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Serverless Chats

142 EpisodesProduced by Jeremy Daly & Rebecca MarshburnWebsite

Serverless Chats is a podcast that geeks out on everything serverless. Join Jeremy Daly and Rebecca Marshburn as they chat with a special guest each week.

56:41

Episode #43: The State of Serverless Report with Stephen Pinkerton and Darcy Rayner

About Stephen Pinkerton:

Stephen Pinkerton is a Product Manager at Datadog. Stephen has also held roles in product strategy and software engineering, working with teams at Google's Nest Labs, Facebook, Cloudflare, Square, and Monzo to ship products, build distributed microservices, debug real-time embedded devices, develop features for modern frontend apps, and create data pipelines.

About Darcy Rayner:

Darcy Rayner is a Software Engineer at Datadog, and previously worked as Lead Software Engineer at at Two Bulls, a boutique software development firm, running the front-end chapter. Darcy’s projects boast clients like Disney, PBS, LIFX, Verizon and the Linux Foundation.

Datadog’s Research Report: The State of Serverless: https://www.datadoghq.com/state-of-serverless/

WATCH THIS EPISODE ON YOUTUBE: https://youtu.be/VC1CjUsKqBI

Transcript:

Jeremy: Hi, everyone. I'm Jeremy Daly, and you're listening to Serverless Chats. This week, I'm chatting with Stephen Pinkerton and Darcy Rayner. Hi, Stephen and Darcy, thanks for joining me.

Stephen: Hey, how's it going? Thanks for having us on.

Darcy: Hi.

Jeremy: So Stephen, you are a product manager for Serverless at Datadog, so why don't you tell the listeners a little bit about what Datadog does and a little bit about your background.

Stephen: So Datadog is a company that lets you monitor all of your servers, all of your application performance, if your website's up or down, all your application logs in one place and it joins all these disparate data sources together, so that whenever you need to explain something or debug something, you can join all of this different data and really figure out root cause quickly. So I've been working here about a year, focusing on our serverless integration, so that's helping our customers running products like AWS Lambda, to be successful in deploying new services built on top of serverless and then debug issues with their applications.

Jeremy: Awesome. Darcy, you are a senior software engineer for the Serverless Team at Datadog, so why don't you tell us about your background and what your role is at Datadog.

Darcy: Sure. So I've been at Datadog about a year in the Serverless Team, before I joined Datadog, I was working at an agency. So we were massive serverless adopters in everything we did. Everything was about getting stuff off the ground running very quickly, and low cost to our customers. But while I was there, I realized that there was still a bit of a gap in terms of the monitoring story. So I joined Datadog about a year ago to work on some of the integrations that we're building here with services like Lambda or Azure Functions or GCP Functions.

Jeremy: Awesome. So a couple of weeks ago, Datadog came out with this very, very cool report called the State of Serverless. You basically looked at a bunch of your clients, went through and figured out how they were using serverless, broke it all down. This is really great, can you, maybe Stephen, can you give me some background on what was the reason for running this or for putting this report together?

Stephen: So we're in a unique position where customers of all different sizes, with all these different use cases are sending all their data to us, and we frequently get questions when we're on the phone with them of, "How do I run serverless successfully?" So this is from customers who are moving workloads into serverless, or they're 100% serverless and they're asking us, "Which metrics do I pay attention to, or how do I get data out of serverless, or how do I run this in a cost efficient way?" So we frequently get these questions, and the report was a way for us to look at data across all of our customers, across all these different data sources that we have and say, "Here's exactly how people are running on serverless." Which technologies are they using, what are they monitoring with it? So it was a really interesting opportunity for our customers to see how other people are using serverless really in a data driven way.

Jeremy: It's interesting, because you do mention in the beginning of the report that you're saying "Serverless," but you are just focusing on FaaS. So actually, I'd love to get your thoughts on this, Darcy. Just considering what serverless is as a whole, what do you consider that to be, because it's more than FaaS.

Darcy: In terms of the things we're looking at specifically in this report, it's very focused on Lambda. I think in general, we have people who come to us asking us for solutions for things like ECS, Fargate, things like Knative or Google Cloud Run, which they're not necessarily following the pair invocation model in terms of pricing and cost structure. And they're not necessarily building containerized services directly around functions, but they're adjacent. They have some of the pieces. The ability to very quickly on-demand spin up resources and the ability to have event driven architectures, like this is something I think we see across different solutions.

Jeremy: Nice. That makes sense. I want to get into the study itself, but I do think it's important, because I know someone who's tried to run a survey in the past, that the methodology is important. We want to know who the people are that are answering these, which way they might skew based on their population and so forth. So the report actually did a great job outlining this, but just because I'd like to go through these findings, it would be great if we could just talk about that methodology for a second. Let's start with the population, so this was just Datadog customers?

Stephen: Yeah. The claims that we make in the data that we looked at is across all of these Datadog customers. We don't have data on someone who's not a Datadog customer, so for all of this, we looked at our customers' metrics, their trace data.

Jeremy: It just seems, and your customers are obviously more cloud savvy. So we're not looking at all enterprises here, just the ones that are probably much more cloud savvy, using Datadog.

Stephen: Yeah, that's correct.

Jeremy: Great. Then we also talk about Lambda adoption in here, and that's one of those tough things too where, what does it mean to adopt Lambda? Can you explain what that means?

Darcy: So Lambda adoption, we consider it to be any account in AWS, which is running more than five Lambda functions a month. That was the cutoff point where it's like maybe they have one or two people experiment around with it, after five, we considered it being regularly run. We considered that to be a company that's adopting Lambda.

Jeremy: And that ties into the AWS usage, right? In order for a company to be using AWS, they would have to be running some workloads in that cloud?

Darcy: Yeah. Our broader definition of AWS usage inclu...

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