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>> Narrator: Live, from San Jose, it's theCUBE!
Presenting Big Data Silicon Valley,
brought to you by SiliconANGLE Media
and its ecosystem partners.
>> Welcome back to Big Data SV, everybody.
My name is Dave Vellante and this is theCUBE,
the leader in live tech coverage.
You know, this is our 10th big data event.
When we first started covering big data, back in 2010,
it was Hadoop, and everything was a batch job.
About four or five years ago,
everybody started talking about real time
and the ability to affect outcomes
before you lose the customer.
Lewis Kaneshiro was here.
He's the CEO of Streamlio
and he's joined by Karthik Ramasamy
who's the chief product officer.
They're both co-founders.
Gentlemen, welcome to theCUBE.
My first question is, why did you start this company?
>> Sure, we came together around a vision that
enterprises need to access the value around fast data.
And so as you mentioned, enterprises are moving out
of the slow data era,
and looking for a fast data value to their data,
to really deliver that back to their users
or their use cases.
And so, coming together around that idea of real time action
what we did was we realized that enterprises can't all
access this data with projects right now that are
not meant to work together, that are very difficult,
perhaps, to stitch together.
So what we did was create an intelligent platform
for fast data that's really accessible
to enterprises of all sizes.
What we do is we unify the core components
to access fast data, which is messaging,
compute and stream storage,
accessing the best of breed open-source technology
that's really open-source out of Twitter and Yahoo!
>> It's a good thing I was going to ask
why does the world need to know there are,
you know, streaming platforms,
but Lewis kind of touched on it, 'cause it's too hard.
It's too complicated,
so you guys are trying to simplify all that.
>> Yep, the reason mainly we wanted to simplify it
because, based on all our experiences at Twitter and Yahoo!
one of the key aspects was to to simplify it
so that it's conceivable by regular enterprise
because Twitter and Yahoo! kind of our position
can afford the talent and the expertise in order to do this
real time platforms.
But when it goes to normal enterprises,
they don't have access to the expertise
and the cost benefits that they might have to reincur.
So, because of that we wanted to
use these open-source projects,
the Twitter and the Yahoo!'s provider,
combine them, and make sure that you have a simple, easy,
drag and drop kind of interface,
so that it's easily conceivable for any enterprise.
Essentially, what we are trying to do is
reduce the (mumbles) for enterprises for real time,
for all enterprises.
>> Dave: Yeah, enterprises will pay up...
>> Yes. >> For a solution.
The companies that you used to work for,
they all gladly throw engineering at the problem.
>> Yeah. >> Sure.
>> To save time, but most organizations,
they don't have the resources and so.
Okay, so how does it, would it work prior to Streamlio?
Maybe take us through
sort of how a company would attack this problem,
the complexities of what they have to deal with,
and what life is like with you guys.
>> So, current state of the world is
it's fragmented solution, today.
So the state of the world is where
you take multiple pieces of different projects
and you'd assemble them together in formats
so that you can do (mumbles) right?
So the reason why people end up doing is
each of these big data projects that people use
was the same for completely different purpose.
Like messaging is one, and compute is another one,
and third one is storage one.
So, essentially what we have done as company is
to simplify this aspect by integrating this well-known,
best-of-the-breed projects called,
for messaging we use something called Apache Poser,
for compute we use something called Apache Krem,
from Twitter, and similarly for storage,
for real time storage, we use something called
Apache Bookkeeper, so and to unify them,
so that, under the hoods, it may be three systems,
but, as a user, when you are using it,
it serves or functions as a single system.
So you install the system, and ingest your data,
express your computation, and get the results out,
in one single system.
>> So you've unified or converged these functions.
If I understand it correctly,
we talking off camera a little bit,
the team, Lewis, that you've assembled
actually developed a lot of these,
or hugely committed to these open-source projects, right?
>> Absolutely, co-creators of each of the projects
and what that allows us to do is to really
integrate, at a deep level, each project.
For example, Pulsar is actually a pub/sub system
that is built on Bookkeeper,
and Bookkeeper, in our minds, is
a pure list best-of-breed stream storage solution.
So, fast and durable storage.
That storage is also used in Apache Heron to store State.
So, as you can see, enterprises,
rather than stitching together multiple different solutions
for queuing, streaming, compute, and storage,
now have one option that they can install
in a very small cluster,
and operationally it's very simple to scale up.
We simply add nodes if you get data spikes.
And what this allows is enterprises
to access new and exciting use cases
that really weren't possible before.
For example, machine learning model deployment to real time.
So I'm a data scientist and what I found is
in data science, you spend a lot of time
training models in batch mode.
It's a legacy type of approach,
but once the model is trained,
you want to put that model into production in real time
so that you can deliver that value
back to a user in real time.
Let's call it under two second SLA.
So, that has been a great use case for Streamlio
because we are a ready made intelligent platform
for fast data, for MLai deployment.
>> And the use cases are typically stateful
and your persisting data, is that right?
>> Yes, use cases, it can be used for
stateless use cases also, but the key advantage
that we bring to a table is stateful storage.
And since we ship along with the storage
(mumbles) stateful storage becomes much easier
because of the fact that it can be used to store
a real intermediate state of the computation
or it can be used for the staging (mumbles) data
when it spills over from what the memory is
it's automatically stored to disk
or you can even in the data for as long as you want
so that you can unlock the value later
after the data has been processed for the fast data.
You can access the lazy data later, in time.
>> So give us the run-down on the company, funding,
you know, VCs, head count.
Give us the basics.
>> Sure, we raise Series A from Lightspeed Venture Partners,
lead by John Vrionis and Sudip Chakrabarti.
We've raised seven and a half million
and emerged from stealth back in August.
That allowed us to ramp up our team to
17, now, mainly engineers,
in order to really have a very solid product,
but we launched post rev, prelaunch
and some of our customers are really looking
at geo replication across multiple data centers
and so active, active geo replication
is an open-source feature in Apache Pulsar,
and that's been a huge draw,
compared to some other solutions that are out there.
As you can see, this theme of simplifying architecture
is where Streamlio sits, so unifying, queuing and streaming
allows us to replace a number of different legacy systems.
So that's been one avenue to help growth.
The other, obviously is on the compute piece.
As enterprises are finding new and exciting use cases
to deliver back to their users,
the compute piece needs to scale up and down.
We also announce Pulsar Functions,
which is stream-native compute
that allows very simple function computation
in native Python and Java,
so you spin out the Apache Python cluster
or Streamlio platform, and you simply
have compute functionality.
That allows us to access edge use cases,
so IOT is a huge, kind of exciting POC's for us right now
where we have connected car examples
that don't need heavyweight schedule
or deployment at the edge.
It's Pulsar Pulsar functions.
What that allows us to do are things like fraud detection,
anomaly detection at the edge, model deployment at the edge,
interpolation, observability, and alerts.
>> And, so how do you charge for this?
Is it usage based. >> Sure.
What we found is enterprise are more comfortable
on a per node basis, simply because we
have the ambition to really scale up and help enterprises
really use Streamlio as their fast data platform
across the entire enterprise.
We found that having a per data charge rate
actually would limit that growth,
and so per node and shared architecture.
So, we took an early investment
in optimizing around Kubernetes.
And so, as enterprises are adopting Kubernetes,
we are the most simple installation on Kubernetes,
so on-prem, multicloud, at the edge.
>> I love it, so I mean for years we've just
been talking about the complexity headwinds
in this big data space.
We certainly saw that with Hadoop.
You know, Spark was designed to certainly
solve some of those problems, but.
Sounds like you're doing some really good work
to take that further.
Lewis and Karthik, thank you so much for coming on theCUBE.
I really appreciate it.
>> Thanks for having us, Dave.
>> All right, thank you for watching.
We're here at Big Data SV, live from San Jose.
We'll be right back.
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