matching to the data lake.
We are also leveraging our Intelligent eXecution engine
that we developed for data integration product, the MX8.
So we are enabling the organizations to take this
data quality offering, whether it's in
Hadoop, MapReduce or Apache Spark,
whichever computer framework it's going to be in the future.
So we are very excited about that now.
>> Congratulations, you mentioned the data lake
being a swamp, that Dave Vellante referred to.
It's interesting, because how does it become a swamp
if it's a silo, right?
We've seen data silos being antithesis to governance,
it challenges, certainly IoT.
Then you've got the complication of geopolitical borders,
you mentioned that earlier.
So you still got to integrate the data,
you need data quality, which has been around for a while
but now it's more complex.
What specifically about the cleansing and the quality
of the data that's more important now
in the landscape now?
Is it those factors, are that the drivers
of the challenges today and what's the opportunity
for customers, how do they figure this out?
>> Complexity is because of many different factors.
Some of it from being global.
Every business is trying to have global presence,
and the data is originating from web, from mobile,
from many different data sets,
and if we just take a simple address,
these address formats are different in every single country.
Trillium Quality for Big Data, we support over 150
postal data from different countries,
and data enrichment with this data.
So it becomes really complex, because you have to deal with
different types of data from different countries,
and the matching also becomes very difficult,
whether it's John Furrier, J Furrier, John Currier,
you have to be >> All my handles on Twitter,
knowing that's about. (Tendu laughs)
>> All of the handles you have.
Every business is trying to have a better targeting
in terms of offering product and understanding
the single and one and only John Furrier as a customer.
That creates a complexity,
and any data management and data processing challenge,
the variety of data and the speed that data is
really being populated is higher than ever
we have observed.
>> Hold on Jim, I want to get Jim involved
in this one conversation, 'cause I want to just make sure
those guys can get settled in on,
and adjust your microphone there.
Jim, she's bringing up a good point,
I want you to weigh in just to kind of
add to the conversation and take it in the direction of
where the automation's happening.
If you look at what Tendu's saying as to complexity
is going to have an opportunity in software.
Machine learning, root-level cleanliness
can be automated, because Facebook and others have shown
that you can apply machine learning and techniques
to the volume of data.
No human can get at all the nuances.
How is that impacting the data platforms
and some of the tooling out there, in your opinion?
>> Yeah well, much of the issue,
one of the core issues is where do you place
the data matching and data cleansing logic or execution
in this distributed infrastructure.
At the source, in the cloud, at the consumer level
in terms of rolling up the disparate versions of data
into a common view.
So by acquiring a very strong, well-established
reputable brand in data cleansing, Trillium,
as Syncsort has done, a great service to your portfolio,
to your customers.
You know, Trillium is well known for offering
lots of options in terms of where to configure the logic,
where to deploy it within distributed hybrid architectures.
Give us a sense for going forward the range of options
you're going to be providing with for customers
on where to place the cleansing and matching logic.
How you're going to support, Syncsort,
a flexible workflows in terms of curation of the data
and so forth, because the curation cycle for data
is critically important, the stewardship.
So how do you plan to address all of that
going forward in your product portfolio, Tendu?
>> Thank you for asking the question, Jim,
because that's exactly the challenge that we hear
from our customers, especially from larger enterprise
and financial services, banking and insurance.
So our plan is our actually next upcoming release
end of the year, is targeting very flexible deployment.
Flexible deployment in the sense that
you might be creating, when you understand the data
and create the business rules and said
what kind of matching and enrichment
that you'll be performing on the data sets,
you can actually have those business rules
executed in the source of the data or in the data lake
or switch between the source and the enterprise
data lake that you are creating.
That flexibility is what we are targeting,
that's one area.
On the data creation side, we see these percentages,
80% of data stewards' time is spent on data prep,
data creation and data cleansing,
and it is actually really a very high percentage.
From our customers we see this still being a challenge.
One area that we started investing is
using the machine learning to understand the data,
and using that discovery of the data capabilities
we currently have to make recommendations
what those business rules can be,
or what kind of data validation and cleansing
and matching might be required.
So that's an area that we will be investing.
>> Are you contemplating in terms of
incorporating in your product portfolio,
using machine learning to drive a sort of,
the term I like to use is recommendation engine,
that presents recommendations to the data stewards,
human beings, about different data schemas
or different ways of matching the data,
different ways of, the optimal way of reconciling
different versions of customer data.
So is there going to be like a recommendation engine
of that sort >> It's going to be
>> In line with your >> That's what our plan
currently recommendations so the users can opt
to apply or not, or to modify them,
because sometimes when you go too far with automation
you still need some human intervention
in making these decisions because you might be
operating on a sample of data versus the full data set,
and you may actually have to infuse some human
understanding and insight as well.
So our plan is to make as a recommendation
in the first phase at least, that's what we are planning.
And when we look at the portfolio of the products
and our CEO Josh is actually today was also in theCUBE,
part of Splunk .conf.
We have acquisitions happening,
we have organic innovation that's happening,
and we really try to stay focused in terms of
how do we create more value from your data,
and how do we increase the business serviceability,
whether it's with our Ironstream product,
we made an announcement this week,
Ironstream transaction tracing to create more visibility
to application performance and more visibility
to IT operations, for example when you make a payment
with your mobile, you might be having problem
and you want to be able to trace back to the back end,
which is usually a legacy mainframe environment,
or whether you are populating the data lake
and you want to keep the data in sync and fresh
with the data source,
and apply the change as a CDC,
or whether you are making that data from raw data set
to more consumable data by creating the trusted,
high quality data set.
We are very much focused on creating more value
and bigger insights out of the data sets.
>> And Josh'll be on tomorrow, so folks watching,
we're going to get the business perspective.
I have some pointed questions I'm going to ask him,
but I'll take one of the questions I was going to ask him
but I want to get your response
from a technical perspective as CTO.
As Syncsort continues your journey,
you keep on adding more and more things,
it's been quite impressive, you guys done a great job,
>> Tendu: Thank you.
>> We enjoy covering the success there,
watching you guys really evolve.
What is the value proposition for Syncsort today,
technically?
If you go in, talk to a customer,
and prospective new customer, why Syncsort,
what's the enabling value that you're providing
under the hood, technically for customers?
>> We are enabling our customers to access
and integrate data sets in a trusted manner.
So we are ultimately liberating the data
from all of the enterprise data stores,
and making that data consumable in a trusted manner.
And everything we provide in that data management stack,
is about making data available, making data accessible
and integrated the modern data architecture,
bridging the gap between those legacy environments
and the modern data architecture.
And it becomes really a big challenge
because this is a cross-platform play.
It is not a single environment that enterprises
are working with.
Hadoop is real now, right?
Hadoop is in the center of data warehouse architecture,
and whether it's on-premise or in the cloud,
there is also a big trend about the cloud.
>> And certainly batch, they own the batch thing.
>> Yeah, and as part of that, it becomes very important
to be able to leverage the existing data assets
in the enterprise, and that requires an understanding
of the legacy data stores, and existing infrastructure,
and existing data warehouse attributes.
>> John: And you guys say you provide that.
>> We provide that and that's our baby
and provide that in enterprise grade manner.
>> Hold on Jim, one second, just let her finish the thought.
Okay, so given that, okay, cool you got that out there.
What's the problem that you're solving for customers today?
What's the big problem in the enterprise
and in the data world today that you address?
>> I want to have a single view of my data,
and whether that data is originating on the mobile
or that data is originating on the mainframe,
or in the legacy data warehouse,
and we provide that single view in a trusted manner.
>> When you mentioned Ironstream, that reminded me that
one of the core things that we're seeing in Wikibon
in terms of, IT operations is increasingly
being automated through AI, some call it AI ops
and whatnot, we're going deeper on the research there.
Ironstream, by bringing mainframe and transactional data,
like the use case you brought in was IT operations data,
into a data lake alongside machine data
that you might source from the internet of things
and so forth.
Seem to me that that's a great enabler
potentially for Syncsort if it wished to play
your solutions or position them into IT operations
as an enabler, leveraging your machine learning investments
to build more automated anomaly detection
and remediation into your capabilities.
What are your thoughts?
Is that where you're going or do you see it
as an opportunity, AI for IT ops,
for Syncsort going forward?
>> Absolutely.
We target use cases around IT operations
and application performance.
We integrate with Splunk ITSI,
and we also provide this data available
in the big data analytics platforms.
So those are really application performance
and IT operations are the main uses cases we target,
and as part of the advanced analytics platform, for example,
we can correlate that data set with other machine data
that's originating in other platforms in the enterprise.
Nobody's looking at what's happening on mainframe
or what's happening in my Hadoop cluster
or what's happening on my VMware environment, right.
They want to correlate the data that's closed platform,
and that's one of the biggest values we bring,
whether it's on the machine data,
or on the application data.
>> Yeah, that's quite a differentiator for you.
>> Tendu, thanks for coming on theCUBE, great to see you.
Congratulations on your success.
Thanks for sharing.
>> Thank you.
>> Okay, CUBE coverage here in BigData NYC,
exclusive coverage of our event, BigData NYC,
in conjunction with Strata Hadoop right around the corner.
This is our annual event for SiliconANGLE,
and theCUBE and Wikibon.
I'm John Furrier, with Jim Kobielus,
who's our analyst at Wikibon on big data.
Peter Burris has been on theCUBE, he's here as well.
Big three days of wall-to-wall coverage
on what's happening in the data world.
This is theCUBE, thanks for watching,
be right back with more after this short break.