>> Live from San Francisco.
It's theCUBE covering DockerCon '18.
Brought to you by Docker and it's ecosystem partners.
>> Welcome back to theCUBE's coverage of DockerCon 2018.
I'm Lisa Martin, in San Francisco, with John Troyer
and we're excited to welcome to theCUBE for the first time,
Ed Hsu, the V.P. of Product and
Product Marketing at Mesosphere. Great to have you on, Ed.
>> Thank you. Pleasure to be here.
>> So, Mesosphere. Tell us about you guys,
what do you do? Why are you at DockerCon?
>> Yeah. So, Mesosphere is a hybrid cloud software platform.
We basically enable you to very easily adopt
all types of new cloud-native technologies
like Docker, Kubernetes, Spark,
all the things that you think about
to build these world changing applications.
We automate that for you to run
on hybrid cloud infrastructures.
>> Nice. So, maybe you could break it down a little bit more.
I know people can sometimes get confused.
Mesosphere the company, Mesosphere the project,
DC/OS the product, and then Kubernetes,
and we're here at DockerCon, so maybe untangle
some of those things a little bit.
>> Sure. Maybe I'll go in chronological order.
So Apache Mesos was actually created
way back, I think around 2010 as a project
to figure out if you had to rebuild
Google's proprietary architecture
for hyper-scale computing, what might that look like?
So that became this project called Apache Mesos.
Later on, at companies like Twitter, Airbnb,
it started being used to solve some real challenges
around scale-ability and performance.
Arguably, without Mesos as a technology,
I don't think Twitter would probably exist today
because Twitter used to crash a lot. You guys remember that.
You got the fail whale picture and all that stuff.
Apparently, Justin Bieber used to crash Twitter, right?
And Mesos became part of the solution.
Now, you fast forward a few years later,
containerization really caught on, right?
And then Docker became a game changer
in terms of making sure people start using
and adopting container technologies,
really popularize containerization,
and of course, Kubernetes later came along
as a way to orchestrate the operations
of these containers. Now, where Mesosphere fits in is
our platform is actually below a container-orchestrator,
right so, Kubernetes is actually the fifth
container-orchestrator to run on Mesos.
There's earlier ones like Netflix,
I think Twitter themselves.
There's different types of container orchestration tools,
and Kubernetes became the most recent
and frankly most popular container-orchestration tool,
and Mesosphere enables customers
to really get one turn-key installation
and operations of that technology.
>> You mentioned Netflix, and I'm thinking,
it powers a lot of our lives.
But thinking about IoT data-driven applications like that,
how does Mesosphere help power IoT
and those data-driven applications?
>> So, any IoT application probably needs
at least three major sets of capabilities.
The first is, you have to ingest tons of data.
If you're a connected car or a home appliance company,
there's a lot of data coming in
from all these internet-connected devices.
You need a way to ingest all that data
without losing any of it and making sure
you can be responsible. You also want
to be able to analyze that data.
So tools like Spark and other things become very important.
You also need to be able to host an application
or service, and Kubernetes becoming
the most popular way to serve these applications.
The last and by far, I think, most important piece
for hybrid-cloud or for, excuse me, for IoT use cases,
is the concept of hybrid-cloud and edge computing.
At Mesosphere, we have many connected car companies
that are doing connected car or self-driving car projects
are actually working with us.
And the reason for this is, we provide consistency
for running containers like with Kubernetes
or data services like Spark and Kafka
on a really elastic infrastructure
that can be on a data center, on AWS, on Google,
or beneath a cell tower or even a cruise ship.
Those are all actual use cases.
We provide a consistent operating model
for operators to just install and run all of this stuff.
>> Super nice. I love in 2018 we're past
some of the press conversation around who's gonna win
or there's only gonna be one way of doing one stack
that's gonna win, and Kubernetes versus whatever,
and that was a conversation a few years ago.
What I love about 2018 is people are in production.
And live and time-to-value are very quick
and very powerful and very deep
and enlightens big data apps. Huge footprint apps as well.
So, can you talk a little bit about some of your customers
and also, in terms of the hybrid cloud.
Are we seeing, are people on Pram?
Are you seeing a lot of multi-cloud uses?
Do apps span on Pram and clouds?
What are some of the use cases and patterns that you see?
>> Yeah. So, I think, maybe I'll start with the one
I find is most interesting which is Royal Caribbean.
If I were to ask you what is the largest
computing cluster in the world by geography,
you probably wouldn't say Royal Caribbean.
So I haven't been on a cruise in a while,
but apparently... I remember back in the day
when I was a child, when I went on a cruise,
you get a daily print-out of today's activities,
and if you wanna go upgrade to a meal plan
or do a tour or scuba diving, you go line up somewhere,
and then you register for it,
and if there's enough inventory, you get to do it.
And so Royal Caribbean is actually trying to move
all of this into a mobile app experience
where based on your preferences,
based on your history, based on what's available,
they'll push certain campaigns
to get you to "John, you really gotta try this scuba diving
because we've got excess inventory,
and we know you have a history
of wanting to do surfing excursion" and so forth.
So what Royal Caribbean has done is create an infrastructure
where they're doing Test and Dev
on campaigns and things like that on AWS.
They actually do a lot of analytics on Pram
in their own data center, and then
when a ship is out at sea, serving those mobile applications
from on Pram cloud-computing environment.
All of this on Mesosphere's DC/OS.
And what this means is that the data
for interacting with passengers
and the campaigns that are available,
the management of the inventory,
all that data, when the ship is in dock,
flies from a data center, through a satellite,
through Kafka into the ship. When the ship goes out to sea,
all the internet connection is used for,
people Skyping with grandma and grandpa
and all that stuff, so the ship can actually,
from an edge computing standpoint,
provide all the resources it needs
for these personalized interaction commission.
>> That's a big example, Royal Caribbean.
It was a very interesting use case,
and I know you mentioned Netflix, Verizon.
I think I saw a Verizon customer video on your website.
When you're talking with companies of either those sizes
or Royal Caribbean that's been around for a long time
versus a cloud-native like Netflix, what are some
of the common data center modernization concerns
that you're hearing consistently across company sizes
and maybe even consistently across industries?
>> Sure. I think that's a great point.
I think some of the early, early adopters,
like Netflix, Twitter, they have their own way
of building out their hyper-scale infrastructures.
And so we work very closely with them
to address their needs. What we're starting to see
as the technology becomes mainstream...
There are a lot of common challenges
that these mainstream enterprises are either
not experienced with, not staffed for,
or just don't have the budget to blow a lot
on these types of projects. And so,
what becomes a key concern is a lot of companies today
recognize containerization is interesting,
it's important. It has the potential
to deliver cost savings, and they recognize
they have to move to a Dev/Ops model
to deliver code very quickly.
But then they also realize that we're starting to live
in an always connected economy
where you can't just sell a product
and not expect to hear from the customer
until they have a problem with it.
You wanna interact with them,
you wanna use that data to help improve
the experience for the customer.
How do you manage all this information?
So the whole concept of data engineering,
data operations, and data science
becomes really a key factor for many enterprises.
And for a lot of them, they just don't have
the resources to really address it.
Now, there are many different companies
that provide individual point solutions
for those technologies, but how do you bring it
all together in a multi-tenet way, right?
How do you make sure if you have one team
that's using one version of Spark
and another team using a different version of Spark
that they can actually share infrastructure?
And that's where Mesosphere's uniqueness
has really come front and center.
We basically pull these data services
the way VMware pulled the traditional model
basic applications. So the cost saving you saw
from server consolidation, we're doing
from cluster consolidation and dramatically reducing costs
while automating operations at the same time.
>> I'd like to follow up on that a little bit.
I think ever since the launch of DC/OS a few years back,
big data was a differentiator for Mesosphere.
And, again, another term that's been through
it's own hype cycle, right? But it's real today.
Can you maybe go a little deeper
with the consolidation piece?
How are Mesosphere admins interacting
with data scientists or even on the container side
and the infrastructure side, what do you have to do
differently to make sure the memory footprints
and all the various big data platforms
are able to be supported?
>> Yeah. So I think big data 1.0, let's call it,
was really a batch operating model.
Wait 'til the data comes in at the end of the quarter,
make some recommendations on how
the business can improve the next quarter.
You guys have all seen reports.
I think Gardner talked about one
where 80% of due projects have failed.
And the reason for this is that it was hard
to justify the benefit right up front.
The cost and the complexity of rolling out
these projects was very prohibitive.
Now, what Mesosphere brings is the ability
to adopt many different types
of these next generation data technologies.
Spark, Cassandra distributed database,
Kafka message queue, TensorFlow, Elasticsearch,
these are all technologies that have become
increasingly popular, but the challenge
for most enterprises is it's hard to have a whole team
just dedicated to learning Kafka
and another one on Spark and another one on Cassandra.
What if your competitors hire them away?
And how do you run all these different technologies
that are clustered systems that require
a lot of infrastructure? They're not designed
to run together and pull together efficiently.
That's what Mesosphere really brings to these technologies.
One is the ability to automate all these technologies,
so instead of getting a whole team
to figure out how to run stuff,
it's literally one click installation,
or a single command on the DC/OS command console.
And then two being able to run all these different types
of data services in a highly pulled way
so that you don't have different clusters
that are turning into snowflakes
that cannot be reused by other teams.
This gives you dramatic changes in how people operate.
If you were a big data team at a major bank
and somebody said "I wanna do transactions
on your infrastructure," you would probably say "No,
stay out of my infrastructure because
I want to make sure I have the resources
to do analytics," and the same would be true
for the people who are actually doing the real-time
transaction-processing with customers.
What if I told you I can give you a way
to do application-aware automations
so that these services can be automated very easily?
And two, these resources can share an infrastructure
while maintaining resource guarantees.
Now, all of a sudden, the individual functional leads
or business unit leads would go "Okay, I'm okay with sharing
resources with these other BU's,
especially if it gives me the benefit over time
of helping different BU's cross-pollinate information."
>> A whole different way of interacting with big data, right?
And actually making it useful.
>> Maybe forcing collaboration.
I wish we had more time, but we wanna thank you so much
for stopping by theCUBE, telling us what's new
at Mesosphere. Sounds like never a dull moment.
>> Oh, absolutely. Thank you very much.
>> We wanna thank you for watching theCUBE.
I am Lisa Martin with John Troyer
from DockerCon 2018. Stick around, John and I
will be right back with our last guest.
(techno music)