>> Announcer: Live from San Jose,
it's theCube!
Presenting Big Data: Silicon Valley
Brought to you by SiliconANGLE Media
and its ecosystem partners.
(techno music)
>> Welcome back to theCube.
We are live on our first day of coverage
at our event BigDataSV.
I am Lisa Martin with my co-host George Gilbert.
We are at this really cool venue in downtown San Jose.
We invite you to come by today,
tonight for our cocktail party.
It's called Forager Tasting Room and Eatery.
Tasty stuff, really, really good.
We are down the street from the Strata Data Conference,
and we're excited to welcome to theCube
a first-time guest, Kunal Agarwal, the CEO of Unravel Data.
Kunal, welcome to theCube.
>> Thank you so much for having me.
>> So, I'm a marketing girl.
I love the name Unravel Data. (Kunal laughs)
>> Thank you. >> Two year old company.
Tell us a bit about what you guys do
and why that name...
What's the implication there with respect to big data?
>> Yeah, we are a application performance management company.
And big data applications are just very complex.
And the name Unravel is all
about unraveling the mysteries of big data
and understanding why things are not performing well
and not really needing a PhD to do so.
We're simplifying application performance management
for the big data stack.
>> Lisa: Excellent.
>> So, so, um, you know, one of the things that
a lot of people are talking about with Hadoop,
originally it was this cauldron of innovation.
Because we had the "let a thousand flowers bloom"
in terms of all the Apache projects.
But then once we tried to get it into operation,
we discovered there's a...
>> Kunal: There's a lot of problems.
(Kunal laughs) >> There's an overhead,
there's a downside to it.
>> Maybe tell us,
tell us why you both need to know,
you need to know how people have done this
many, many times.
>> Yeah.
>> How you need to learn from experience
and then how you can apply that even in an environment
where someone hasn't been doing it for that long.
>> Right.
So, if I back a little bit.
Big data is powerful, right?
It's giving companies an advantage that they never had,
and data's an asset to all of these different companies.
Now they're running everything from BI, machine learning,
artificial intelligence, IOT, streaming applications
on top of it for various reasons.
Maybe it is to create a new product
to understand the customers better, etc.,
But as you rightly pointed out,
when you start to implement all of these
different applications and jobs, it's very, very hard.
It's because big data is very complex.
With that great power comes a lot of complexity,
and what we started to see is a lot of companies,
while they want to create these applications
and provide that differentiation to their company,
they just don't have enough expertise as well in house
to go and write good applications,
maintain these applications,
and even manage the underlying infrastructure
and cluster that all these applications are running on.
So we took it upon ourselves where we thought,
Hey, if we simplify application performance management
and if we simplify ongoing management challenges,
then these companies would run more big data applications,
they would be able to expand their use cases,
and not really be fearful of,
Hey, we don't know how to go and solve these problems.
Do we actually rely on our system
that is so complex and new?
And that's the gap the Unravel fills, which is
we monitor and manage not only one componenent
of the big data ecosystem, but like you pointed out,
it's a, it's a full zoo of all of these systems.
You have Hadoop, and you have Spark,
and you have Kafka for data injection.
You may have some NoSQL systems
and newer MPP platforms as well.
So the vision of Unravel is really to be that one place
where you can come in and understand what's happening
with your applications and your system overall
and be able to resolve those problems
in an automatic, simple way.
>> So, all right, let's start at the concrete level
of what a developer might get out of
>> Kunal: Right.
>> something that's wrapped in Unravel
and then tell us what the administrator experiences.
>> Kunal: Absolutely.
So if you are a big data developer
you've got in a business requirement that,
Hey, go and make this application
that understands our customers better, right?
They may choose a tool of their liking,
maybe Hive, maybe Spark, maybe Kafka for data injection.
And what they'll do is they'll write an app first in dev,
in their dev environment or the QA environment.
And they'll say, Hey, maybe this application is failing,
or maybe this application is not performing
as fast as I want it to, or even worse
that this application is starting to hog a lot of resources,
which may slow down my other applications.
Now to understand what's causing these kind of problems
today developers really need a PhD to go and decipher them.
They have to look at tons of law rogs, uh, raw logs
metrics, configuration settings
and then try to stitch the story up in their head,
trying to figure out what is the effect, what is the cause?
Maybe it's this problem, maybe it's some other problem.
And then do trial and error to try, you know
to solving that particular issue.
Now what we've seen is big data developers
come in variety of flavors.
You have the hardcore developers who truly understand
Spark and Hadoop and everything,
but then 80% of the people submitting these applications
are data scientist or business analysts,
who may understand SQL, who may know Python,
but don't necessarily know what distributed computing
and parallel processing and all of these things really are,
and where can inefficiencies and problems really lie.
So we give them this one view,
which will connect all of these different data sources
and then tell them in plain English,
this is the problem,
this is why this problem happened,
and this is how you can go and resolve it,
thereby getting them unstuck
and making it very simple for them to go in
and get the performance that they're getting.
>> So, these, these, um,
they're the developers up front
and you're giving them a whole new, sort of,
toolchain or environment to solve the operational issues.
>> Kunal: Right.
>> So that the, if it's DevOps,
its really dev is much more sufficient.
>> Yes, yes, I mean, all companies want to run fast.
They don't want to be slowed down.
If you have a problem today, they'll file a ticket,
it'll go to the operations team,
you wait a couple of days to get some more information back.
That just means your business has slowed down.
If things are simple enough where
the application developers themselves
can resolve a lot of these issues,
that'll get the business unstuck
and get them moving on further.
Now, to the other point which you were asking,
which is what about the operations
and the app support people?
So, Unravel's a great tool for them too
because that helps them see
what's happening holistically in the cluster.
How are other applications behaving with each other?
It's usually a multitenant, multiapplication environment
that these big data jobs are running on.
So, is my apps slowing down George's apps?
Am I stealing resources from your applications?
More so, not just about
an individual application issue itself.
So Unravel will give you visibility into each app,
as well as the overall cluster
to help you understand cluster-wide problems.
>> Love to get at, maybe peel apart
your target audience a little bit.
You talked about DevOps.
But also the business analysts, data scientists,
and we talk about big data.
Data is, has such tremendous power
to fuel a company and, you know, like you said
use it to deliver and, create and deliver new products.
Are you talking with multiple audiences within a company?
Do you start at DevOps and they bring in their peers?
Or do you actually start, maybe,
at the Chief Data Officer level?
What's that kind of entrance
for Unravel? >> So the word I use
to describe this is DataOps, instead of DevOps, right?
So in the older world you had developers,
and you had operations people.
Over here you have a data team and operations people,
and that data team can comprise of the developers,
the data scientists, the business analysts, etc., as well.
But you're right.
Although we first target the operations role
because they have to manage and monitor the system
and make sure everything is running
like a well-oiled machine,
they are now spreading it out to be end-users,
meaning the developers themselves saying,
"Don't come to me for every problem.
"Look at Unravel, try solve it here,
"and if you cannot, then come to me."
This is all, again, improving agility
within the company,
making sure that people have
the necessary tools and insights
to carry on with their day.
>> Sounds like an enabler, >> Yeah, absolutely.
>> That operations would push down to the
DevOp, the developers themselves.
>> And even the managers and the CDOs, for example,
they want to see their ROI that they're getting
from their big data investments.
They want to see, they have put in
these millions of dollars,
have got an infrastructure and these services set up,
but how are we actually moving the needle forward?
Are there any applications that
we're actually putting in business,
and is that driving any business value?
So we will be able to give them a very nice dashboard
helping them understand what kind of throughput
are you getting from your system,
how many applications were you able
to develop last week and onboard
to your production environment?
And what's the rate of innovation
that's really happening inside your company
on those big data ecosystems?
>> It sort of brings up
an interesting question on two prongs.
One is the well-known, but inexact number
about how many big data projects,
>> Kunal: Yeah, yeah. >> I don't know whether
they fail or didn't pay off.
So there's going in and saying,
"Hey, we can help you manage this
"because it was too complicated."
But then there's also the, all the folks
who decided, "Well, we really don't want
"to run it all on-prem.
"We're not going to throw away everything we did there,
"but we're going to also put a lot of new investment
>> Kunal: Exactly, exactly. >> in the cloud.
Now, Wikibon has a term for that,
which true private cloud, which is when you have the
operational processes that you use in the public cloud
and you can apply them on-prem.
>> Right.
>> George: But there's not many products
that help you do that.
How can Unravel work...? >> Kunal: That's a
very good questions, George.
We're seeing the world move more and more
to a cloud environment, or I should say
an on-demand environment where you're not
so bothered about the infrastructure and the services,
but you want Spark as a dial tone.
You want Kafka as a dial tone.
You want a machine-learning platform as a dial tone.
You want to come in there, you want to put in your data,
and you want to just start running it.
Unravel has been designed from the ground up
to monitor and manage any of these environments.
So, Unravel can solve problems
for your applications running on-premise
and similarly all the applications
that are running on cloud.
Now, on the cloud there are other levels of problems as well
so, of course, you'd have applications that are slow,
applications that are failing; we can solve those problems.
But if you look at a cloud environment,
a lot of these now provide you an autoscaling capability,
meaning, Hey, if this app doesn't run
in the amount of time that we were hoping it to run,
let's add extra hardware and run this application.
Well, if you just keep throwing machines at the problem,
it's not going to solve your issue.
Now, it doesn't decrease the time
that it will take linearly with how many servers
that you're actually throwing in there,
so what we can help companies understand is
what is the resource requirement
of a particular application?
How should we be intelligently allocating resources
to make sure that you're able to meet your time SLAs,
your constraints of, here I need
to finish this with x number of minutes,
but at the same time be intelligent
about how much cost you're spending over there.
Do you actually need 500 containers to go and run this app?
Well, you may have needed 200.
How do you know that?
So, Unravel will also help you get efficient
with your run, not just faster,
but also can it be a good multitenant citizen,
can it use limited resources
to actually run this applications as well?
>> So, Kunal, some of the things I'm hearing
from a customer's standpoint that are potential
positive business outcomes are internal:
performance boost. >> Kunal: Yeah.
>> It also sounds like, sort of...
productivity improvements internally.
>> And then also the opportunity
to have the insight to deliver new products,
but even I'm thinking of, you know,
helping make a retailer, for example,
be able to do more targeted marketing, so
>> the business outcomes and the impact
that Unravel can make really seem to have
pretty strong internal and external benefits.
>> Kunal: Yes.
>> Is there a favorite customer story,
(Kunal laughs) don't have to mention names,
that you really think speaks to your capabilities?
>> So, 100%
Improving performance is a very big factor
of what Unravel can do.
Decreasing costs by improving productivity,
by limiting the amount of resources that you're using,
is a very, very big factor.
Now, amongst all of these companies that we work with,
one key factor is improving reliability,
which means, Hey, it's fine that he can
speed up this application,
but sometimes I know the latency
that I expect from an app,
maybe it's a second, maybe it's a minute,
depending on the type of application.
But what businesses cannot tolerate is
this app taking five x amount more time today.
If it's going to finish in a minute,
tell me it'll finish in a minute
and make sure it finishes in a minute.
And this is a big use case for all of the big data vendors
because a lot of the customers are moving
from Teradata, or from Vertica,
or from other relation databases,
on to Hortonworks or Cloudera or Amazon EMR.
Why? Because it's one tenth the amount of cost
for running these workloads.
But, all the customers get frustrated and say,
"I don't mind paying 10 x more money,
"but because over there it used to work.
"Over here, there are just so many complications,
"and I don't have reliability with these applications."
So that's a big, big factor of, you know,
how we actually help these customers get value
out of the Unravel product.
>> Okay, so, um...
A question I'm, sort of...
why aren't there so many other Unravels?
>> Kunal: Yeah. (Kunal laughs)
>> From what I understood from past conversations.
>> Kunal: Yeah.
>> You can only really build the models
that are at the heart of your capabilities
based on tons and tons of telemetry
>> Kunal: Yeah. >> that cloud providers
or, or, sort of, internet scale
service providers have accumulated
in that, because they all have sort of a
well-known set of configurations
and well-known kind of typology.
In other words, there're not a million degrees
of freedom on any particular side
that you can, you have a well-scoped problem,
and you have tons of data.
So it's easier to build the models.
So who, who else could do this?
>> Yeah, so the difference between Unravel
and other monitoring products is
Unravel is not a monitoring product.
It's an intelligent performance management suite.
What that means is we don't just give you graphs
and metrics and say, "Here are all the raw information,
"you go figure it out."
Instead, we have to take it a step further
where we are actually giving people answers.
In order to develop something like that,
you need full stack information; that's number one.
Meaning information from applications
all the way down to infrastructure
and everything in between.
Why? Because problems can lie anywhere.
And if you don't have that full stack info,
you're blind-siding yourself,
or limiting the scope of the problems
that you can actually search for.
Secondly is, like you were rightly pointing out,
how do I create answers from all this raw data?
So you have to think like how an expert
with big data would think,
which is if there is a problem
what are the kinds of checks, balances,
places that that person would look into,
and how would that person establish that
this is indeed the root cause of the problem today?
And then, how would that person
actually resolve this particular problem?
So, we have a big team of scientists, researchers.
In fact, my co-founder is a professor of computer science
at Duke University who has been researching
data-based optimization techniques for the last decade.
We have about 80 plus publications in this area,
Starfish being one of them.
We have a bunch of other publications,
which talk about how do you automate problem discovery,
root cause analysis, as well as resolution,
to get best performance out of these different databases?
And you're right.
A lot of work has gone on the research side,
but a lot of work has gone
in understanding the needs of the customers.
So we worked with some of the biggest companies out there,
which have some of the biggest big data clusters,
to learn from them, what are some everyday,
ongoing management challenges that you face,
and then taking that problem to our datasets
and figuring out, how can we automate problem discovery?
How can we proactively spot a lot of these errors?
I joke around and I tell people that
we're big data for big data.
Right?
All these companies that we serve,
they are gathering all of this data,
and they're trying to find patterns,
and they're trying to find, you know,
some sort of an insight with their data.
Our data is system generated data,
performance data, application data,
and we're doing the exact same thing,
which is figuring out inefficiencies,
problems, cause and effect of things,
to be able to solve it in a more intelligent, smart way.
>> Well, Kunal, thank you so much
for stopping by theCube >> Kunal: Of course.
>> And sharing how Unravel Data is helping
to unravel the complexities of big data.
(Kunal laughs) >> Thank you so much.
Really appreciate it. >> Now you're a Cube almuni.
(Kunal laughs) >> Absolutely.
Thanks so much for having me.
>> Kunal, thanks.
>> Yeah, and we want to thank you
for watching the Cube.
I'm Lisa Martin with George Gilbert.
We are live at our own event BigData SV
in downtown San Jose, California.
Stick around.
George and I will be right back with our next guest.
(quiet crowd noise)
(techno music)