In episode 26 of o11ycast, Charity and Shelby speak with Parveen Khan of Square Marble Technology. They discuss Parveen’s journey into observability and the insights she’s gained as a test engineer and quality advocate.
About the Guests
Parveen Khan: I think I have a very interesting story to tell, like how I got into observability, because like I usually--
When I'm unable to attend some conferences or something, what I do usually is like, I try to follow the tweets or hashtags of the conferences.
So it was something like I was following tweets from one of the conference where Abby was doing her workshop about the observability.
Shelby Spees: This is Abby Bangser?
Parveen: Yeah. It's Abby Bangser.
Shelby: Yeah. We've had her on o11ycast before.
Parveen: Yeah. That's where I got introduced to this term.
Like until then I never knew something called observability.
So after like getting to know about that, like first word encounter, and then I was like, okay, you know, like I'm a tester.
So we're always very curious to find what if we find something new.
So it was like, what is this observability all about?
And that's where I started to like, you know, dig more into it, try to find what it is and it was such a coincidence that I was at a new company.
I joined at a new company at that period of time and I was working on a completely new product and like within the new team, and it was such a balance that I was facing those similar situations where like observability was needed and that's where I came across this term, trying to learn about it.
So it was a very nice combination of, at the right time getting to know this thing and that's where I started to learn about it and I think I was on my testing tour at that point where I was trying to pair up with different testers and developers and different topics basically.
Charity Majors: A testing tour? What is that?
Parveen: It's like pairing up with different testers by having different sessions like with anyone across the globe basically.
I can pair up with anyone for an hour or two where I'm exploring or learning about or sharing about one particular topic by picking up one each topic, the different testers and developers across the globe.
So I was trying to do that.
That's where I thought, "okay, can I ask Abby, like if I can pair on trying to learn about observability."
And it started falling on, you know, I paired with Abby and it was like, you know, she introduced me to this new world of like the whole infrastructure and observability.
She taught me how to get into this, comfortable into learning this new term and getting to learn about dealing with infrastructure and, you know--
That's where, as soon as I was getting into this, I came across a lot of tweets from Charity and that's where like everything fall in place and I still remember reading the blog post which you were sharing, like, you know, like giving questionable advices.
Like I remember that blog post when I read. So you know introduced like how to introduce observability within the team.
So I read that. So that's where my journey started into observability and it's like still, trying to learn more about it.
Charity: This feels like a great time for you to introduce yourself.
Parveen: So yeah, I am Parveen Khan and I'm a senior tester at Square Mobile Technology, which is based in London and I am more like, you know--
I call myself like as a quality advocate and because I've been mostly a solo tester on a team where I'm trying to drive the quality.
It's like trying to work with every member of the team, not just the developers, but all OPS and product managers and prod owners and trying to figure out the risks from the product and trying to bring in the quality aspect.
So I think this is what I try to do, being a tester.
Shelby: I think it's really interesting being sort of responsible and having ownership of a service when you're not the one writing the code or managing the infrastructure and so what is that like when you, I love the term quality advocate because it's, you know, this is important stuff for the business.
Parveen: Yeah. I think it's not about just having the ownership, but for me, like, you know, when I look at this observability and having the ownership of the services, I feel like--
You know, we, as a testers, we are so great at being very curious and asking different questions and I feel like observability is all about, trying to find unknown unknowns and being able to ask those questions.
So I think it is going to be so much powerful for testers if we have the ownership of those, you know, of those services and trying to work out with different tools.
Charity: Without observability, how would you find the answers to these questions?
Parveen: After knowing this, I feel like I was completely blind.
After knowing this, like, I feel like there was nothing I could know, like, you know, and one thing I would say is like, because I am a tester, like on my current team, I get access to all the tools earlier.
I would never have that access to the tools. So I was completely relying on like, what I see.
It's not about trying to find out any question or any answer about anything, but it was all about trying to see from the UI, like only see from, hardly I could go into the network tab. That is it, but now.
Charity: So you can look at the-- believe that the application was behaving for anomalies but you couldn't track it down below the surface is what you're saying.
Parveen: Yeah. If anything goes wrong, it's just like, okay, I don't know, what's gone wrong.
It has gone wrong. I don't know what has gone wrong behind it.
Charity: And that's when engineers started building like sand castles in the sky, just trying to understand, because you can look at the code and you could look at the output but everything that happens in the middle, like what the user is doing, the infrastructure that it's running on, what the code is actually doing when the user is using it on the infrastructure, that moment is opaque.
So you kind of have to like trace it through in your mind and humans are terrible at this.
Like, we are really bad at looking at code and understanding because it's too complicated.
We can't, you know.
Like a chess board has what, you know, hundreds of thousands of possibilities, well code plus infrastructure plus users has trillions of possibilities.
Like you can't model it in your head.
You can barely remember like what infrastructure changes have happened and like what components it might be hitting, you know?
Like we just have no hope, without tools.
Shelby: There's so many moving parts and I also think about like communication, even if you find an anomaly just from probing like the output of a system, and you have to communicate that over the developer team, there's so much like it's lost in translation.
I don't know if you've ever had that happen where you go back and forth and back and forth talking about a bug or something versus like being able to it's like, you see something weird you're like oh, I wonder what's happening under the surface and being actually investigate that yourself as a tester which you have the domain knowledge to be able do so.
Like this like my chip in my shoulder, where it's like, people talk about being technical and stuff, and it's not.
You don't have to write code to be technical.
We had Heidi Waterhouse a couple episodes ago talking about what it's like to be a developer advocate who doesn't write code and so I think there's this whole swath of opportunity for people who are technical and don't necessarily get to manage code changes and infrastructure changes that you can still investigate and interrogate into the system.
Charity: You can still understand what's the best of them.
Like writing a code is just one way to learn about how systems actually work.
Parveen: Yeah. That's so true. Like you don't know what's going on under the hood.
Like, you know, how many services. Like when we have these ability to look through the system, like for me as a tester, like you know.
You know which services are talking to what and how it's behaving, like, what is it happening and especially when you are raising any issues, it's not just saying that, oh there's something wrong.
It's about trying to figure out all the information and saying, okay, this is talking to this and this is what is happening and trying to give extra information, like trying to provide more information and more help.
It's not about just raising it and leaving it but it's about providing that extra bit of information so that it's more helpful for the developer or anyone who is looking into it basically.
So this kind of like, you know, it allows me to look into that and it's also about like, you know, for me, like, I think what I'm trying to see at in my guaranteeing, like when I'm working, it's like more about like trying to be more proactive rather than reactive, you know, trying to, when I'm looking at something, I know something is going wrong because I have that information, because I can see that.
So and then trying to figure out what is it affecting and how is it affecting instead of like waiting for someone like, you know, for a user to come and say, like something is wrong and that's where we dig into it.
So it's more about trying to be proactive rather than being reactive.
Charity: Yeah, it reminds me a lot of operations.
This for a long times, operations didn't write code, we were expected to run it, you know.
And so it was all about figuring out what was happening in these black boxes and the only thing that was available to us then was these low level system metrics, which we got very good at reading and interpreting, but it's like Daniel says.
His analogy is, "Yes, you could probably measure someone's heart rate or something and if you know what you're looking for, then you can tell that they broke their arm, but wouldn't it be easier if you could just look at their arm and see if it was broken, right?"
There are all of these little systems metrics that will go haywire when something is happening and, we've often like memorize like certain combinations of like, it's like scar tissue, right?
Like events, we've had outages before in the past and we've noticed a certain constellation of behaviors in our metrics.
So we remember that and like if it happens again and go oh, I know what this says but you're not actually looking at the problem.
You're not actually diagnosing it by looking at directly and that's because like the domain that most of us live in now is as much higher level domain. We're not looking at little systems.
That's Amazon's job, that's as your job, right?
We are looking at code, we're looking at lines of code and that means we need to speak the language of endpoints, variables, you know, function names, stuff like that and that hasn't really been available to us.
You know, I've been thinking a lot lately about how, for most of my career, you know, most of the engineers at any company, we're not working on things that move the business forward, they were working on infrastructure.
They were working on the databases or fracking servers or imaging EC2 instances or something.
And only like, 10% of engineers might be working on the business problems and over the course of my career that 10% has expanded, right?
Because now, there are so many infrastructure companies that write the components and now mostly you are living in the land of APIs and plugging things together but like this explains why the tools that we have that are mature, our infrastructure tools, and the thing about observability is because it lives in that high level of domain of, you know, the way you're writing the software that your business consists of it's time right?
It's time for observability. The only aggregation that observability performs, is around the request path, right?
Everything else is raw. Around the request path it's aggregated and that's because that is what corresponds to the user's behavior.
Parveen: Yeah, and it's funny, like, you know, for me, like the way I have come across this and the way I see this as a more value because I've seen the problems, like, as I said, like I joined this new team.
So it was like every issue we were facing, we didn't knew what's going wrong because we didn't have the ability to look into the system.
We could not figure out. It's like everything we were coming across we've been marking it as like blocked because we didn't know what's happening.
I think we would spend around a week or so, and try to figure out what's happening but then there were no answers because there's nothing we can look into it.
That's how much we can look into it because there's, we don't have that ability.
So that's where I was like, what do we need?
Like, you know. That's where I was trying to figure out like these questions about like, what do we need for this?
And that's, that's exactly like the way I tried to come across this is like seeing the problem and then trying to find out, okay, this is exactly what we need on our product.
So that this is exactly what will help us and that's where I think, even though lot of people know, I think somehow it's like different term or something.
Like, for example, like we know that we need some kind of visibility into our system, but how, like, you know, what do we need that like, do we need logging.
If we need observability, there's lot of changes, which we need to do on our current code and we need to redeploy it and we don't want to break anything.
That's the biggest challenge.
Shelby: When you trying to investigate something when things are already degraded and so you don't want to change anything in an order to be able to investigate it better.
I've totally been in that situation. I've been in a situation where like, it's so expensive to send certain kinds of data.
Like if you want more verbose logs, or if you want metrics on certain like, you know, variables, so they'll turn it off most of the time and then when something goes wrong, they'll turn it back on and pay a little bit extra instead of like having that data all the time.
So then you can go back in time and look like oh. Like, being able to investigate an incident that happened yesterday or a week ago or a month ago, or trends over time.
Like and so when you're flipping on and off your data spew, in order to like gulf your observability or your monitoring spend, like you lose out on a lot of that, like capability, that ability to investigate.
And so there's so many of these teams that are in this, like this place of like, you know, you just have these ongoing problems, it's a known issue and you don't know the impact of it and you don't really know how to solve it and so it's just like, this is just a bad part of the app for months, right?
And that's not production excellence, that's not what we going for and it just feels like, you know, people bend over backwards to make things work with StatsD or make things work with their logging tools but you end up having to do all this work later on to connect the dots and so just having your data be good to start with, and then being able to like ask three questions.
Charity: And I understand why we used to do this because hardware was expensive.
You know, storage was expensive. We didn't have high throughput networks, but now all that stuff is really cheap.
You know, there's no reason that we can't afford it.
You know, Honeycomb makes it so that everyone retains 60 days, no matter what because you need that in order to investigate trends.
And it feels like the pricing models of companies haven't really caught up to the modern era because they're still like trying to charge by megabyte or by gigabyte or by C or something when you know, all of that data is easy and cheap.
What is not cheap is your attention and your time.
Shelby: And I always makes me like mad, but in like it's like angry laughter when companies won't spend, the price of a laptop per year to have better observability when they'll spend their engineers like hours and hours and hours over months.
Charity: Yeah, their life force.
Shelby: And it like burns people out, you know, like, especially, it's always the people who care the most who burn out the fastest.
Yeah, and so it's just, it just makes me angry and it's like such a huge cost on the human side and also on the business side, you know, like if your employees, if your engineers can. and like, and everyone on your team, like if your product managers or your BI people.
Charity: It impacts your customers, you know.
Charity: And it's very shortsighted. It's not really expensive compared to people's time.
Replacing people who have gotten burned out and quit is far more expensive.
There are good tools out there and yet, you know, it's easier to get head count than it is to just spend on a tool.
I think that this kind of comes back to the, you know, not understanding our total cost of ownership over the lifetime of software.
Like it is incredibly cheap to write software compared to how expensive it is to maintain it over the long run.
Shelby: Yeah, that was something that I was very lucky to learn just from, you know, big speakers and writers, like very early in my career about like how 99% of the life of a software service is in like the maintenance mode. Right?
So like yeah it's important to design things well up front to make things easily changed and maintainable but most of the cost comes from that longterm maintenance and it's not yet on a maintenance thing where you just like, put it on ice.
It's an active ownership, right? It's an active and I think.
Charity: It's like a garden. You have to prune it. You have to like, make it grow to living thing.
Shelby: And I think that's where like a culture of observability extends beyond the developers, especially is if you design a system, that's observable.
If you write your data to actually map to what matters for your business, then your product managers can go on and observe things in production, your BI people can go in and ask questions.
It's like, why are things bad for this customer?
Well, let me go look it up and so that starts to get into this like, you know, if you're designing things well on the code side, then it can become much more accessible to the technical people who don't write code.
Charity: Yeah. The production adjacent teams need to understand production just as much as the people who are writing the code. Yeah.
Shelby: And then everyone's just like in the same boat, you know, and we're all working towards the same goal.
And so that's, that's what I, you know, I get so excited about this stuff because I've been so lucky to work with people who really care and that's what sounds like Pervin like, what you're doing at your company is you care so much and you're trying to move the needle for your team. Just like, look what's possible.
And so like how have you gone about like, trying to like convey this to your teammates?
Charity: I feel like the biggest thing that we have to fight, and I'm sorry I cut you off there but like the biggest thing that we have to fight is like learned helplessness, is just this idea that this is just what it's like working with computers.
You're just never going to know what's going on and it's just going to be shitty and your production is just going to be like this hairball of shit that like got coughed out one day, nobody ever expected to understand it.
You know, people get so cynical and the reason they get cynical is because they've told it couldn't be better before and they've been burned and they get tired of believing, right?
And I do think there's a lot of truth in the idea that, for you to really go through the cognitive exercise of learning something new, it has to be order of magnitude better than what you had before, because you know how to use the stuff you've got now right?
And I feel like the constellation of tools around deploying better and more safely and understanding your systems has only recently kind of crossed that threshold of being an order of magnitude better until it, until now it's been like 20% better, 50% better.
You know, even if it's like twice as good, is that really worth, you know, forcing everyone to like throw out the known stuff and then like it dots something new and unknown.
It's kind of not, you know, and I think that if you look at The Dow Report year over year, what you're starting to see just in the last two years, the tooling has gotten to a place where is an order of magnitude better and the teams that do start to adopt it, do see just like compounding benefits and they can move dramatically faster and more safely with more confidence than teams that don't use it.
So, sorry Parveen, up to you.
Parveen: Yeah, as I said, like, I've seen this as similar to how the DevOps movement is like, you know, it's not about getting the tools and saying that we are following DevOps.
So it's something similar for me like as far as I know, like, you know, I feel like it is something like, it's not about having some tooling in place and saying that yeah, we are following observability.
For me like, again, it is a cultural shift. It's a cultural change which needs like, you know, to come from leadership basically.
Charity: I think that it can come either top-down or bottoms-up. We've seen it both ways.
Parveen: Yeah. I think I say that majorly because for me, like me being a tester and trying to bring in the change because I come from using that observability point of view and not trying to implement point of view.
So I'm not a technical person, or I can't say that because I cannot do it and show it like, okay, look, this is what it will bring.
So I am trying to come from a point where like, okay, look, if we try to implement this, this is what we are going to get into our system and this is what we can save and this is what we can learn about a system.
We can't just say that our system is so complex that's the reason why we are not trying to figure out what's going on under the hood. It's about try to build the right thing under the hood so that we can get those answers like, you know, and partly because me being the tester, trying to get this word out.
So the way I tried was like I know like a lot of people in my team, they know about the logging and all that stuff, but I kind of organize a lunch and learn session.
So I've said, okay, let me try to introduce this term. Let me get this term out in my team.
So I tried to give that and then I'm trying to use a sample so that I can show like, you know, show what does this bring the value?
Like, you know, so that I can bring in the change basically.
So that's the reason why I said it's like, not just, it is the change can come from anywhere, but because I think it's majorly because me as a tester, trying to bring this value within the team.
So that's the reason why I said like it needs to be not just from me, not just from anyone from the team, but.
Charity: I think you're right, like there is like a pecking order within technical assistance teams and that's, you know, it shouldn't necessarily be there, but it is true.
Like the people who write code are the top of the heap, and the people who have been there longer, who are more senior at the top of that heap. Right?
And the people who don't write code are lower you know and I think that you said there's something there, like, it's not that just anyone can bring something in, like you have to have credibility with the team and it's only, that's only partly under your control.
That's partly under other people's control. So that is very valid.
But I do think that, like, I think even there, if you can band together with a critical mass of people, you know, like you can kind of overcome some of that.
I think that you can start persuading, you know, people who, the people in the team who seem more pro-tool, who are more open to new ideas, the people in the team who love geeky out over understanding systems because it's not all of them, but some of them--
I think that like lunch and learns are awesome, but I think you also often have more success if you take them privately and just like, get them excited about something, you know, and then they go and championing it.
Charity: I do think that it's too early to give up on bottoms-up as a concept because some of the most successful teams we've seen, have been ones where it is usually the developers but the developers bring it in because we're writing a tool for developers, you know, has ancillary benefits for other teams.
But we're writing this tool for the people who are writing the code, who need to understand it in production, like they're our use case.
So I just don't want to people to be left with the idea that if it doesn't come from leadership too late, you know, leave your job because a lot of teams do have a lot of great successes bringing it in showing that it works and then, pitching it to higher-ups.
Shelby: And I think that's how you get buying from leadership, is as you get enough developers and you get enough people on the team to be like, "Oh, like, this is, this is where we should go."
And the hard part is if you're not in a position to add instrumentation to code, then it's very hard to show the impact of that and start getting more people to start getting involved with that.
It's exactly what happened on my last team where I was lucky enough that one of the developers looked at Honeycomb.
I was like oh okay this is exactly what I've always wanted and just dove in head first and just, I mean, he'd start spending nights and weekends, like getting instrumentation working and stuff and that's also not always like a good path for people.
I don't want people to be spending nights and weekends to get to further their observability journey, but you know, this person's also just that enthusiastic.
And I think this is where it's important, where for teams to make space for people to explore, because that's how you find these, you know, these impactful changes and that's how you can test them out and so what we're trying to to do at Honeycomb and just in general and in the observability community is make it easier for people to try things out and make it easier to get started.
And just see how different it is compared to the status quo of monitoring and logging and tracing tools and if the three pillars type stuff where you're sending your data separately and having to connect the dots later on.
Like, what if you just sent data that's like structured to start with and just see the difference there and so having that, that self-serve option can really make a difference for a lot of developers who are just like, oh, this observability thing seems cool, but I don't want to spend my entire weekend getting at working.
Charity: Yeah. We've actually put a lot of engineering effort into making it pretty turnkey, you know, so that all you have to do is install a library or you know, link to an implementation.
Like we gather up all of the information for you.
We, provide a framework so that it's basically like doing a print ad, if you want to append more data to it and this is why, because almost nobody adopted it until we did all that stuff to make it, you know, pretty magical for them.
You know and partly this is a shame.
What this tells me is that, vanishingly few people have seen the impact of good instrumentation and over the course of their career. They don't know what a difference it makes.
They think that the magical stuff that they're being, you know, sent by, you know, all the vendors are just like, just give us millions of dollars.
We'll do all the work for you and then you don't have to know how to instrument your code, which is a fucking trap because your vendors do not know your business use case and observability is all about your business use case.
We can provide helpers, we can provide stuff that guesses, we can provide stuff that gives a lot of the defaults.
We can do stuff around the structure of, you know, the parameters that are passed in the underlying systems, but only you know what you're trying to do with your code and only you know what you should capture in order to really shine a light on what you're trying to build for your business.
Shelby: Mm-hmm. And that's exactly what I was talking about, where like, if you're designing your code to be observable, then it can be much more self-serve--
Like it answers the business questions that people who don't live in the code will be asking and so then they can self serve start running those queries and getting those questions answered and they don't have to bug your engineers, you know, four times a day, every time something's weird.
And that requires writing good instrumentation and writing instrumentation that sends the data that's meaningful to your business.
Charity: And it kind of has to be done by the person who is implementing the business logic, you know. It has to be done at that time.
I don't believe it's possible to go along years after code was written and reinstrument it in as good of a way as you could do while you're writing it.
I think instrumentation should be seen just like commenting your code.
Right, is commenting your code. It's just including a little bit of reality in there because it's common that your code emits from deep inside of your infrastructure, right?
Which is like, when you don't have that original intent in your head, when you don't know what you're trying to do, how can you write good comments about it?
You know, you can come along later, you can guess what they were trying to do.
You can often guess pretty well, but you can't ever guess perfectly and sometimes you guess badly.
The connection between commenting and instrumentation reminds me of like what I learned at my first job when I had to give a whole lot of technical presentations to non-technical managers and the advice that they gave me was like, don't just talk about like what you implemented, talk about the so what, like why is this important?
Why do I care? What's the business impact? And that's really what we should be thinking about when we're writing our code.
Like, what's the point of this? Like, so what, like, why is this code even exist?
Well, that's what goes in your comments and that's what goes in your instrumentation.
Parveen: Yeah. And I think that's really a good point where you mentioned that okay, look, it's about giving the ability to anyone who can just run the query and find out what's going on.
Like, you know, it's about like, like for example like we have on our team, like we have, ours is a multitenancy product.
So we have multiple project managers who always have some questions.
They want to know certain things when something is not working.
They have such a great questions. Like, you know, they have such a great questions, but then they need some time from the engineer.
So to answer those questions, so we like we are not full blown, like, you know, we are not completely there yet, but we are taking step by step approach where we have some structured laws in place.
A bay we can query and find out what's going on.
So we are trying to give that ability to our project managers so that they can, you know, if they are interested--
Like it's not about forcing them, but it's about if they're interested to finding out what's going on, you know, giving that power to them as well.
Charity: Everyone does a better job if they can self-serve from production, if they can get real answers from prod without having to have a translator there in place.
Shelby: And I think that's like another of the failings of these complicated tools where you have to learn their bespoke query language in order to parse, you know, the log spew from production.
And then it's like a 32nd, like turnaround time to get an answer from your query and so you never really learned the query language because it's like the terrible feedback loop and so frustrating.
You know, product managers and all these folks like they're technical, like the, you know, they go in and they write these Excel macros that like blow my mind.
It's not that they can't go in and investigate a system and learn these tools, it's the tools don't allow you to learn them.
And so that's why I think like, like at Honeycomb we've a relatively strict as I love for career results because we want that feedback loop to be really tight, you know and I think it's helped me learn our systems better because I can just go in and just start investigating and poking around and it's like, "Oh okay, well that wasn't, you know that wasn't the right thing to ask. Let me ask a new question," and that's how we learn as humans.
Charity: Yeah. You definitely shouldn't have to like compose a query and then go get coffee then like by the time you've gotten back, what even were you thinking? You know, it really does have to be rapid in order for you to iterate on it.
Charity: This is something that like we rarely call out and when we're talking about Honeycomb, because it seems so obvious to us, but so many times new people start using Honeycomb and that's the thing that they're most blown away by. They're just like, how is it so fast?
Shelby: Yeah and so it's just, I think about all, I mean, I have a background in like education and I was a teacher and stuff.
So I think a lot about just like the learning process and the onboarding process for like new tools and new paradigms and it's really important to have those feedback loops.
And, you know, when I was working in a job where like I was making changes to Chef cookbooks and it was like a five, 10 minute like feedback loop, like I would forget what I was doing by the time I got the result of my change.
And like, this is like, you know, we are making a lot of big, heavy changes.
We were upgrading our Chef version and it was really important for me to like, know what broke and stuff and so, and it was, gets really, really frustrating.
And so, you know, not having to change context, switch context between asking your question and getting your answer. It's just important across the board.
Charity: Well, what would you say to other testers who haven't tried observability yet or who maybe are the very, you know, just hearing about it for the first time and what would you tell software engineers who I guess listen to testers more?
I don't know. What rants do you have for your software engineers?
Praveen: Yeah, I think for testers that this is something completely new and I think we are still in the early stages of trying to understand.
Like, you know what, like it's something like, oh, is this for us? Like, do we have to learn this? Is it something for us or not?
Because I see this thing, like, you know, when I'm trying to learn, so I want to attend lot of like webinars or like now we are all virtual, so virtual conferences to like, learn more about it.
So it's about like, when I was trying to register myself, like, you know, you have those options about like, what do you do?
Like, you know, most of the options were like SRE, Ops or a developer.
So I used to feel like oh, even we, as a testers want to learn about this. So we should have that option for sure.
So I feel like for any testers like we are very good at having, you know, having to learn new tools, so which can help us in building our quality.
So, I really like to say this to any testers, who are trying to explore about this observability is like it is about, we trying to learn another thing.
So that that helps us in delivering better quality and this is something very new for us as a testers basically.
So that's the reason why I think I am trying to learn and share about what I'm trying to learn about observability and I think I try to blog about it to say, like, just sharing my viewpoints and my experience about how I'm learning and what I'm trying to get out of it basically.
Shelby: Well, thank you so much for joining us today. Oh and Parveen, you're giving a talk on observability in a couple of months, right?
Parveen: Yeah, I'm just sharing how I try to learn and what I'm trying to do so just again as like sharing my experience so that any testers like, if they're interested, they can learn from.
You can know I can share what I'm learning.
So yeah, I'm trying to share my journey so far, how I learn and what I'm doing with observability.
So I'm doing this at another conference in couple of months.
Shelby: And cool we'll be sure to link to that in the show notes.
Charity: Thanks for that.
Shelby: Thanks so much.
Praveen: Thank you so much for having me. Thank you.