Hello and welcome to 'This is My Architecture.'
Today, I'm joined by Edwin from Cohesity.
Hi, Marie. Hi, Edwin, so let's start off.
Can you tell me a little bit about Cohesity?
Sure, Cohesity provides a comprehensive data management platform
for backup recovery, DR, analytics and security,
we've called Cohesity Helios,
and this platform is built on modern architecture,
software defined. It can be deployed on premise,
in a customer's AWS account as well as continue it as a service
which we're talking about today would you call data management as a service.
That's awesome, I'm really looking forward
to hearing about data management as a service.
But you guys take a little bit of a different spin on it.
Can you tell me more? Definitely, definitely.
And what we did is with our DMaaS service is we wanted to make something
enterprise grade that's scalable, that will scale linearly and up.
As well as has flexibility for our customers
to do data in whatever environment that they need to.
Yeah, that's great.
And so, let's dive in. Can you tell me a little bit
about where we would start? Yeah, so the control point
is the management plane, and it's built around microservices
in EKS. And the micro services
allow our users to come in from the UI
when they come in and use the service,
as well as it uses for internal processing
when we're doing analytics for the data inside.
And it can scale from hundreds to thousands
as we consume multiple petabytes of data
and process data both internally as well as externally.
Well, that's great. Can you tell me a little bit more
about those microservices? Yeah, the microservices are leveraged
in multiple ways, and we chose EKS
because we want to add new services when they come on board,
but also the services are used to do data processing.
So customers can get billing information,
analytics about their backups as well as the UI
and how they manage their environment.
And so I see Elasticsearch and Aurora, so why are we using both?
Well, we're using both because customers' data comes in,
and we want to store it in Aurora. And then, what we want to do
is continually analyze the data so that customers know
about any type of alerts or any type of analytics
that they need to know on their backup.
And so how has Aurora and Elasticsearch made this architecture work
for you guys and for your customers? Yeah, I mean having native services
in AWS has helped us provide the information very quickly
and store it for a varied amount of time, like 30 days.
And continue to give customers updates when they need it in real-time.
So I see that we have PrivateLink and Transit Gateway next here
on that architecture. Tell me how that fits in.
Yeah, so we talked a little bit about the data plane.
That's where we store-- the customer stores their data.
And any type of data we want to emphasize is secure.
So we're leveraging PrivateLink to make sure that
if it's an inter-region data plane, that it's going over the PrivateLink
and leveraging everything in a private VPC.
So we're leveraging VPC,
so it's staying internally and it's secure.
And then we have regions in other-- across the Americas,
across different geographies,
and we use Transit Gateway in order to talk to those.
So everything uses the backbone for protection and speed.
And then what we'll also do is, as the data is moving between,
using the KMS to encrypt the data to make sure that
any of the data in flight, as well--is stored securely.
Yeah, that's great, so,
for PrivateLink and Transit Gateway,
say I want to have inter-region data as well as geodata, how does that work?
Yeah, it works really, really great,
and the PrivateLink provides that secure connection between...
if this is in the East, this data plane is in the East,
it provides that private link so all the data is internally,
and it has the speed of the backbone,
and then if I'm going to a different region across the country or a different geo,
we are using Transit Gateway to make sure that data is secure
and also leveraging the backbone of AWS.
Yeah, that's great
and so I want to take us to this final part of the data plane.
So, I know you mentioned KMS, obviously that's super important
to make sure data is secure. Can you talk to me a little bit about
how we go between these 2 planes?
Yeah, definitely. So, the control plane and data plane we've separated,
so we can scale, have a modular configuration,
and the data plane is leveraging EC2 and S3, and what we do is,
when a customer subscribes to the service, we're using a KMS to encrypt all the data
that's moving across the link into the data plane,
as well as for each tenant, they have their own unique environment
that's provisioned automatically when they subscribe to the service.
So we can scale up linearly and horizontally depending on the customer's use case.
Yeah, that's great. So, kind of taking a look at this
modern approach to data management in general,
what's most advantageous for your customers?
Really, the most advantageous thing is to backup very quickly,
and we have customers who can do this in less than an hour.
So what typically took months or days or weeks,
they can do very quickly and leverage the data and have it very secure,
scalable, and then it can be globally procured.
Thanks for joining us today, Edwin. I've learned a lot about data management
as a service.
And thank you for watching 'This is My Architecture.'