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Clough cause media presents from the

0:02

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0:04

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Morning you didn't rub your and will come back to the. Cloud

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0:20

And. It is Aaron for a quick

0:22

intro this week. We've. Been gathering

0:24

news all month for next week's clone news

0:27

show. It's. Been an interesting

0:29

to say the least month in the

0:31

emerging deck space and you'll want to

0:33

be on the lookout for that next

0:35

Wednesday. This. Week though, we're

0:37

talking about visualization of data and diving

0:39

into observe ability I've been wanting to

0:41

talk to grow fond of for a

0:44

while now and I'm glad we're able

0:46

to make that happen and we're going

0:48

to jump right into that right after

0:50

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2:06

And we're back and Aaron

2:08

it's good to be back doing this with you.

2:10

I am I am finally back after the the

2:12

much Bali who'd trip around the world. How

2:16

are you man? How have you been? I'm

2:18

good. I'm good. I more than anything I

2:20

want to know like what time zone does

2:22

your body think it is now that you're

2:24

back? How long what's the over under and

2:26

how long it's going to take for you to adjust? Yeah,

2:28

no idea. No idea. I'm in for a month now I

2:30

wake up every day between two and three in the morning

2:32

and I go to bed at eight

2:34

o'clock at night in whatever country I'm in. So

2:36

I have I have no idea. It'll probably take

2:38

a week or so to kind of get back

2:40

to normal but could be back in it back

2:42

in North Carolina back in the regular massive studios

2:44

and you know speaking of sort of you know

2:47

big things and big visibility about stuff which you

2:49

know I've been seeing the world. We

2:53

have not had a chance in a little while

2:55

to kind of talk about the thing that that

2:57

sort of sees everything and sort of looks at

2:59

things globally which is observability and today we're going

3:01

to kind of dive in and be like what's

3:03

new with observability? What are people learning? Is there

3:05

as they're deploying it all over the place and

3:08

why don't you go ahead and introduce our guest?

3:10

Yeah, absolutely. And so real quick

3:12

this is interesting too because this

3:15

actually comes out of a conversation

3:18

our guest and I we had at all

3:20

things open this year in Raleigh. So

3:23

I stopped by the booth said hey and

3:26

one conversation led to another and here we

3:28

are with doing the podcast and

3:30

so what we have this week is we're

3:32

going to be talking about obviously observability and

3:34

visualization of data in general and so we

3:37

have Ronald McCollum senior manager solutions engineering at Creative

3:39

Final Labs. So

3:42

Ronald welcome to the show. Oh,

3:44

thank you Aaron. I'm really happy to be here.

3:46

Thank you for having me on. Yes, absolutely. Before

3:49

we dive into today's discussion why don't you give

3:51

everyone a Brief introduction to your

3:53

background please? Yeah, absolutely. So, yeah, since

3:55

we met at all Things Open I

3:57

think it kind of follows that I've

3:59

been an Open. and for escape from

4:01

way were back I got started in

4:03

the linux and open source community is

4:06

back in the mid nineties of he

4:08

hopes in high school went from there,

4:10

became a programmer for a bit but

4:12

I i really always enjoyed the intersection

4:14

of a software and hardware. I like

4:16

to play with an infrastructure elected he

4:18

to hold a screwdriver occasionally so I

4:20

ended up in the monitoring space keeping

4:22

the lights on and Adidas other up

4:24

and after that of have worked at

4:26

a number of companies including Canonical which

4:28

is the company behind. Have been to

4:31

Linux where I built up the original

4:33

hardware certification program there so I got

4:35

the scratch that itch of combining hardware

4:37

and software again. Ah, and really from

4:40

there have been in a bunch of

4:42

small startups which included your photo labs

4:44

when I joined about five years ago

4:47

although it is way way better now

4:49

I and I managed to team solution

4:51

engineers. They're helping folks filled out observe

4:53

ability practices with the group. Honest. Testing

4:56

and and I'll say this real quick round

4:59

to I'm. So. We last talked

5:01

to grow fonder. Gosh, we're actually couple

5:03

times a twenty nineteen and and twenty

5:05

twenty and in links in the sooners

5:07

by the way. see if anybody wants

5:09

to go back and times observe ability

5:11

continues to be a really hot topic

5:13

on. In this combination of

5:16

roof observer ability and open source,

5:18

how are you seeing the open

5:20

source community and open source tools?

5:22

And how's that involved in this

5:24

space? Because it has been quite

5:26

a few years, Definitely, and I

5:28

really honestly believe that open source

5:30

is the natural and point for

5:33

most software. but definitely observe ability

5:35

and especially things like data collection.

5:37

So I kind of think of

5:39

it like plumbing where it's something

5:41

that you really don't normally think

5:43

about, right? It's. There it's in the

5:45

background and as long as everything's going okay,

5:48

you just ignore it. But. when

5:50

something breaks you suddenly care a lot

5:52

and you want to fix things very

5:54

very quickly so just like plumbing really

5:56

you wanna be able to both get

5:59

in there yourself and fix things if

6:01

you can, but also have a ton

6:03

of people who are already familiar with

6:05

the things that you're using can jump

6:07

in really quickly and get things fixed

6:09

and back up and working. So I

6:12

think about open source in that way

6:14

that if you are relying on proprietary

6:16

tooling for your observability system, you're

6:18

both really reducing the size of the talent

6:20

pool that can work on those tools, but

6:22

you're also locking yourself into one vendor. So

6:24

if things change out from under you, you

6:26

kind of have to go along with it

6:28

or do a very expensive lift and shift

6:31

off of that. So I think

6:33

as a result of that, we've

6:35

seen the particularly the data collection side

6:37

of things become commoditized and trend toward

6:40

open source, right? Prometheus has pretty

6:42

much one on the metric side. I think

6:44

at this point, we're seeing a ton of

6:47

momentum behind things like open telemetry for tracing

6:49

and probably eventually logging. So I

6:51

think that the real value of these things

6:53

is what you do with the data once

6:55

you collect it, rather than focusing

6:57

on kind of building out your own

7:00

data collection system, your own observability

7:02

telemetry system. Yeah,

7:04

no, it makes makes a ton of sense.

7:06

We've had Grafana on the show a few

7:08

times, we've tried to do our best to

7:10

follow kind of both observability

7:13

and dashboards. You

7:15

know, we might be naive in this, but

7:17

I think we oftentimes

7:19

hear Grafana combined with other

7:21

things. Grafana with Prometheus,

7:23

Grafana with some

7:26

set of sort of logging slash observability.

7:29

Where do you see sort of the

7:31

evolution of maybe both the role of

7:34

Grafana, but also maybe more

7:36

importantly, kind of the role of weird

7:39

dashboarding and visualization fits? Are

7:41

you seeing it mostly adopted by

7:44

infrastructure and ops teams? Is it

7:46

security teams? Is it individualized dashboards

7:48

for certain developers and certain applications?

7:50

Like how has it sort of evolved in

7:54

that sense of you know, both the

7:56

breadth of what Grafana does, as Well as

7:58

you know, whose. Using the

8:00

visualization, how are they best using it. Yeah,

8:03

I think it's a great question if

8:05

you have to look historically I think

8:08

you can really plot a line at

8:10

and feel excuse upon from people doing

8:12

things very ad hoc you know, starting

8:14

with logs In really at the back

8:16

to the very earliest days of computing.

8:20

and then really evolving from there to

8:22

add new ways of working with a

8:24

that thinking about data. So you kind

8:26

of move from this idea of individual

8:28

logs telling you about. What's. Going

8:30

on in your system to metrics that

8:32

can tell you more about things and

8:34

aggregate And as we've moved into the

8:36

cloud we see things like treating tools

8:39

that we can see how requests will

8:41

pass through very complex environments and and

8:43

touch multiple different services. So I think

8:45

if you you look at a visualization

8:47

side of things, it's really evolved in

8:49

sort of the same way. We we

8:51

started with just looking at raw logs,

8:53

having a developer kind of look at

8:55

the state of an environment, the state

8:58

of a service and figure out what's.

9:00

Happening to as those systems grew beyond

9:02

what an individual developer could look at.

9:04

having to figure out how to do

9:06

things were like our D tool and

9:08

Nog, ios and and other similar tools

9:10

that were the sort of early entrance

9:12

into that visualization and and graphing layer

9:15

of things. And then again as things

9:17

started to get even more complex you

9:19

see tools of all to be less

9:21

about directly visualization at the visualization still

9:23

important as the wanna know what's going

9:25

on but I start to think about

9:28

things like what can I automate what.

9:30

Can I have twitter and alert and direct

9:32

me to what I need to see? rather

9:34

than having somebody sitting in an operations center

9:36

all day just looking at screams and waiting

9:38

for something to go wrong? And

9:40

I think that. Really?

9:43

Ties into the the other side of

9:45

the question about who uses these tools

9:47

and how they use them. I think

9:49

it really depends on the audience that

9:51

you're talking to. So like if you're

9:53

talking to business, he asserts that the

9:55

folks that are running the actual business

9:57

itself and selling things to customers big.

10:00

Here a lot about things like return on investment.

10:02

They want to know that if they are. Investing.

10:05

In an observer delivery system, Investing in these

10:07

tools that is actually going to get the

10:09

more money. In the end, they don't want

10:11

to spend money for no good reason and

10:13

so they're going to care about things like

10:15

response rates or latency. If you've got an

10:17

E commerce solution, you're going to think about

10:19

the things that impact your users because if

10:21

they have a bad experience, they're not gonna

10:23

spend money on you. But.

10:26

If we think about this from an engineering

10:28

perspective, I think people think about. The.

10:30

Things that they want to observe very

10:32

differently so they focus on of things

10:35

like it's does your latency but in

10:37

with an individual service rather them latency

10:39

in at a summer transaction for example.

10:41

And their the folks that are gonna

10:44

care very deeply about things like infrastructure

10:46

and do I have enough sleep you

10:48

desk memory or cloud environments to support

10:51

the services that I have now and

10:53

can I continue to do that as

10:55

these things grow on something like Black

10:58

Friday. So I really do think that

11:00

there's. A couple of different audiences here

11:02

meet the care about different things, but

11:04

they're up closely enough. Related to that.

11:06

I think you can unify those two

11:08

worlds and and give everybody one view

11:10

of what's happening in their environment. And.

11:13

And rama when I think about

11:16

that in the context of of

11:18

your answer their he reminds me

11:20

of the early days of have

11:22

developed at times of I'm there

11:24

are some. Political. Considerations:

11:26

There's some cultural and cultural changes

11:29

considerations because this does impact anything

11:31

from you know, the business side

11:33

of the house to the the

11:35

asari side of the house, to

11:38

development. Ah how.

11:41

Do. You approach something when you're talking

11:43

to customers or or folks out there.

11:46

When it comes to bridging

11:48

those gaps, I'm or having

11:50

to build those alliances or

11:52

do sometimes in large scale

11:55

change. In Operations On that

11:57

seems like a delicate balance and

11:59

or maybe a recipe for disaster.

12:01

Sometimes out of the your thoughts

12:03

on that. Yeah, I have absolutely

12:05

seen people do this incredibly well,

12:07

and I've seen people do it

12:10

incredibly badly. And I think there

12:12

are. That.

12:14

There are lessons to be learned. their I'll I'll say it that

12:16

way. I really think

12:18

that the folks that are the

12:20

most successful in both building observed

12:22

ability, practice, but also in building

12:24

really reliable systems are the ones

12:27

that do it solas. Stickley that

12:29

it's really a top down. How

12:32

a drive from the sea level are

12:34

all the way down through the organization

12:37

that we're going to focus on quality.

12:39

We're going to focus on user experience

12:41

and as a result of that we

12:43

need to know what's happening in are

12:45

in agreement at all times. So the

12:47

organizations that I see that a really

12:49

successful in that way or the ones

12:51

that are thinking about things as a

12:53

serviceable agreements service level objectives right? They

12:56

have essays where their customers if I

12:58

don't. Perform. A certain

13:00

number of transactions in a in a minute

13:02

where I don't ship so many widgets and

13:04

our than I have to refund money to

13:06

somebody. So of course I'm gonna care very

13:09

deeply about making sure that I am getting

13:11

injured or targets and as a result I

13:13

can drive those as hallows, the service level

13:15

objectives internally to measure those things and to

13:17

know what I need to set to be

13:19

able to to meet those targets. And.

13:22

The reason I think that the

13:24

folks that use this approach of

13:26

the most successful are because that

13:28

Esa low focus. Actually let's you

13:30

decide where you should put your

13:33

engineering efforts right now. If.

13:35

you've got him as hello you can effectively

13:37

think about that as an error budget it's

13:39

how often can i miss my target and

13:41

still not blow my i select so if

13:43

i'm at one hundred percent i haven't missed

13:46

any targets for the month so far i've

13:48

got a lot of basically budget to work

13:50

with i can be really aggressive at building

13:52

new features rolling out experiments testing things out

13:54

and if a sale okay i can just

13:57

walk it back on still within those agreements

13:59

whereas on flip side, if I'm close

14:01

to violating those SLOs, I know I

14:03

need to focus very hard on stability,

14:05

on keeping things up and running, making

14:08

things more repeatable and calming down that

14:10

rate of change. So

14:12

yeah, I think if you if you

14:14

have that focus, that's ideal. But

14:16

I would say that I have seen people

14:18

be successful with a ground up approach in

14:21

building that kind of focus as well. You

14:23

can actually drive that from an individual

14:26

engineering level too. And I

14:28

think the way you do that is

14:30

you just start making things publicly available,

14:32

you collect information, you put some telemetry

14:34

and monitoring in on whatever you can,

14:36

and start sending that out, send an

14:38

email out once, excuse me, send

14:40

an email out once a week to all

14:43

of the managers in your organization that shows the

14:45

SLOs that you've put in place. And

14:48

when that happens, what will happen is those

14:50

managers will start to say, hey, why aren't

14:52

my services in here, I want

14:54

to show off how good I'm doing. So they'll

14:57

start to add those SLOs, they'll come to you

14:59

and say, how can I get included in this

15:01

report. And you run that for a

15:03

few months, you start to get a real groundswell of

15:06

people engaging with that kind of process

15:09

and starting to build the ideas of

15:11

running a real SLO focused organization. It's

15:13

honestly really, really cool to see when

15:15

that happens. Yeah. You

15:19

know, I tend to think about because

15:21

the space is interesting, it oftentimes, and

15:24

I kind of feel like I'm repeating that the previous question,

15:26

but I feel like we often see a lot

15:29

of tools get involved, right? And it tends

15:31

to break down to be, you know,

15:34

identify a problem or identify a situation,

15:37

try and resolve it if it's problematic

15:39

enough, you know, it's in the red,

15:41

it's above a threshold. And then oftentimes,

15:43

there's maybe a third phase of it

15:45

that might be something

15:47

that's looking at things historically, trying to maybe

15:49

do some predictive stuff for, you

15:51

know, kind of get in front of it. Where

15:54

do you I guess, two parts of the question,

15:56

where do You see Grafana

15:58

today. sort of sort of. The

16:00

the best at a been part of

16:02

that. The. String

16:04

of of trying to make life better

16:06

for operations and for applications and have

16:08

you as grow fond of tried to

16:10

you know kind of expand where their

16:12

role sets or do you guys feel

16:14

pretty comfortable with? You know the capabilities

16:16

you provide today and in where you

16:18

integrate with some of the other tools.

16:20

whether it's her you know, a logging

16:22

tool or some other type of thing.

16:26

Yeah, I think historically Griffon

16:28

I was very focused on.

16:30

The identification stage. it's is what is

16:33

happening in my environment right now and

16:35

that is because Grow Fonder really started

16:37

as a visualization tool, something that you

16:39

can plug on top of multiple different

16:42

sources of data and is visualize the

16:44

Molinar common way. And I think that's.

16:47

Often. Times what people think of when

16:49

they think a grip on up. But

16:51

really, over the last five years or

16:53

so, the platform itself has evolved, so

16:55

there's a lot more to it than

16:57

just visualization. That. Part still there,

17:00

but we've expanded the stacked much

17:02

more into covering. Really? Both

17:04

sides of the identification

17:06

and and remediation. Spectrum.

17:10

So. That We've got

17:12

tools now to do things like manage

17:14

alerting and notifications and escalation policies so

17:16

we can deal with events as they've

17:18

happened. Get the right people involved and

17:20

and surface that data up. Ah, as

17:22

well as things like back and there's

17:24

a skeletal previous back and the scale

17:26

of a logging backend of treason. Back

17:28

him than so forth for Griffon Out

17:30

that is just part of the native

17:32

stack there, so they're available to you

17:34

if you want to use them. Of

17:36

course, you can still plugin any data

17:38

source that he had before, but. I.

17:41

Actually, think that if you think about

17:43

this on a spectrum of like identification

17:45

and resolution of problems, it sells observe

17:47

ability a little bit short as a

17:49

practice because that is thinking about things

17:51

as how you react to issues. How

17:53

do I hello my notified about something

17:55

going wrong and how do I then

17:58

fix it. Were. if

18:00

you're doing observability the right way, according

18:02

to me, you

18:04

really want to be able to

18:06

understand what's happening in your environment

18:08

before an incident occurs. You want

18:10

to head off those problems before

18:12

they impact your users. So you

18:14

want to be able to do

18:16

things like load testing and scale

18:18

testing and do continuous profiling to

18:20

identify hotspots in your code. And

18:22

all of that's still related to

18:24

observability. I'm still collecting information about

18:26

my environment and about how my

18:28

services behave. But ideally, I'm doing

18:30

that upfront so that I can

18:32

identify those issues before they impact

18:34

users. So that's a real

18:36

big area of focus for Grafana right

18:39

now is building out that kind of

18:41

tooling, the ability to do those scale

18:43

tests and performance tests and continuous profiling

18:46

on your code before it actually goes

18:48

into production. That makes

18:50

sense. That makes sense. And so, Ronald, I'm

18:53

going to ask probably

18:55

the question that gets asked a good bit

18:58

these days. The

19:00

AI question. How

19:03

has AI either already changed

19:05

or in your opinion will

19:07

change both observability

19:09

and visualization? AI

19:14

is a big buzzword for sure. I

19:16

hear people talk about it almost on

19:18

a daily basis. And part

19:20

of the problem is I think every

19:22

single person that talks about AI has

19:24

a different idea about what AI actually

19:26

means. So it's very much the wild

19:28

west right now. But one

19:31

thing I have noticed is the biggest

19:33

divide seems to be between the suits

19:35

and the geeks on this one. So

19:37

like when I talk to business execs,

19:40

there's a big focus on things like

19:42

AI ops. They want automation,

19:44

they want auto remediation, they want

19:46

something that they can just drop

19:48

into their environment and have it

19:50

solve all of their problems and do

19:52

that automatically. But If I

19:54

talk to engineers, the practitioners who

19:57

are building the systems that they're

19:59

monitoring. The observe ability systems.

20:01

The things that they're interested in

20:03

are much more about surfacing the

20:05

right information and filtering out of

20:08

the noise so that they can

20:10

get to the right place and

20:12

fix the issue themselves. And honestly,

20:14

I think that ladder approach is

20:17

probably more real, at least for

20:19

the next few years. Ah, I

20:21

don't think it's impossible to automate

20:23

remediation to automate some parts of

20:26

incident response, but. Anything that

20:28

you can trivially automate is also

20:30

a pretty trivial to solve and

20:33

therefore to prevent selects. You really

20:35

do need a human level intelligence

20:37

involved in incident response and in

20:40

dealing with a of it incidences.

20:42

they occur in your environment. So

20:45

I think that. The companies that

20:47

are going to be the most successful over

20:49

the next four or five years in the

20:51

Ai space are the ones that are working

20:53

to enhance humans rather than replace them to

20:55

the ones that are are making humans more

20:57

effective. I I want

21:00

to sort of a follow up on. That

21:02

is do you get the sense? ah enough,

21:04

you're just put a stake in the ground

21:06

today. It like you said, you've got. You've

21:09

got mansion folks who are like look, I'm.

21:12

You know I just want the system to

21:14

fix itself. I wanted to be self healing

21:16

I just wanna drop it in in it's

21:18

in like a get less complaints from your

21:20

users in your constituents since and typically. Your

21:23

even just like automated systems, you have a

21:25

tendency of of the operations teams to sort

21:27

of be like. Well. They

21:30

don't know as much as we do. Not

21:32

really sure I trust the thing to do

21:34

stuff with the what do you think is

21:37

closer to be think that the technology is

21:39

is fairly close to being able to do

21:41

those things and. It's a matter of

21:43

of kind of. Convincing the

21:45

operation seems to be like it's okay. You

21:47

should let it do. These things: are you

21:49

still think we're. Quite. A ways

21:52

away from in of the gap between

21:54

sort of that that really smart operations

21:56

person that has to jump in there

21:58

and the system being. And

22:00

an equal level to where you know

22:02

that smart operations per second. In. A

22:04

quote unquote. Gonna go work on other

22:06

high value things. He? yeah. I

22:09

think there's some of of each to

22:11

be totally honest states. I do talk

22:14

to practitioners, the engineers who are concerned

22:16

about Ai taking a job. They have

22:18

been doing this for thirty years. I

22:20

I don't want to change and all

22:22

on or replace it, but I don't

22:24

think it's all that I think if

22:26

you think about. Well. Yeah, there's

22:28

the old joke about Ai that anything

22:30

that we can't yet do is artificial

22:32

intelligence, and as soon as we figure

22:34

out how to do it, it's just

22:36

automation. Ah, I think you see a

22:38

lot of that in observed ability and

22:40

in. Particularly. Cloud, skill

22:42

or infrastructure and I am as right

22:44

like communities as a great example There

22:46

to that it is is automation. It

22:48

is are all about self healing. It's

22:50

about being able to the to describe

22:52

how you want something to deploy and

22:54

then we'll let the system itself scale

22:56

it, upscale it down, kill it if

22:58

it die, if it it's into a

23:01

bad state restart things like that. So

23:03

those kind of automation I think are

23:05

going to continue to grow on it.

23:07

I think they're going to continue to

23:09

be a big part of the industry.

23:12

I think we're. We're

23:14

people might get off track and I

23:16

think we're some of the complexity isn't

23:19

always clear to the the business level

23:21

folks that are talking about things like

23:23

a i asked is that. You're

23:26

dealing with. Often.

23:28

Times when you're dealing with incidents that

23:30

are. Novel. The things that you

23:32

didn't expect. You didn't know how to look

23:34

for them up front. And so to expect.

23:37

An Ai system to be able to figure something

23:39

out that you didn't know about ahead of time

23:41

and didn't know how to even. Detect

23:44

let alone prevent. It's gonna be very

23:46

difficult to build a system that can

23:48

truly automate not only the detection side

23:50

of that, but the remediation side of

23:52

that. So I I think unless Chat

23:54

U P Five takes over the world

23:57

and and is truly sanction. ah I

23:59

don't see. That becoming were

24:01

that? that? The. State

24:03

of the industry ends up. The

24:06

mess up. My that's that's really

24:08

helpful Thank you on and so

24:10

our all out by you are

24:12

a quick of the final question

24:14

hopefully this is a softball question

24:16

are because you've. Literally.

24:18

Written a book on grow fauna on suffered

24:20

the hoses and we're interested it out there.

24:23

it's how do you recommend? They get started

24:25

with grow fonder know by the way. linked

24:27

to the book in the show notes. but

24:29

glad that a soft spot as an awesome

24:32

plug been que eran. I appreciate that. Yeah,

24:34

I tried to distill as much as I

24:36

could end of my books. And for folks

24:39

that like to learn things up front, you

24:41

know, really dig in before they get started.

24:43

A I think it's a great way to

24:45

a great place to start. I'm definitely. Going

24:48

to recommend my book. But honestly I'd also

24:50

say that Griffon A itself is pretty easy

24:52

to get into. You can go download it

24:54

for free, you can deploy it and on

24:56

your laptop or a raspberry pi are in

24:59

a container in the cloud. It it really

25:01

is very easy to get started there. you

25:03

can even use it. There's a freak tear

25:05

on grip on a cloud so I'm actually

25:08

more of a get my hands dirty kind

25:10

of person. So that's where I would start

25:12

and I'm always going to recommend the people.

25:14

Just in a kick the tires start playing

25:16

with it. I think or

25:18

Fauna is probably the easiest part of an

25:21

observer abilities stack to get started with. Because

25:23

it is pretty lightweight, it's pretty easy to

25:25

crank up and and start running. The hard

25:27

part? the real work is actually and thinking

25:29

about your observed ability Strategy: If you're always

25:31

gonna have to figure out what is it

25:33

that I care about, What signals do I

25:35

want to pay attention to them? What do

25:37

I want to do with those signals? So

25:39

there's no, again, no Ai that can really

25:41

do that for you? Yet you're always gonna

25:43

have to put in some of that work.

25:46

But have you not? Griffon itself as. A

25:48

very easy place to visualize the results of

25:50

that. and arts

25:52

that's very cool it's dwell on it it's also good

25:54

to know that it's it's not it's not so hard

25:56

that issue either you you potentially could have called the

25:59

book out you know I'm a fan of the hard

26:01

way, a la Kelsey Hightower back

26:03

in the Kubernetes days. I'm

26:06

kind of convinced that every way is the hard way

26:08

with Kubernetes, but that's just because I've been doing Docker

26:10

and bare metal for too long. Nice.

26:14

Well, listen, we want

26:16

to thank you so much for the time today. It's

26:18

been good to both, you know, kind of get

26:20

an update. You know, for us, it's

26:22

been a couple of years to get an update, but also

26:24

really kind of dig into, you know,

26:26

kind of your specialty, which is not just the

26:28

technology, but how is it used, what are the

26:30

best ways to use it? And so thank you

26:33

so much for, you know, allowing us to dig

26:35

into that. We really, really appreciate that today. Any

26:38

last, you know, one last thing, I guess, if

26:40

people want to follow up other than the book,

26:43

you know, kind of best ways to maybe engage you,

26:46

engage your team, engage, you know, getting started

26:48

with the technology that you might want to

26:50

throw out there. Yeah, absolutely.

26:52

People can always email me

26:54

at ronald.grafana.com, but feel

26:57

free to contact us through the Grafana webpage.

26:59

You can go sign up for a free

27:01

cloud account. You can download it for free,

27:03

and there's a bunch of contact info and

27:06

community info up there on that page. So

27:08

definitely welcome anybody to reach out and say

27:10

hello. Excellent, excellent stuff.

27:13

Well, listen, Aaron, you want to wrap it up, take us

27:15

home? Yeah, absolutely. So Ronald, first of all,

27:17

thank you very much for your time today. And on behalf

27:19

of myself and a very

27:21

time confused Brian, thank you very

27:24

much for listening this week. And if

27:26

you are out there, please tell a friend about

27:28

the show and please leave us a review wherever

27:30

you get your podcasts. And for that, I will

27:32

wrap for this week and we will talk to

27:34

everyone next week. Thank you

27:36

for listening to The Cloudcast. Please

27:39

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27:41

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