>> Live from San Francisco, it's theCUBE.
Covering Google Cloud Next 19.
Brought to you by Google Cloud and its eco system partners.
>> Hello everyone welcome back here
to theCUBE live coverage here in San Francisco, California.
We're in the Moscone Center on the ground floor here.
Day three of three days of coverage
for Google Cloud Next 2019.
I'm John Furrier, my co-host, Dave Vellante,
Stew Miniman out there getting stories out there
He's also been hosting.
Dave, great to see you!
Evren, Director of Product Management at Google Cloud,
doing all the data streaming the data.
We're streaming data right now.
>> Absolutely, this is it. This is it.
>> So let's stream some data.
So streaming data has certainly been around for awhile.
Dave and I when we first started theCUBE ten years ago,
it was part of Silk and Angle Media
hadoop was just a small little project.
That really kind of was the catalyst moment
for around big data that's now evolved to it's own position.
Now you have streaming data, you have cloud scale,
the Cloud has really changed the game on big data.
Changed the nature and dynamics of it
and one of the things is streaming data,
streaming analytics as a core value proposition
for enterprises, and this is fairly new.
>> Very true.
>> What's your take on it and how does it relate
to what's going on with Google Cloud?
>> I am glad we're talking about that.
This is an exciting time for us.
Streaming like you said is growing.
Batch is not going away, but streaming
is actually overtaking a lot of
the applications that we're seeing.
Today we're seeing more streaming applications
taking place than batch.
One of the things that we're seeing
is everybody is gathering data from all over the place
from your websites, from your mobile phones,
from your IoT devices, just like we're doing right now.
There's data coming in and people want
to make decisions real time whether it's in
the banking industry, in your healthcare, retail,
it doesn't matter which word cycle you're working with
and we're seeing how those messages
how those events are coming in
and where the decisions are being made
real time, milliseconds we're talking about.
>> Why is it happening, what's the real catalyst here?
Just tsunami of data, nature of the value,
all of the above, what's the?
>> We believe one of the things is
like you mentioned Cloud really changed the game.
Where people actually can reach
globally data and messages at scale.
We're talking about billions of messages coming in
and processing capacity is available
now we can actually process it and make
a decision within milliseconds and get to the results.
To me, that was the biggest catalyst.
And we're seeing many of us have grown up
using batch data, making decisions
now everybody is talking about M.L. and A.I.
You need that data coming in real time
and we can actual process it and make the decision.
To me, that's the catalyst.
>> First of all we love streaming data, this topic.
One we believe streaming where shooting video
but data, real time, has been one
of the keys you see self driving cars
monging of data, mixing and matching of data
to get better signal and better machine learning
and I got to ask you, because batch is certainly
the role for batch is kind of old school
it's some old techniques it's been around for awhile,
>> It's not going to go away though.
>> It's not going to go away it's established it's place
but the knee jerk reaction of existing old school
people who haven't migrated to the new modern version
they go to the batch kind of mind set.
I want to get you're reaction.
Data lakes, there's nothing flowing in a lake.
Okay, so there is a role for a data lake
streaming gives me the impression of like an ocean
or a river or something moving fast.
Talk about the differences because
it's not just the data lake okay that's
a batch kind of reaction.
>> It is a complementary.
Actually it's not going away because
all of that data that we had in the back
is something we're relying on
to really augment and see what's changing.
So if you're in a retail house
you're buying something, you're going to
make a decision and your support is actually behind it.
OK here's Evren, he's actually shopping around this
and he wants this for his son.
That's what the models built around it
is looking at what is my behavior
and in the moment making a decision for me.
So that's not going away.
The other thing is batch users
are able to take advantage of the technology today.
If you look at our data flow,
same set of codes, same set of capability
can be used by the same folks that are used to batch.
You don't have to change anything so that actually
we help folks to be up skilled using the same set
of tools and become much more experienced
and experts in the streaming too.
That's not going away we help both of the worlds.
>> So, complementary.
>> Very complementary.
>> So data lakes are good for kind of setting the table
if you have to store it somewhere
but that's not the end game though.
>> No.
>> Okay.
>> I wonder if we could talk about
the evolution from batch to real time streaming.
And my favorite example, because I think
people can relate to it, is fraud detection.
Ten years ago, it was up to the user to go
through his or her bill, right?
And then you started to get inundated
with false positives, and now lately,
last couple of years it's getting better and better.
Fewer false positives, usually when you
usually no news is good news.
News is usually bad news now, so take that example
and use that to describe how things have evolved.
>> I am a student of A.I. I did my Master's
and PhD in that and I went through that change
in my career because we had to collect the data,
batch it and analyze it, and actually
make a decision about it and we had a lot of false positives
and in some cases some negative misses too
which you don't want that either.
And what happened is our modeling
capabilities became much better.
With this rich data, and you actually tap into
that data lake, you can go in there
the data is there, and this is spread data
we can pull in data from different sources
and actually remove the outliers
and make our decision real time right there.
We didn't have the processing capability
we didn't have a place like PostUp
where globic can scan and bring in data
at hundreds of gigabytes of data.
That's messaging you want to deal with
at scale no matter where it is
and process that, that wasn't available for us.
Now it's available it's like a candy shelf
for technologists, all the technology
is in our hands and we want to hold these things.
>> You were talking about I think
the simplicity of, I'm able to use
my batch processes and apply them.
One of the complaints I hear from developers sometimes
is that the data pipeline is getting so complicated.
You were talking about you're grabbing stuff from
websites, from financial databases, and so
depending on what data store you're using
and what streaming tools you're using
or other A.I. tools, the pipeline gets very
complicated the A.P.Is start to get complicated
but I'm hearing a story of simplicity.
Can you elaborate on that and add some color?
>> Yeah I'm glad you're asking that question
you may have heard, yesterday we announced
a whole bunch of new things and ease of use
is the top of the line for us.
Really are trying to make it easy.
If you look at this eco pipeline
we're building with data flow,
it helps you end to end.
Data engineered no matter which angle their coming in
should be able to use their known skill sets
and be able to build their pipelines end to end
so that you can achieve your goals around streaming.
We aren't really having to go through a lot
of the clusters of the pipelines
we are going to continue to push that ease of use
over and over, we're not going to let it go
because make it easier, everyone will adapt it faster.
>> You mentioned you got a PhD in A.I.,
Master's in A.I., A.I. has been around for awhile.
A lot of people have been saying that
but machine learning certainly has changed the game.
Machine learning plus cloud has been a real accelerant
in the academic and now commercial aspects of A.I.
So I want to get your thoughts on
the notion of scale which you talk about,
plus the diversity of data.
So if you can bring in data at scale
get more signaling points more access to data signaling
the diversity of data becomes very key.
But cleanliness, data cleaning, used to be an old
practice of you get a bunch of data,
stack it up, put it in a pile corpus,
and you kind of go clean it.
With streaming, if it's always flowing
there's kind of a behavioral characteristic
of data cleanliness, data monitoring,
talk about that diversity of data clean data
and how that feeds machine learning and makes better A.I.
>> Good one, so that's where we actually are able to,
if you look at PostUp, you're building joint
your table set of datas with streaming set of datas
you can actually put it into data filter it
and make those analyses.
And within both, we provide enough of a window
for you to be able to go back,
hey are there things that I should be looking at,
up to seven days we can provide a snapshot
because you will always find something
you can go back, you know what I'm going
to remove this outlier.
All worrying about all the processing we do
before we bring in the data so there's
a lot of cleanliness that takes place
but we have the built in tools
we have the built in capabilities
for everyone to get going.
It's ready to scale for you from the moment you open it up.
That's the beauty of it, that's the beauty
of when you start from PostUp to data flow to streaming
engine it's ready for you to run.
>> Talk about what's changed though
when people hear diversity of data
they get scared, oh my god I work, heavy lifting.
Now it's a benefit.
What's easier now to deal with all of these
diverse data sets, what's the easy revolution?
>> So do you remember the big V's of big data right?
Volume, velocity, variety.
People were scared about the variety.
Now I can actually bring in my data from different places.
Again, let's go back to the shopping example.
Where I shop, what I shop for,
that actually defines my behavior around it.
Those data sit somewhere else.
We bring those in to make a decision about
okay everyone wants to go buy a scooter or whatever else,
that's the diversity of the data.
We're now able to deal to with this at scale.
That was not available we could actually bring in
and render this, now everything is going
to do this much more sequential.
We're now able to bring all of them together
process it at the same time and make the decision.
>> What's the key products that will make
all of those happen, take us through
the portfolio if I want that would you just said
which is a great value.
It sounds like not a heavy lift
all I have to do is point the data sources
into this engine, what are the products
that make up that capability?
>> So if I look at the overall portfolio
on Google Cloud from our data analysts point of view,
so you actually can bring in your data through PostUp,
lots of messaging capability globally
and you can actually do it regionally
because we have a lot of regional requirements
coming from various countries and data flow
is where we actually transfer the data.
That's where you do the processing.
And you use all of these advance analytics capabilities
through your streaming engine that we released
and you have your B query, you have your OMLs,
you have all kinds of things that you can bring in
you're big tables and what have you.
That's all easily integrated end to end
for any analyst to be able to use.
>> What is beam?
>> Beam ah that's great I'm so glad
you asked that question I almost forgot!
Beam is one of our open sources
we donated the same set, just like we did
with Koppernes few years ago,
we donated to the open source it's growing.
This year actually it won The Technology Awards.
So the source is open the community really took
it upon, they use that toolkit to build their pipelines
you can use any kind of a code that you want
Java, Gold, whatever you want to do it and they contribute.
We use it internally and externally.
It's one of those things that's going to grow.
We have a lot of community events coming up this year.
We might, and I've seen the increase,
I'm really really proud of that community.
>> Evren, I love the A.I. can't get my mind off
your background and academic because I studied A.I. as well
in the 80s and 90s all that good stuff.
Young kids are flocking to computer science now
because A.I. is very sexy, it's very intoxicating
and it's so easy to deal with now.
You guys had a hack-a-thon here with NCAA using data
really kind of real time and kind of
cool things are happening.
So it's a moment now for A.I. this is the moment.
What's your advice, you've been through the wars
you've done your chore duty all those years
now it's actually happening.
What's your advice for young people
who want to come in, get their hands dirty,
build things, use A.I., what's your advice,
how they should tackle that?
>> I am living it, both of my sons one
is finishing junior high, the other one
is a senior in high school, their both in it.
So when I hear my young kids come and say,
"hey bubba we just built this using transfer flow."
Like it is making me really proud.
At the middle school level they were doing it.
So the good news is we have all of this
publicly available data for them.
I encourage every one of them.
If you look at what we provide from Google Cloud,
you come in there, we have the data for them,
we have the tools for them, it's all ready
for them to play so schools get free access to it too.
>> It's a major culture but how do they get
someone who's interested but never coded before,
how do they jump right in and get ingratiated
and immersed into the code, what do they do?
>> We have some community reaches
that we're actually doing as Google.
We go out to them and we're actually
establishing centers to really build community events
for them to really learn some new skills.
And we're making this easy for them.
And I'm happy to hear more and do it,
but I'm an advocate I go to middle schools,
I go to high schools, I go to colleges.
Colleges are a different story.
We provide school classes and we provide our technologies
at the universities because enterprises need that talent,
need that skill, when they graduate,
their going to hire them just like
I'm going to hire them into my organization.
>> So my number one complaint my kids have about school,
they're talking about kids that, oh school's going
to be a waste it's so linear I can learn everything
on YouTube and Google.com.
All the stuff I learned in school
I'm never going to use in the real world.
So the question is, what skill should kids learn
that could be applied to machine learning,
thinking, the kind of constructs, data structures,
or methodologies, what are some of the skills
and classes that can tease out and be natural lead into
computer science and machine learning A.I.?
>> You know, actually their going to build up the skills.
The languages will evolve and so forth.
As long as they have that inner curiosity
asking new questions, how can I find
the answer a little faster, that will push them towards
different sets of tools, different sets of areas.
If you go to Berkeley in here, you will see
a whole bunch of high school kids
working side by side with graduate students
asking those questions, developing those skill sets,
but it's all coming down to their curiosity.
>> And I think that applies for business too.
I mean there's a big gap between the A.I. haves
and have-nots I always say.
And the good news here that my take away is,
you're going to buy A.I, you're going to buy it
from people like Google and you're going to
build it and apply it, you're going
to spend time applying it,
and that's how these incumbents can close the gap
and that's the good news here.
>> Very true if you look at it,
look at all the A.P.Is that we have.
From text recognition to image recognition
to whatever it is, those are all built models
and I've seen some customers build some
fantastic applications starting from there
and they use their own data, bring it in,
they update their model for their own businesses cases.
>> It's composition it's composing.
It's not coding it's composing.
>> Exactly, it's composing.
We are taking it to the next level.
That abstraction is going to actually help others
come into the field because they know their field
of expertise, they can ask direct questions.
You and I may not know it but,
they will ask direct questions.
And they will go with the tools available for them
for the curiosity that they reach.
>> Okay what's the coolest thing you're working on right now?
>> Coolest thing, I just y'know streaming is my baby.
We are working on, I want to solve all
the streaming challenges, whatever the industry is.
I really want to welcome everyone, bring you to us.
I think, if I look at it, one of the things
we discussed today was Antos was fantastic right?
I mean we're really going to change the game
for all enterprises to be able to provide
those capabilities at the infrastructure.
But imagine what we can do with all the data analytics
capabilities we have on top of it.
I think this is the next five years
is going to be fantastic for us.
>> What's the coolest use case thing
you see emerging out of streaming?
>> Ah you know, yesterday I actually had
one of my clients with me onstage, AB Tasty.
They had a fantastic capability that they built.
They tried everything.
And we were not their first choice, I'll be very open.
They said the same thing to everybody,
you guys were not our first choice.
They went around, they looked at
all the tool kits, everything.
They came they used PostUp, they used data flow,
they used engine, streaming engine.
And they AB testing for marketing.
And they do that at scale, billions of messages
every minute, and they do it within seconds,
milliseconds, 32 milliseconds at most.
Because they have to make the decision.
That was awesome, go check.
I don't know if you're familiar with that.
One of our customers, they provide these
real time delivery.
In India, imagine where things are.
In global leaders, you can actually
ask for a food to be delivered
and they have to optimize, depending on what
the traffic is and go with their scooters,
and provide you this delivery.
They aren't doing it as well.
Okato, they believe, provide food
in UK 70% of the population use our technologies
for real time delivery.
Those are some great examples.
>> Evren, great insight, great to have you on.
Just a final word here, next couple years,
how do you see the trajectory of machine learning A.I.
Analytics feeding into the value of making life easier
society better, and businesses more productive?
>> We are seeing really good pull from enterprises
from every archival that you can think of.
Regulated, retail, what have you.
And we're going to solve some really hard problems
whether it's in health care industry,
financial industry, retail industry,
we're going to make lives of people much easier.
And their going to benefit from it at scale.
And I believe we're just scratching
the tip of it and you're seeing this energy in here.
Year over year this has gotten better and better.
I can't wait to see what's going to happen next year.
>> Evren Eryurek great energy, expert at A.Is,
streaming analytics, again this is early days
of a brand new shift that's happening.
You get on the right side of history
it's A.I. machine learning, streaming analysts.
Thanks for coming, I appreciate it.
>> Thank you so much, take care guys.
>> More live coverage here in theCUBE
in San Francisco at Google next Cloud 2019.
We'll be back after this short break.