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>> [Announcer] Live from Boston, Massachusetts,
this is the Cube, covering Sparks Summit East 2017.
Brought to you by Databricks.
Now, here are your hosts, Dave Vellante and George Gilbert.
>> Welcome back to Sparks Summit in Boston, everybody.
This is the Cube,
the worldwide leader in live tech coverage.
We've been here two days,
wall-to-wall coverage of Sparks Summit.
George Gilbert, my cohost this week,
and I are going to review part two
of the Wikibon Big Data Forecast.
Now, it's very preliminary.
We're only going to show you a small subset
of what we're doing here.
And so, well, let me just set it up.
So, these are preliminary estimates,
and we're going to look at different ways
to triangulate the market.
So, at Wikibon, what we try to do
is focus on disruptive markets,
and try to forecast those over the long term.
What we try to do is identify where the traditional market
research estimates really, we feel,
might be missing some of the big trends.
So, we're trying to figure out, what's the impact,
for example, of real time.
And, what's the impact of this new workload that we've been
talking about around continuous streaming.
So, we're beginning to put together
ways to triangulate that, and we're going to show you,
give you a glimpse today of what we're doing.
So, if you bring up the first slide,
we showed this yesterday in part one.
This is our last year's big data forecast.
And, what we're going to do today,
is we're going to focus in on that line, that S-curve.
That really represents the real time
component of the market.
The Spark would be in there.
The Streaming analytics would be in there.
Add some color to that, George, if you would.
>> [George] Okay, for 60 years,
since the dawn of computing, we have two ways
of interacting with computers.
You put your punch cards in, or whatever else
and you come back and you get your answer later.
That's batch.
Then, starting in the early 60's, we had interactive,
where you're at a terminal.
And then, the big revolution in the 80's
was you had a PC, but you still were either interactive
either with terminal or batch,
typically for reporting and things like that.
What's happening is the rise of a new interaction mode.
Which is continuous processing.
Streaming is one way of looking at it
but it might be more effective
to call it continuous processing
because you're not going to get rid of batch or interactive
but your apps are going to have a little of each.
So, what we're trying to do, since this is early, early
in its life cycle, we're going to try and look at that
streaming component from a couple of different angles.
>> Okay, as I say, that's represented by this Ogive curve,
or the S-curve.
On the next slide, we're at the beginning
when you think about these continuous workloads.
We're at the early part of that S-curve, and of course,
most of you or many of you know how the S-curve works.
It's slow, slow, slow.
For a lot of effort, you don't get much in return.
Then you hit the steep part of that S-curve.
And that's really when things start to take off.
So, the challenge is, things are complex right now.
That's really what this slide shows.
And Spark is designed, really,
to reduce some of that complexity.
We've heard a lot about that, but take us through this.
Look at this data flow from ingest, to explore,
to process, to serve.
We talked a lot about that yesterday,
but this underscores the complexity in the marketplace.
>> [George] Right, and while we're just looking mostly at
numbers today, the point of the forecast is to estimate when
the barriers, representing complexities, start to fall.
And then, when we can put all these pieces together,
in just explore, process, serve.
When that becomes an end-to-end pipeline.
When you can start taking the data in on one end,
get a scientist to turn it into a model,
inject it into an application,
and that process becomes automated.
That's when it's mature enough
for the knee in the curve to start.
>> And that's when we think the market's going to explode.
But now so, how do you bound this.
Okay, when we do forecasts, we always try to bound things.
Because if they're not bounded, then you get no foundation.
So, if you look at the next slide,
we're trying to get a sense of real-time analytics.
How big can it actually get?
That's what this slide is really trying to--
>> [George] So this one was one firm's take on
real-time analytics, where by 2027,
they see it peaking just under--
>> [Dave] When you say one firm,
you mean somebody from the technology district?
>> [George] Publicly available data.
And we take it as as a,
since they didn't have a lot of assumptions published,
we took it as, okay one data point.
And then, we're going to come at it with some bottoms-up
end top-down data points, and compare.
>> [Dave] Okay, so the next slide we want to drill into the
DBMS market and when you think about DBMS,
you think about the traditional RDBMS and what we know,
or the Oracle, SQL Server,
IBMDB2's, etc.
And then, you have this emergent NewSQL,
and noSQL entrance, which are,
obviously, we talked today to a number of folks.
The number of suppliers is exploding.
The revenue's still relatively small.
Certainly small relative to the RDBMS marketplace.
But, take us through what your expectations is here,
and what some of the assumptions are behind this.
>> [George] Okay, so the first thing to understand
is the DBMS market, overall, is about $40 billion
of which 30 billion goes to online transaction processing
supporting real operational apps.
10 billion goes to Orlap
or business intelligence type stuff.
The Orlap one is shrinking materially.
The online transaction processing one,
new sales is shrinking materially
but there's a huge maintenance stream.
>> [Dave] Yeah which companies like Oracle
and IBM and Microsoft are living off of that
trying to fund new development.
>> We modeled that declining gently
and beginning to accelerate more
going out into the latter years of the tenure period.
>> What's driving that decline?
Obviously, you've got the big sucking sound
of a dup in part, is driving that.
But really, increasingly it's people shifting
their resources to some
of these new emergent applications
and workloads and new types of databases
to support them right?
But these are still, those new databases,
you can see here, the NewSQL
and noSQL still, relatively, small.
A lot of it's open source.
But then it starts to take off.
What's your assumption there?
>> So here, what's going on is,
if you look at dollars today,
it's, actually, interesting.
If you take the noSQL databases,
you take DynamoDB,
you take Cassandra,
Hadoop, HBase, Couchbase, Mongo, Kudu
and you add all those up,
it's about, with DynamoDB,
it's, probably, about 1.55 billion
out of a $40 billion market today.
>> [Dave] Okay but it's starting to get meaningful.
We were approaching two billion.
>> But where it's meaningful is the unit share.
If that were translated into Oracle pricing.
The market would be much, much bigger.
So the point it.
>> Ten X?
>> At least, at least.
>> Okay, so in terms of work being done.
If there's a measure of work being done.
>> [George] We're looking at dollars here.
>> Operations per second or etcetera,
it would be enormous.
>> Yes, but that's reflective of the fact
that the data volumes are exploding
but the prices are dropping precipitously.
>> So do you have a metric to demonstrate that.
We're, obviously, not going to show it today but.
>> [George] Yes.
>> Okay great, so--
>> On the business intelligence side,
without naming names, the data warehouse appliance vendors
are charging anywhere from 25,000 per terabyte
up to, when you include running costs,
as high as 100,000 a terabyte.
That their customers are estimating.
That's not the selling cost but that's
the cost of ownership per terabyte.
Whereas, if you look at, let's say Hadoop,
which is comparable for the off loading
some of the data warehouse work loads.
That's down to the 5K per terabyte range.
>> Okay great, so you expect that these platforms
will have a bigger and bigger impact?
What's your pricing assumption?
Is prices going to go up or is it just
volume's going to go through the roof?
>> I'm, actually, expecting pricing.
It's difficult because we're going
to add more and more functionality.
Volumes go up and if you add sufficient functionality,
you can maintain pricing.
But as volumes go up, typically, prices go down.
So it's a matter of how much do these
noSQL and NewSQL databases add in terms of functionality
and I distinguish between them
because NewSQL databases are scaled out version of Oracle
or Teradata but they are based on
the more open source pricing model.
>> Okay and NoSQL, don't forget,
stands for not only SQL, not not SQL.
>> If you look at the slides,
big existing markets never fall off a cliff
when they're in the climb.
They just slowly fade.
And, eventually, that accelerates.
But what's interesting here is,
the data volumes could explode
but the revenue associated with the NoSQL
which is the dark gray and the NewSQL
which is the blue.
Those don't explode.
You could take, what's the DBMS cost
of supporting YouTube?
It would be in the many, many, many billions of dollars.
It would support 1/2 of an Oracle itself probably.
But it's all open source there so.
>> Right, so that's minimizing the opportunity
is what you're saying?
>> Right.
>> You can see the database market
is flat, certainly flattish and even declining
but you do expect some growth
in the out years as part of that evasion,
that volume, presumably--
>> And that's the next slide which is where
we've seen that growth come from.
>> Okay so let's talk about that.
So the next slide, again,
I should have set this up better.
The X-axis year is worldwide dollars
and the horizontal axis is time.
And we're talking here about these continuous
application work loads.
This new work load that you talked about earlier.
So take us through the three.
>> [George] There's three types of workloads that,
in large part, are going to be driving
most of this revenue.
Now, these aren't completely, they are completely
comparable to the DBMS market because
some of these don't use traditional databases.
Or if they do, they're Torry databases
and I'll explain that.
>> [Dave] Sure but if I look at the IoT Edge,
the Cloud and the micro services and streaming,
that's a tail wind to the database forecast
in the previous slide, is that right?
>> [George] It's, actually, interesting
but the application and infrastructure telemetry,
this is what Splunk pioneered.
Which is all the torrents of data
coming out of your data center
and your applications and you're trying
to manage what's going on.
That is a database application.
And we know Splunk, for 2016, was 400 million.
In software revenue Hadoop was 750 million.
And the various other management vendors,
New Relic, AppDynamics, start ups
and 5% of Azure and AWS revenue.
If you add all that up,
it comes out to
$1.7 billion for 2016.
And so, we can put a growth rate on that.
And we talked to several vendors to say,
okay, how much will that work load be
compared to IoT Edge Cloud.
And the IoT Edge Cloud is the smart devices
at the Edge and the analytics are in
the fog but not counting the database revenue
up in the Cloud.
So it's everything surrounding the Cloud.
And that, actually, if you look out five years,
that's, maybe, 20% larger than the app
and infrastructure telemetry but growing much, much faster.
Then the third one where you were talking about
was this a tail wind to the database.
Micro server systems streaming are very different ways
of building applications from what we do now.
Now, people build their logic for the application
and everyone then, stores their data
in this centralized external database.
In micro services, you build a little piece
of the app and whatever data you need,
you store within that little piece of the app.
And so the database requirements are, rather, primitive.
And so that piece
will not drive a lot of database revenue.
>> So if you could go back to the previous slide, Patrick.
What's driving database growth in the out years?
Why wouldn't database continue
to get eaten away and decline?
>> [George] In broad terms,
the overall database market, it staying flat.
Because as prices collapse but
the data volumes go up.
>> [Dave] But there's an assumption in here
that the NoSQL space, actually, grows
in the out years.
What's driving that growth?
>> [George] Both the NoSQL and the NewSQL.
The NoSQL, probably,
is best serving capturing the IoT data because
you don't need lots
of fancy query capabilities for concurrency.
>> [Dave] So it is a tail wind in a sense in that--
>> [George] IoT but that's different.
>> [Dave] Yeah sure but you've got
the overall market growing.
And that's because the new stuff,
NewSQL and NoSQL is growing faster
than the decline of the old stuff.
And it's not in the 2020 to 2022 time frame.
It's not enough to offset that decline.
And then they have it start growing again.
You're saying that's going to be driven
by IoT and other Edge use cases?
>> Yes, IoT Edge
and the NewSQL, actually,
is where when they mature,
you start to substitute them for
the traditional operational apps.
For people who want to write database apps
not who want to write micro service based apps.
>> Okay, alright good.
Thank you, George, for setting it up for us.
Now, we're going to be at Big Data SV in mid March?
Is that right? Middle of March.
And George is going to be releasing
the actual final forecast there.
We do it every year.
We use Spark Summit to look at
our preliminary numbers, some of the Spark
related forecasts like continuous work loads.
And then we harden those forecasts
going into Big Data SV.
We publish our big data report
like we've done for the past,
five, six, seven years.
So check us out at Big Data SV.
We do that in conjunction with the Strada events.
So we'll be there again this year
at the Fairmont Hotel.
We got a bunch of stuff going on all week there.
Some really good programs going on.
So check out siliconangle.tv for all that action.
Check out Wikibon.com.
Look for new research coming out.
You're going to be publishing this quarter, correct?
And of course, check out siliconangle.com
for all the news.
And, really, we appreciate everybody watching.
George, been a pleasure co-hosting with you.
As always, really enjoyable.
>> Alright, thanks Dave.
>> Alright, to that's a rap from Sparks.
We're going to try to get out of here,
hit the snow storm and work our way home.
Thanks everybody for watching.
A great job everyone here.
Seth, Ava, Patrick and Alex.
And thanks to our audience.
This is the Cube.
We're out, see you next time.
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