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Three Cloud-Native Gaps That Could Really Worsen In 2022 | Matt Provo


Guest: Matt Provo (LinkedIn, Twitter)
Company: StormForge (LinkedIn, Twitter)
Show: 2022 Prediction Series

Matt Provo, Founder and CEO of StormForge, predicts three cloud-native gaps, viz. ‘Day 2’ complexity, data gap and skills gap, that are existing today but have the potential to really worsen in 2022 as more workloads move to Kubernetes. “As organizations really scale their Kubernetes deployments, not just for day one but move into production, move towards day two operations, they’re going to increasingly hit what we call the cloud-native wall, where these complexity challenges kind of become too big to handle manually,” says Provo.

He also predicts that more and more data is being collected by enterprises as they adopt Kubernetes, as they take on further monitoring tools, and this will only accelerate exponentially in 2022. Check out the above video to know more.


Swapnil Bhartiya: Hi, this is your host, Swapnil Bhartiya, and welcome to our 2022 Prediction Series. Today, we have with us, Matt Provo, Founder and CEO of StormForge. Matt, it’s great to have you on the show.

Matt Provo: Thanks for having me. Glad to be here.

Swapnil Bhartiya: Yeah, it’s a pleasure. Before I ask you to share your predictions, please tell us quickly, what is the company all about?

Matt Provo: Yeah. StormForge started in 2016 out of Boston and Washington, D.C., about 70 million in funding up to this point. We’ve essentially developed a scenario planning platform for developers and we use our patented machine learning engine to automate the processes that are associated with how you configure and manage workloads specifically running in a cloud native or Kubernetes environment. So we’ve built the platform for developers by developers. The company’s expertise and background is really in applied mathematics, but with a goal to actually productize the core algorithms for real business use case.

So as we see more and more workloads moving over into a Kubernetes environment, we really have a vision for what we’re calling intelligent and automatic Kubernetes resource management at scale, and that’s really the background of the company. We started in 2016 and have been fortunate enough to grow and have a lot of success and excited about what’s coming.

Swapnil Bhartiya: Excellent. Thanks for sharing the history and the story of the company. Now, it’s time for you to grab your crystal ball and share what predictions you have for us.

Matt Provo: Yeah, for sure. So I think that, first of all, we should address why more and more organizations are moving to Kubernetes in the first place. Really, there’s three main goals. Organizations want to accelerate their development and deployment frequency, they want to improve how they monitor and automate, and ultimately they want to reduce their IT costs. So as digital transformation takes place and more and more organizations are moving in that direction, we really see continued adoption of Kubernetes, if you look at the number of workloads moving to Kubernetes, both in pre-prod and prod.

So as this is happening, and the Kool-Aid is being drunk by a number of organizations more and more, we also see just significant challenges that are impeding progress to what I would call kind of full or broader adoption. So in 2022, I wanted to predict three what I’m calling cloud native gaps that are existing today, but have the potential to really worsen in 2022 as more workloads move to Kubernetes.

So the first prediction would be what I’m calling day two complexity as a gap. So what do I mean by that? As organizations really scale their Kubernetes deployments, not just for day one, but move into production, move towards day two operations. They’re going to increasingly hit what we call the cloud native wall, where these complexity challenges kind of become too big to handle manually.

So as workloads increase, day two operations really kind of hit mainstream, this complexity gap, which really is connected to the core value proposition of adopting Kubernetes around its flexibility kind of smacks organizations in the face. We really see this continuing as more and more workloads move and people move further into day two operations.

The second prediction that I have for 2022 is what I’m calling a data gap. So the prediction itself would be that more and more data is being collected by enterprises as they adopt Kubernetes, as they take on further monitoring tools, and this will only accelerate exponentially in 2022. Collection of data is one thing, drawing insights and taking action on that information is really kind of the holy grail of the next step. So if we think about the data that’s able to be captured in a cloud native and Kubernetes environment with observability tools, et cetera, it’s exploding.

The value that they’re getting from that information or that data, however, is not accelerating kind of at the same rate. So without the right amount of intelligence and automation in order to kind of understand what action should be taken based on the data that’s being collected, it really leaves a lot to be desired. So prediction alongside that is machine learning and automation will absolutely continue to increase in importance as organizations look to not only capture data, but really drive optimization and continuous improvement in their production environments from a cloud native standpoint.

The third prediction is what I’m calling a continued or an ever growing skills gap within a native environment. So as digital transformation continues, the same humans who were previously responsible for managing applications or environments on a pre-cloud native or pre-Kubernetes environment often are the same people responsible for managing that same environment in a Kubernetes world, just without the training and the skills necessarily to be able to understand what the world looks like in this new environment.

So this will have a big impact on how organizations kind of think about the investment that they’ve made in moving over to Kubernetes. The question will be whether organizations can innovate and effectively compete both for talent and from a training standpoint in order to really enable and empower their employees to have the right types of skills. So management tools leveraging AI and automation will increase in importance across 2022, really to help teams themselves do more in many ways with less knowledge than they were previously.

Swapnil Bhartiya: Matt, thanks for sharing these predictions. Now, tell me what is going to be the focus for the company in 2022?

Matt Provo: Yeah. For StormForge, we have a couple things we’re focused on. One is what I’d categorize as calling closing the loop. So we’ll be providing practitioners and our users really as this world of Kubernetes and cloud native expands that in many cases are on disparate teams. We’ll be giving them the opportunity and the ability to both confidently and continuously scenario plan, both in pre-prod and production. We’ll be giving them the opportunity to do that with utilizing the tools and the data that they already have, so with little to no switching costs.

We’ll also be taking our core machine learning capabilities that we’ve been developing since 2016 and we’ll be expanding those pretty significantly. So we’ll be focused on expansion of customer assets or data sources within these tools, as the tool chain itself continues to harden that we can use for our optimization itself. So going beyond load and performance testing data sources to traces and telemetry data, observability data logs, et cetera, and really expanding our core machine learning capabilities to be compatible with those additional data points.

Then we have a huge focus on integrations and partnerships. So we are both focused in strategic, but very intentionally integrating into major enterprise cloud providers, observability platforms and performance engineering suites. So we’ll be announcing a bunch of strategic partnerships and integrations across the first half of 2022. The goal for us as an organization is to continue to kind of really be great at what we’re great at, and that’s the core machine learning piece, and kind of take an Intel Inside model and approach to how we scale both our company and really support our partners strategically.

Swapnil Bhartiya: Matt, thank you so much for, of course, taking your time out today and talking about the company, your focus, yeah, and more importantly, the predictions. I would love to have you back on the show next year so that we can see, we can hold a scorecard and see how many of your predictions turn out to be true and also to get the next set of predictions. But thank you for your time today. Thank you.

Matt Provo: Thank you very much. Glad to be here.


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