(upbeat music)
>> Dave Volante: Okay. We're back with Mark Hill.
who's the Director of IT Operations at Digital River.
Mark. Welcome to the cube. Good to see you.
Thanks for having me. I really appreciate it.
>> Hey, tell us a little bit more about Digital River,
people know you as a, a payment platform,
you've got marketing expertise.
How do you differentiate from other e-commerce platforms?
>> Well, I don't think people realize it,
but Digital River was founded about 27 years ago.
Primarily as a one-stop shop for e-commerce right?
And so we offered site development, hosting,
order management, fraud, expert controls, tax,
um, physical and digital fulfillment,
as well as multilingual customer service,
advanced reporting and email marketing campaigns, right?
So it was really just kind of a broad base for e-commerce.
People could just go there.
Didn't have to worry about anything.
What we found over time as e-commerce has matured,
we've really pivoted to a more focused API offering,
specializing in just our global seller services.
And to us that means payment, fraud, tax,
and compliance management.
So our, our global footprint allows companies
to outsource that risk management
and expand their markets internationally,
um very quickly.
And with low cost of entry.
>> Yeah. It's an awesome business.
And, you know, to your point, you were founded way before
there was such a thing as the modern cloud,
and yet you're a cloud native business.
>> Yeah.
>> Which I think talks to the fact that,
that incumbents can evolve.
They can reinvent themselves from a technology perspective.
I wonder if you could first paint a picture of,
of how you use the cloud, you use AWS, you know,
I'm sure you got S3 in there.
Maybe we could talk about that a little bit.
>> Yeah, exactly.
So when I think of a cloud native business,
you kind of go back to the history. Well, 27 years ago,
there wasn't a cloud, right?
There wasn't any public infrastructure. It was,
we basically stood our own data center up in a warehouse.
And so over our history,
we've managed our own infrastructure
and collocated data centers over time through acquisitions
and just how things worked.
You know those over 10 data centers globally.
for us it was expensive,
well from a software hardware perspective,
as well as, you know, getting the operational teams
and expertise up to up to speed too. So,
um, and it was really difficult to maintain
and ultimately not corridor our business, right?
Nowhere in our mission statement,
does it say that we're our goal is to manage data centers?
So, so about five years ago,
we started the journey from our hosted into AWS.
It was a hundred percent lift it and shift plan,
and we were able to bleed that migration
a little over two years, right.
Amazon really just fit for us. It was a natural,
a natural place for us to land
and they made it really easy here for us
to not to say it wasn't difficult, but,
but once in the public cloud,
we really adopted a cloud first vision.
Meaning that we'll not only consume their infrastructure
as the service,
but we'll also purposely evaluate and migrate to software
as a service. So I come from a database background.
So an example would be migrating from self deployed and
managed relational databases over to AWS RDS,
relational database service.
You know, you're able to utilize the backups,
the standby and the patching tools.
Automagically, you know,
with a click of the button. And that's pretty cool.
And so we moved away
from the time consuming operational tasks and,
and really put our resources into revenue
and generate new products, you know,
like pivoting to an API offering.
I always like to say that we stopped being busy
and started being productive.
>> Ha ha. I love that.
>> That's really what the cloud has done for us.
>> Is that you mean by cloud native?
I mean, being able to take advantage of those primitives
and native API. So what does that mean for your business?
>> Yeah, exactly.
I think, well, the first step for us
was just to consume the infrastructure right, in that,
but now we're looking at targeted services
that they have in there too.
So, you know, we have our, our,
our data stream of services. So log analytics, for example,
we used to put it locally on the machine.
Now we're just dumping into an S3 bucket
and we're using Kinesis to consume that data,
put it in Eastic and go from there.
And none of the services are managed by Digital River.
We're just utilizing the capabilities
that AWS has there too. So.
>> And as an e-commerce player, retail company,
we were ever concerned about moving to AWS as a possible
competitor, or did you look at other clouds?
What can you tell us about that?
>> Yeah. And, and so I think e-commerce has really matured,
right? And so we, we got squeezed out
by the Amazons of the world.
It's just not something that we were doing,
but we had really a good area of expertise
with our global seller services.
But so we evaluated Microsoft.
We evaluated AWS as well as Google. And, you know,
back when we did that, Microsoft was Windows-based.
Google was just coming into the picture,
really didn't fit for what we were doing,
but Amazon was just a natural fit.
So we made a business decision, right?
It was financially really the best decision for us.
And so we didn't really put our feelings into it, right?
We just had to move forward
and it's better than where we're at.
And we've been delighted actually.
>> Yeah. It makes sense. Best cloud, best, best tech.
>> Yeah.
>> Yeah. I want to talk about ChaosSearch.
A lot of people describe it
as a data lake for log analytics. Do you agree with that?
You know, what does that, what does that even mean?
>> Well, from, from our perspective,
because they're self-managed solutions were costly
and difficult to maintain, you know,
we had older versions of self deployed using Splunk,
other things like that, too.
So over time, we made a conscious decision
to limit our data retention in generally seven days.
But in a lot of cases, it was zero.
We just couldn't consume that, that log data
because of the cost, intimidating in itself,
because of this limit, you know,
we've lost important data points use for incident triage,
problem management, problem management, trending,
and other things too.
So ChaosSearch has offered us a manageable and
cost-effective opportunity to store months,
or even years of data that we can use for operations,
as well as trending automation.
And really the big thing that we're pushing into
is an event driven architecture
so that we can proactively manage our services.
>> Yeah. You mentioned Elastic,
I know I've talked to people who use the ELK Stack.
They say you there's these exponential growth
in the amount of data.
So you have to cut it off at whatever.
I think you said seven days or, or less you're saying,
you're not finding that with, with ChaosSearch?
>> Yeah. Yeah, exactly.
And that was one of the huge benefits here too.
So, you know, we were losing out
if there was a lower priority incident,
for example, and people didn't get to it until eight,
nine days later. Well, all the breadcrumbs are gone.
So it was really just kind of a best guess
or the incident really wasn't resolved.
We didn't find a root cause.
>> Yeah. Like my video camera down there.
My, you know, my other house, somebody breaks in
and I don't find out for, for two weeks
and then the video's gone. That kind of same thing.
>> Yep
So, so, so how do you, can you give us some more detail
on how you use your data lake and ChaosSearch specifically?
>> Yeah, yeah.
Yep. And, and so there's, there's many different areas,
but what we found is we were able to easily consolidate data
from multiple regions, into a single pane of glass
to our customers.
So internally and externally, you know,
it relieves us of that operational support
for the data extract transformation load process, right?
It offered us also a seamless transition for the users,
who were familiar with ElasticSearch, right?
It wasn't, it wasn't difficult to move over.
And so all these are a lot of selling points, benefits.
And, and so now that we have all this data
that we're able to, to capture and utilize,
it gives us an opportunity to use machine learning,
predictive analysis. And like I said, you know,
driving to an event driven architecture.
>> Okay.
>> So that's, that's really what it's offered.
And it's, it's been a huge benefit.
>> So you're saying
that you can speak the language of Elastic.
You don't have to move the data out of an S3 bucket
and you can scale more easily. Is that right?
>> Yeah, yeah, absolutely.
And, so for us,
just because we're running in multiple regions
to drive more high availability,
having that data available from multiple regions
in a single pane of glass or a single way to utilize it,
is a huge benefit as well.
Just, you know, not to mention actually having the data.
>> What was the initial catalyst to sort of rethink
what you were doing with log analytics? Was it cost?
Was it flexibility? Scale?
>> There was, I think all of those went into it.
One of the main drivers.
So, so last year we had a huge project,
so we have our ELK Stack
and it's probably from a decade ago, right?
And, you know, a version point oh two or something,
you know, anyways, it's a very old,
and we went through a whole project
to get that upgraded and migrated over.
And it was just,
we found it impossible internally to do, right?
And so this was a method for us to get out of that business,
to get rid of the security risks, the support risk,
and have a way for people to easily migrate over.
And it was just a nightmare here,
consolidating the data across regions.
And so that was, that was a huge thing, but yeah,
it was also been the cost, right?
It was, we were finding it cheaper to use ChaosSearch
and have more data available
versus what we're doing currently in AWS.
>> Got it. I wonder if you could,
you could share maybe any stories that you have
or examples that, that underscore the impact
that this approach to analytics is having on your business,
maybe your team's everyday activities, any,
any metrics you can provide
or even just anecdotal information.
>> Yeah. Yeah.
And, and I think, you know,
one coming from an Oracle background here, so
Digital River historically has been an Oracle shop, right?
And we've been developing a reporting
and analytics environment on Oracle
and that's complicated and expensive, right?
We had to use advance features in Oracle,
like partitioning materialized views,
and bring in other supporting software
like Informatica, Hyperion, Sbase, right?
And all of these required our large team
with a wide set of expertise
into these separate focus areas, right?
And the amount of data
that we were pushing at the ChaosSearch
would simply have overwhelmed this legacy method
for data analysis than a relational database, right?
In that dimension, the human toll of,
of the stress of supporting that Oracle environment,
meant that a 24 by seven by 365 environment, you know,
which requires little or no downtime. So,
just that alone, it's a huge thing.
So it's allowed us to break away from Oracle,
it's allowed us to use new technologies that make sense
to solve business solutions.
>> I, you know, ChaosSearch is really interesting company
to me. I'm sure like me, you see a lot of startups,
I'm sure they're knocking on your door every day.
And I always like to say, okay, where are they going after?
Are they going after a big market?
How are they getting product market fit?
And it seems like ChaosSearch has really looked at,
hard at log analytics
and kind of maybe disrupting the ELK Stack.
But I see, you know, other potential use cases, you know,
beyond analyzing logs. I wonder if you agree,
are there other use cases that you see in your future?
>> Yeah, exactly.
So I think there's, one area would be Splunk, for example,
we have that here too. So we use Splunk versus, you know,
flat file analysis or other ways to,
to capture that data just because from a PCI perspective,
it needs to be secured
for our compliance and certification, right?
So ChaosSearch allows us to do that.
There's different types of authentication.
Um, really a hodgepodge of authentication
that we used in our old environment,
but ChaosSearch has a more easily usable one,
One that we could set up,
one that can really segregate the data
and allow us to satisfy our PCR requirements too. So,
but Splunk,
but I think really deprecating
all of our ElasticSearch environments are homegrown ones,
but then also taking a hard look at
what we're doing with relational databases, right?
27 years ago, there was only relational databases;
Oracle and Sequel Server.
So we we've been logging into those types of databases
and that's not, cost-effective, it's not supportable.
And so really getting away from that and putting the data
where it belongs and that was easily accessible
in a secure environment and allowing us to,
to push our business forward.
>> Yep. When you say, where the data belongs, right?
It sounds like you're putting it in the bit bucket, S3,
leaving it there,
because it's the the most cost-effective way to do it
and then sort of adding value on top of it.
That's, what's interesting about ChaosSearch to me.
>> Yeah, exactly.
Yup. Yup.
Versus the high priced storage, you know,
that you have to use for a relational database,
you know, and not to mention that the standbys, the backups.
So, you know, you're duplicating, triplicating
all this data too in an expensive manner, so yeah. Yeah.
>> Yeah. Copy. Create. Moving data around
and it gets expensive.
It's funny when you say about databases, it's true.
But database used to be such a boring market.
Now it's exploded.
Then you had the whole no Sequel movement
and Sequel, Sequel became the killer app.
You know, it's like full circle, right?
>> Yeah, exactly.
>> Well, anyway, good stuff, Mark, really,
really appreciate you coming on the Cube and,
and sharing your perspectives.
We'd love to have you back in the future.
>> Oh yeah, no problem.
Thanks for having me. I really appreciate it.
(upbeat music)
>> Okay. So that's a wrap.
You know, we're seeing a new era in data and analytics.
For example, we're moving from a world
where data lives in a cloud object store
and needs to be extracted,
moved into a new data store, transformed, cleansed,
structured into a schema, and then analyzed.
This cumbersome and expensive process
is being revolutionized by companies like ChaosSearch
that leave the data in place and then interact with it
in a multi-lingual fashion with tooling,
that's familiar to analytic pros.
You know,
I see a lot of potential for this technology beyond just
login analytics use cases, but that's a good place to start.
You know, really, if I project out into the future,
we see a trend of the global data mesh,
really taking hold where a data warehouse or data hub
or a data lake or an S3 bucket
is just a discoverable node on that mesh.
And that's governed by an automated computational processes.
And I do see ChaosSearch as an enabler of this vision,
you know, but for now,
if you're struggling to scale with existing tools
or you're forced to limit your attention
because data is exploding at too rapid a pace,
you might want to check these guys out.
You can schedule a demo just by clicking the button
on the site to do that.
Or stop by the ChaosSearch booth at AWS Reinvent.
The Cube is going to also be there.
We'll have two sets, a hundred guests.
I'm Dave Volante. You're watching the Cube,
your leader in high-tech coverage.