(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Welcome back everyone. We're so glad that you're still with us. It's the Cube Live at the MGM Grand. This is our second day of coverage of Palo Alto Networks Ignite. This is takeaways from Ignite 22. Lisa Martin here with two really smart guys, Dave Valante. Dave, we're joined by one of our cube alumni, a friend, a friend of the, we say friend of the Cube. >>Yeah, otc. A friend of the Cube >>Karala joined us. Guys, it's great to have you here. It's been an exciting show. A lot of cybersecurity is one of my favorite topics to talk about. But I'd love to get some of the big takeaways from both of you. Dave, we'll start with you. >>A breathing room from two weeks ago. Yeah, that was, that was really pleasant. You know, I mean, I know was, yes, you sat in the analyst program, interested in what your takeaways were from there. But, you know, coming into this, we wrote a piece, Palo Alto's Gold Standard, what they need to do to, to keep that, that status. And we hear it a lot about consolidation. That's their big theme now, which is timely, right? Cause people wanna save money, they wanna do more with less. But I'm really interested in hearing zeus's thoughts on how that's playing in the market. How customers, how easy is it to just say, oh, hey, I'm gonna consolidate. I wanna get into that a little bit with you, how well the strategy's working. We're gonna get into some of the m and a activity and really bring your perspectives to the table. Well, >>It's, it's not easy. I mean, people have been calling for the consolidation of security for decades, and it's, it's, they're the first company that's actually made it happen. Right? And, and I think this is what we're seeing here is the culmination of this long term strategy, this company trying to build more of a platform. And they, you know, they, they came out as a firewall vendor. And I think it's safe to say they're more than firewall today. That's only about two thirds of their revenue now. So down from 80% a few years ago. And when I think of what Palo Alto has become, they're really a data company. Now, if you look at, you know, unit 42 in Cortex, the, the, the Cortex Data Lake, they've done an excellent job of taking telemetry from their products and from the acquisitions they have, right? And bringing that together into one big data lake. >>And then they're able to use that to, to do faster threat notification, forensics, things like that. And so I think the old model of security of create signatures for known threats, it's safe to say it never really worked and it wasn't ever gonna work. You had too many day zero exploits and things. The only way to fight security today is with a AI and ML based analytics. And they have, they're the gold standard. I think the one thing about your post that I would add the gold standard from a data standpoint, and that's given them this competitive advantage to go out and become a platform for a security. Which, like I said, the people have tried to do that for years. And the first one that's actually done it, well, >>We've heard this from some of the startups, like Lacework will say, oh, we treat security as a data problem. Of course there's a startup, Palo Alto's got, you know, whatever, 10, 15 years of, of, of history. But one of the things I wanted to explore with you coming into this was the notion of can you be best of breed and develop a suite? And we, we've been hearing a consistent answer to that question, which is, and, and do you need to, and the answer is, well, best of breed in security requires that full spectrum, that full view. So here's my question to you. So, okay, let's take Esty win relatively new for these guys, right? Yeah. Okay. And >>And one of the few products are not top two, top three in, right? Exactly. >>Yeah. So that's why I want to take that. Yeah. Because in bakeoffs, they're gonna lose on a head-to-head best of breed. And so the customer's gonna say, Hey, you know, I love your, your consolidation play, your esty win's. Just, okay, how about a little discount on that? And you know, these guys are premium priced. Yes. So, you know, are they in essentially through their pricing strategies, sort of creating that stuff, fighting that, is that friction for them where they've got, you know, the customer says, all right, well forget it, we're gonna go stove pipe with the SD WAN will consolidate some of the stuff. Are you seeing that? >>Yeah, I, I, I still think the sales model is that way. And I think that's something they need to work on changing. If they get into a situation where they have to get down into a feature battle of my SD WAN versus your SD wan, my firewall versus your firewall, frankly they've already lost, you know, because their value prop is the suite and, and is the platform. And I was talking to the CISO here that told me, he realizes now that you don't need best of breed everywhere to have best in class threat protection. In fact, best of breed everywhere leads to suboptimal threat protection. Cuz you have all these data data sets that are in silos, right? And so from a data scientist standpoint, right, there's the good data leads to good insights. Well, partial data leads to fragmented insights and that's, that's what the best, best of breed approach gives you. And so I was talking with Palo about this, can they have this vision of being best of breed and platform? I don't really think you can maintain best of breed everywhere across this portfolio this big, but you don't need to. >>That was my second point of my >>Question. That's the point. >>Yeah. And so, cuz cuz because you know, we've talked about this, that that sweets always win in the long run, >>Sweets >>Win. Yeah. But here's the thing, I, I wonder to your your point about, you know, the customer, you know, understanding that that that, that this resonates with them. I, my guess is a lot of customers, you know, at that mid-level and the fat middle are like still sort of wed, you know, hugging that, that tool. So there's, there's work to be done here, but I think they, they, they got it right Because if they devolve, to your point, if they devolve down to that speeds and feeds, eh, what's the point of that? Where's their valuable? >>You do not wanna get into a knife fight. And I, and I, and I think for them the, a big challenge now is convincing customers that the suite, the suite approach does work. And they have to be able to do that in actual customer examples. And so, you know, I I interviewed a bunch of customers here and the ones that have bought into XDR and xor and even are looking at their sim have told me that the, the, so think of soc operations, the old way heavily manually oriented, right? You have multiple panes of glass and you know, and then you've got, so there's a lot of people work before you bring the tools in, right? If done correctly with AI and ml, the machines would do all the heavy lifting and then you'd bring people in at the end to clean up the little bits that were missed, right? >>And so you, you moved to, from something that was very people heavy to something that's machine heavy and machines can work a lot faster than people. And the, and so the ones that I've talked that have, that have done that have said, look, our engineers have moved on to a lot different things. They're doing penetration testing, they're, you know, helping us with, with strategy and they're not fighting that, that daily fight of looking through log files. And the only proof point you need, Dave, is look at every big breach that we've had over the last five years. There's some SIM vendor up there that says, we caught it. Yeah. >>Yeah. We we had the data. >>Yeah. But, but, but the security team missed it. Well they missed it because you're, nobody can look at that much data manually. And so the, I I think their approach of relying heavily on machines to fight the fight is actually the right way. >>Is that a differentiator for them versus, we were talking before we went live that you and I first hit our very first segment back in 2017 at Fort Net. Is that, where do the two stand in your >>Yeah, it's funny cuz if you talk to the two vendors, they don't really see each other in a lot of accounts because Fort Net's more small market mid-market. It's the same strategy to some degree where Fort Net relies heavily on in-house development and Palo Alto relies heavily on acquisition. Yeah. And so I think from a consistently feature set, you know, Fort Net has an advantage there because it, it's all run off their, their their silicon. Where, where Palo's able to innovate very quickly. The, it it requires a lot of work right? To, to bring the front end and back ends together. But they're serving different markets. So >>Do you see that as a differentiator? The integration strategy that Palo Alto has as a differentiator? We talk to so many companies who have an a strong m and a strategy and, and execution arm. But the challenge is always integrating the technology so that the customer to, you know, ultimately it's the customer. >>I actually think they're, they're underrated as a, an acquirer. In fact, Dave wrote a post to a prior on Silicon Angle prior to Accelerate and he, he on, you put it on Twitter and you asked people to rank 'em as an acquirer and they were in the middle of the pack, >>Right? It was, it was. So it was Oracle, VMware, emc, ibm, Cisco, ServiceNow, and Palo Alto. Yeah. Or Oracle got very high marks. It was like 8.5 out of, you know, 10. Yeah. VMware I think was 6.5. Nice. Era was high emc, big range. IBM five to seven. Cisco was three to eight. Yeah. Yeah, right. ServiceNow was a seven. And then, yeah, Palo Alto was like a five. And I, which I think it was unfair. >>Well, and I think it depends on how you look at it. And I, so I think a lot of the acquisitions Palo Altos made, they've done a good job of integrating their backend data and they've almost ignored the front end. And so when you buy some of the products, it's a little clunky today. You know, if you work with Prisma Cloud, it could be a little bit cleaner. And even with, you know, the SD wan that took 'em a long time to bring CloudGenix in and stuff. But I think the approach is right. I don't, I don't necessarily believe you should integrate the front end until you've integrated the back end. >>That's >>The hard part, right? Because UL ultimately what you're gonna get, you're gonna get two panes of glass and one pane of glass and it might look pretty all mush together, but ultimately you're not solving the bigger problem, right. Of, of being able to create that big data like the, the fight security. And so I think, you know, the approach they've taken is the right one. I think from a user standpoint, maybe it doesn't show up as neatly because you don't see the frontend integration, but the way they're doing it is the right way to do it. And I'm glad they're doing it that way versus caving to the pressures of what, you know, the industry might want >>Showed up in the performance of the company. I mean, this company was basically gonna double revenues to 7 billion from 2020 to >>2023. Three. Think about that at that, that >>Make a, that's unbelievable, right? I mean, and then and they wanna double again. Yeah. You know, so, well >>What did, what did Nikesh was quoted as saying they wanna be the first cyber company that's a hundred billion dollars. He didn't give a timeline market cap. >>Right. >>Market cap, right. Do what I wanna get both of your opinions on what you saw and heard and felt this week. What do you think the likelihood is? And and do you have any projections on how, you know, how many years it's gonna take for them to get there? >>Well, >>Well I think so if they're gonna get that big, right? And, and we were talking about this pre-show, any company that's becoming a big company does it through ecosystem >>Bingo. >>Right? And that when you look around the show floor, it's not that impressive. And if that, if there's an area they need to focus on, it's building that ecosystem. And it's not with other security vendors, it's with application vendors and it's with the cloud companies and stuff. And they've got some relationships there, but they need to do more. I actually challenge 'em on that. One of the analyst sessions. They said, look, we've got 800 cortex partners. Well where are they? Right? Why isn't there a cortex stand here with a bunch of the small companies here? So I do think that that is an area they need to focus on. If they are gonna get to that, that market caps number, they will do so do so through ecosystem. Because every company that's achieved that has done it through ecosystem. >>A hundred percent agree. And you know, if you look at CrowdStrike's ecosystem, it's pretty similar. Yeah. You know, it doesn't really, you know, make much, much, not much different from this, but I went back and just looked at some, you know, peak valuations during the pandemic and shortly thereafter CrowdStrike was 70 billion. You know, that's what their roughly their peak Palo Alto was 56, fortune was 59 for the actually diverged. Right. And now Palo Alto has taken the, the top mantle, you know, today it's market cap's 52. So it's held 93% of its peak value. Everybody else is tanking. Even Okta was 45 billion. It's been crushed as you well know. But, so Palo Alto wasn't always, you know, the number one in terms of market cap. But I guess my point is, look, if CrowdStrike could got to 70 billion during Yeah. During the frenzy, I think it's gonna take, to answer your question, I think it's gonna be five years. Okay. Before they get back there. I think this market's gonna be tough for a while from a valuation standpoint. I think generally tech is gonna kind of go up and down and sideways for a good year and a half, maybe even two years could be even longer. And then I think there's gonna be some next wave of productivity innovation that that hits. And then you're gonna, you're almost always gonna exceed the previous highs. It's gonna take a while. Yeah, >>Yeah, yeah. But I think their ability to disrupt the SIM market actually is something I, I believe they're gonna do. I've been calling for the death of the sim for a long time and I know some people at Palo Alto are very cautious about saying that cuz the Splunks and the, you know, they're, they're their partners. But I, I think the, you know, it's what I said before, the, the tools are catching them, but they're, it's not in a way that's useful for the IT pro and, but I, I don't think the SIM vendors have that ecosystem of insight across network cloud endpoint. Right. Which is what you need in order to make a sim useful. >>CISO at an ETR roundtable said, if, if it weren't for my regulators, I would chuck my sim. >>Yes. >>But that's the only reason that, that this person was keeping it. So, >>Yeah. And I think the, the fact that most of those companies have moved to a perpetual MO or a a recurring revenue model actually helps unseat them. Typically when you pour a bunch of money into something, you remember the old computer associate days, nobody ever took it out cuz the sunk dollars you spent to do it. But now that you're paying an annual recurring fee, it's actually makes it easier to take out. So >>Yeah, it's it's an ebb and flow, right? Yeah. Because the maintenance costs were, you know, relatively low. Maybe it was 20% of the total. And then, you know, once every five years you had to do a refresh and you were still locked into the sort of maintenance and, and so yeah, I think you're right. The switching costs with sas, you know, in theory anyway, should be less >>Yeah. As long as you can migrate the data over. And I think they've got a pretty good handle on that. So, >>Yeah. So guys, I wanna get your perspective as a whole bunch of announcements here. We've only been here for a couple days, not a big conference as, as you can see from behind us. What Zs in your opinion was Palo Alto's main message and and what do you think about it main message at this event? And then same question for you. >>Yeah, I, I think their message largely wrapped around disruption, right? And, and they, in The's keynote already talked about that, right? And where they disrupted the firewall market by creating a NextGen firewall. In fact, if you look at all the new services they added to their firewall, you, you could almost say it's a NextGen NextGen firewall. But, but I do think the, the work they've done in the area of cloud and cortex actually I think is, is pretty impressive. And I think that's the, the SOC is ripe for disruption because it's for, for the most part, most socks still, you know, run off legacy playbooks. They run off legacy, you know, forensic models and things and they don't work. It's why we have so many breaches today. The, the dirty little secret that nobody ever wants to talk about is the bad guys are using machine learning, right? And so if you're using a signature based model, all they're do is tweak their model a little bit and it becomes, it bypasses them. So I, I think the only way to fight the the bad guys today is with you gotta fight fire with fire. And I think that's, that's the path they've, they've headed >>Down and the bad guys are hiding in plain sight, you know? >>Yeah, yeah. Well it's, it's not hard to do now with a lot of those legacy tools. So >>I think, I think for me, you know, the stat that we threw out earlier, I think yesterday at our keynote analysis was, you know, the ETR data shows that are, that are that last survey around 35% of the respondents said we are actively consolidating, sorry, 44%, sorry, 35 says we're actively consolidating vendors, redundant vendors today. That number's up to 44%. Yeah. It's by far the number one cost optimization technique. That's what these guys are pitching. And I think it's gonna resonate with people and, and I think to your point, they're integrating at the backend, their beeps are technical, right? I mean, they can deal with that complexity. Yeah. And so they don't need eye candy. Eventually they, they, they want to have that cuz it'll allow 'em to have deeper market penetration and make people more productive. But you know, that consolidation message came through loud and clear. >>Yeah. The big change in this industry too is all the new startups are all cloud native, right? They're all built on Amazon or Google or whatever. Yeah. And when your cloud native and you buy a cloud native integration is fast. It's not like having to integrate this big monolithic software stack anymore. Right. So I I think their pace of integration will only accelerate from here because everything's now cloud native. >>If a customer comes to you or when a customer comes to you and says, Zs help us with this cyber transformation we have, our board isn't necessarily with our executives in terms of execution of a security strategy. How do you advise them where Palo Alto is concerned? >>Yeah. You know, a lot, a lot of this is just fighting legacy mindset. And I've, I was talking with some CISOs here from state and local governments and things and they're, you know, they can't get more budget. They're fighting the tide. But what they did find is through the use of automation technology, they're able to bring their people costs way down. Right. And then be able to use that budget to invest in a lot of new projects. And so with that, you, you have to start with your biggest pain points, apply automation where you can, and then be able to use that budget to reinvest back in your security strategy. And it's good for the IT pros too, the security pros, my advice to, to it pros is if you're doing things today that aren't resume building, stop doing them. Right? Find a way to automate the money your job. And so if you're patching systems and you're looking through log files, there's no reason machines can't do that. And you go do something a lot more interesting. >>So true. It's like storage guys 10 years ago, provisioning loans. Yes. It's like, stop doing that. Yeah. You're gonna be outta a job. And so who, last question I have is, is who do you see as the big competitors, the horses on the track question, right? So obviously Cisco kind of service has led for a while and you know, big portfolio company, CrowdStrike coming at it from end point. You know who, who, who do you see as the real players going for that? You know, right now the market's three to 4%. The leader has three, three 4% of the market. You know who they're all going for? 10, 15, maybe 20% of the market. Who, who are the likely candidates? Yeah, >>I don't know if CrowdStrike really has the breadth of portfolio to compete long term though. I I think they've had a nice run, but I, we might start to see the follow 'em. I think Microsoft is gonna be for middle. They've laid down the gauntlet, right? They are a security vendor, right? We, we were at Reinvent and a AWS is the platform for security vendors. Yes. Middle, somewhere in the middle. But Microsoft make no mistake, they're in security. They've got some good products. I think a lot of 'em are kind of good enough and they, they tie it to the licensing and I'm not sure that works in security, but they've certainly got the ear of a lot of it pros. >>It might work in smb. >>Yeah. Yeah. It, it might. And, and I do like Zscaler. I, I know these guys poo poo the proxy model, but they've, they've done about as much with proxies as you can. And I, I think it's, it's a battle of, I love the, the, the near, you know, proxies are dead and Jay's model, you know, Jay over at c skater throw 'em back at 'em. So I, it's good to see that kind of fight going on between the two. >>Oh, it's great. Well, and, and again, ZScaler's coming at it from their cloud security angle. CrowdStrike's coming at it from endpoint. I, I do think CrowdStrike has an opportunity to build out the portfolio through m and a and maybe ecosystem. And then obviously, you know, Palo Alto's getting it done. How about Cisco? >>Yeah. Cisco's interesting. And I, I think if Cisco can make the network matter in security and it should, right? We're talking about how a lot of you need a lot of forensics to fight security today. Well, they're gonna see things long before anybody else because they have all that network data. If they can tie network security, I, I mean they could really have that business take off. But we've been saying that about Cisco for 20 years. >>But big install based though. Yeah. It's hard for a company, any company to just say, okay, hey Cisco customer sweep the floor and come with us. That's, that's >>A tough thing. They have a lot of good peace parts, right? And like duo's a good product and umbrella's a good product. They've, they've not done a good job. >>They're the opposite of these guys. >>They've not done a good job of the backend integration that, that's where Cisco needs to, to focus. And I do think g G two Patel there fixed the WebEx group and I think he's now, in fact when you talk to him, he's doing very little on WebEx that that group's running itself and he's more focused in security. So I, I think we could see a resurgence there. But you know, they have a, from a revenue perspective, it's a little misleading cuz they have this big legacy base that's in decline while they're moving to cloud and stuff. So, but they, but they, there's a lot of work there're trying to, to tie to network. >>Right. Lots of fuel for conversation. We're gonna have to carry this on, on Silicon angle.com guys. Yes. And Wikibon, lets do see us. Thank you so much for joining Dave and me giving us your insights as to this event. Where are you gonna be next? Are you gonna be on vacation? >>There's nothing more fun than mean on the cube, so, right. What's outside of that though? Yeah, you know, Christmas coming up, I gotta go see family and do the obligatory, although for me that's a lot of travel, so I guess >>More planes. Yeah. >>Hopefully not in Vegas. >>Not in Vegas. >>Awesome. Nothing against Vegas. Yeah, no, >>We love it. We >>Love it. Although I will say my year started off with ces. Yeah. And it's finishing up with Palo Alto here. The bookends. Yeah, exactly. In Vegas bookends. >>Well thanks so much for joining us. Thank you Dave. Always a pleasure to host a show with you and hear your insights. Reading your breaking analysis always kicks off my prep for show and it's always great to see, but predictions come true. So thank you for being my co-host bet. All right. For Dave Valante Enz as Carla, I'm Lisa Martin. You've been watching The Cube, the leader in live, emerging and enterprise tech coverage. Thanks for watching.

>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Welcome back everyone. We're so glad that you're still with us. It's the Cube Live at the MGM Grand. This is our second day of coverage of Palo Alto Networks Ignite. This is takeaways from Ignite 22. Lisa Martin here with two really smart guys, Dave Valante. Dave, we're joined by one of our cube alumni, a friend, a friend of the, we say friend of the Cube. >>Yeah, F otc. A friend of the Cube >>Karala joins us. Guys, it's great to have you here. It's been an exciting show. A lot of cybersecurity is one of my favorite topics to talk about. But I'd love to get some of the big takeaways from both of you. Dave, we'll start with >>You. A breathing room from two weeks ago. Yeah, that was, that was really pleasant. You know, I mean, I know was, yes, you sat in the analyst program, interested in what your takeaways were from there. But, you know, coming into this, we wrote a piece, Palo Alto's Gold Standard, what they need to do to, to keep that, that status. And we hear it a lot about consolidation. That's their big theme now, which is timely, right? Cause people wanna save money, they wanna do more with less. But I'm really interested in hearing zeus's thoughts on how that's playing in the market. How customers, how easy is it to just say, oh, hey, I'm gonna consolidate. I wanna get into that a little bit with you, how well the strategy's working. We're gonna get into some of the m and a activity and really bring your perspectives to the table. Well, >>It's, it's not easy. I mean, people have been calling for the consolidation of security for decades, and it's, it's, they're the first company that's actually made it happen. Right? And, and I think this is what we're seeing here is the culmination of this long-term strategy, this company trying to build more of a platform. And they, you know, they, they came out as a firewall vendor. And I think it's safe to say they're more than firewall today. That's only about two thirds of their revenue now. So down from 80% a few years ago. And when I think of what Palo Alto has become, they're really a data company. Now, if you look at, you know, unit 42 in Cortex, the, the, the Cortex Data Lake, they've done an excellent job of taking telemetry from their products and from the acquisitions they have, right? And bringing that together into one big data lake. >>And then they're able to use that to, to do faster threat notification, forensics, things like that. And so I think the old model of security of create signatures for known threats, it's safe to say it never really worked and it wasn't ever gonna work. You had too many days, zero exploits and things. The only way to fight security today is with a AI and ML based analytics. And they have, they're the gold standard. I think the one thing about your post that I would add, they're the gold standard from a data standpoint. And that's given them this competitive advantage to go out and become a platform for security. Which, like I said, the people have tried to do that for years. And the first one that's actually done it, well, >>We've heard this from some of the startups, like Lacework will say, oh, we treat security as a data problem. Of course there's a startup, Palo Alto's got, you know, whatever, 10, 15 years of, of, of history. But one of the things I wanted to explore with you coming into this was the notion of can you be best of breed and develop a suite? And we, we've been hearing a consistent answer to that question, which is, and, and do you need to, and the answer is, well, best of breed in security requires that full spectrum, that full view. So here's my question to you. So, okay, let's take Estee win relatively new for these guys, right? Yeah. Okay. And >>And one of the few products are not top two, top three in, right? >>Exactly. Yeah. So that's why I want to take that. Yeah. Because in bakeoffs, they're gonna lose on a head-to-head best of breed. And so the customer's gonna say, Hey, you know, I love your, your consolidation play, your esty win's. Just, okay, how about a little discount on that? And you know, these guys are premium priced. Yes. So, you know, are they in essentially through their pricing strategies, sort of creating that stuff, fighting that, is that friction for them where they've got, you know, the customer says, all right, well forget it, we're gonna go stove pipe with the SD WAN will consolidate some of the stuff. Are you seeing that? >>Yeah, I, I, I still think the sales model is that way. And I think that's something they need to work on changing. If they get into a situation where they have to get down into a feature battle of my SD WAN versus your SD wan, my firewall versus your firewall, frankly they've already lost, you know, because their value prop is the suite and, and is the platform. And I was talking with the CISO here that told me, he realizes now that you don't need best of breed everywhere to have best in class threat protection. In fact, best of breed everywhere leads to suboptimal threat protection. Cuz you have all these data data sets that are in silos, right? And so from a data scientist standpoint, right, there's the good data leads to good insights. Well, partial data leads to fragmented insights and that's, that's what the best, best of breed approach gives you. And so I was talking with Palo about this, can they have this vision of being best of breed and platform? I don't really think you can maintain best of breed everywhere across this portfolio this big, but you don't need to. >>That was my second point of my question. That's the point I'm saying. Yeah. And so, cuz cuz because you know, we've talked about this, that that sweets always win in the long run, >>Sweets win. >>Yeah. But here's the thing, I, I wonder to your your point about, you know, the customer, you know, understanding that that that, that this resonates with them. I, my guess is a lot of customers, you know, at that mid-level and the fat middle are like still sort of wed, you know, hugging that, that tool. So there's, there's work to be done here, but I think they, they, they got it right Because if they devolve, to your point, if they devolve down to that speeds and feeds, eh, what's the point of that? Where's their >>Valuable? You do not wanna get into a knife fight. And I, and I, and I think for them the, a big challenge now is convincing customers that the suite, the suite approach does work. And they have to be able to do that in actual customer examples. And so, you know, I I interviewed a bunch of customers here and the ones that have bought into XDR and xor and even are looking at their sim have told me that the, the, so think of soc operations, the old way heavily manually oriented, right? You have multiple panes of glass and you know, and then you've got, so there's a lot of people work before you bring the tools in, right? If done correctly with AI and ml, the machines would do all the heavy lifting and then you'd bring people in at the end to clean up the little bits that were missed, right? >>And so you, you moved to, from something that was very people heavy to something that's machine heavy and machines can work a lot faster than people. And the, and so the ones that I've talked that have, that have done that have said, look, our engineers have moved on to a lot different things. They're doing penetration testing, they're, you know, helping us with, with strategy and they're not fighting that, that daily fight of looking through log files. And the only proof point you need, Dave, is look at every big breach that we've had over the last five years. There's some SIM vendor up there that says, we caught it. Yeah. >>Yeah. We we had the data. >>Yeah. But, but, but the security team missed it. Well they missed it because you're, nobody can look at that much data manually. And so the, I I think their approach of relying heavily on machines to fight the fight is actually the right way. >>Is that a differentiator for them versus, we were talking before we went live that you and I first hit our very first segment back in 2017 at Fort Net. Is that, where do the two stand in your >>Yeah, it's funny cuz if you talk to the two vendors, they don't really see each other in a lot of accounts because Fort Net's more small market mid-market. It's the same strategy to some degree where Fort Net relies heavily on in-house development in Palo Alto relies heavily on acquisition. Yeah. And so I think from a consistently feature set, you know, Fort Net has an advantage there because it, it's all run off their, their their silicon. Where, where Palo's able to innovate very quickly. The, it it requires a lot of work right? To, to bring the front end and back ends together. But they're serving different markets. So >>Do you see that as a differentiator? The integration strategy that Palo Alto has as a differentiator? We talk to so many companies who have an a strong m and a strategy and, and execution arm. But the challenge is always integrating the technology so that the customer to, you know, ultimately it's the customer. >>I actually think they're, they're underrated as a, an acquirer. In fact, Dave wrote a post to a prior on Silicon Angle prior to Accelerate and he, he on, you put it on Twitter and you asked people to rank 'em as an acquirer and they were in the middle of the pack, >>Right? It was, it was. So it was Oracle, VMware, emc, ibm, Cisco, ServiceNow, and Palo Alto. Yeah. Or Oracle got very high marks. It was like 8.5 out of, you know, 10. Yeah. VMware I think was 6.5. Naira was high emc, big range. IBM five to seven. Cisco was three to eight. Yeah. Yeah, right. ServiceNow was a seven. And then, yeah, Palo Alto was like a five. And I, which I think it was unfair. Well, >>And I think it depends on how you look at it. And I, so I think a lot of the acquisitions Palo Alto's made, they've done a good job of integrating the backend data and they've almost ignored the front end. And so when you buy some of the products, it's a little clunky today. You know, if you work with Prisma Cloud, it could be a little bit cleaner. And even with, you know, the SD wan that took 'em a long time to bring CloudGenix in and stuff. But I think the approach is right. I don't, I don't necessarily believe you should integrate the front end until you've integrated the back end. >>That's >>The hard part, right? Because UL ultimately what you're gonna get, you're gonna get two panes of glass and one pane of glass and it might look pretty and all mush together, but ultimately you're not solving the bigger problem, right. Of, of being able to create that big data lake to, to fight security. And so I think, you know, the approach they've taken is the right one. I think from a user standpoint, maybe it doesn't show up as neatly because you don't see the frontend integration, but the way they're doing it is the right way to do it. And I'm glad they're doing it that way versus caving to the pressures of what, you know, the industry might want or >>Showed up in the performance of the company. I mean, this company was basically gonna double revenues to 7 billion from 2020 to >>2023. Think about that at that. That makes, >>I mean that's unbelievable, right? I mean, and then and they wanna double again. Yeah. You know, so, well >>What did, what did Nikesh was quoted as saying they wanna be the first cyber company that's a hundred billion dollars. He didn't give a timeline market >>Cap. Right. >>Market cap, right. Do what I wanna get both of your opinions on what you saw and heard and felt this week. What do you think the likelihood is? And and do you have any projections on how, you know, how many years it's gonna take for them to get there? >>Well, >>Well I think so if they're gonna get that big, right? And, and we were talking about this pre-show, any company that's becoming a big company does it through ecosystem >>Bingo >>Go, right? And that when you look around the show floor, it's not that impressive. No. And if that, if there's an area they need to focus on, it's building that ecosystem. And it's not with other security vendors, it's with application vendors and it's with the cloud companies and stuff. And they've got some relationships there, but they need to do more. I actually challenge 'em on that. One of the analyst sessions. They said, look, we've got 800 cortex partners. Well where are they? Right? Why isn't there a cortex stand here with a bunch of the small companies here? So I do think that that is an area they need to focus on. If they are gonna get to that, that market caps number, they will do so do so through ecosystem. Because every company that's achieved that has done it through ecosystem. >>A hundred percent agree. And you know, if you look at CrowdStrike's ecosystem, it's, I mean, pretty similar. Yeah. You know, it doesn't really, you know, make much, much, not much different from this, but I went back and just looked at some, you know, peak valuations during the pandemic and shortly thereafter CrowdStrike was 70 billion. You know, that's what their roughly their peak Palo Alto was 56, fortune was 59 for the actually diverged. Right. And now Palo Alto has taken the, the top mantle, you know, today it's market cap's 52. So it's held 93% of its peak value. Everybody else is tanking. Even Okta was 45 billion. It's been crushed as you well know. But, so Palo Alto wasn't always, you know, the number one in terms of market cap. But I guess my point is, look, if CrowdStrike could got to 70 billion during Yeah. During the frenzy, I think it's gonna take, to answer your question, I think it's gonna be five years. Okay. Before they get back there. I think this market's gonna be tough for a while from a valuation standpoint. I think generally tech is gonna kind of go up and down and sideways for a good year and a half, maybe even two years could be even longer. And then I think there's gonna be some next wave of productivity innovation that that hits. And then you're gonna, you're almost always gonna exceed the previous highs. It's gonna take a while. Yeah. >>Yeah, yeah. But I think their ability to disrupt the SIM market actually is something that I, I believe they're gonna do. I've been calling for the death of the sim for a long time and I know some people of Palo Alto are very cautious about saying that cuz the Splunks and the, you know, they're, they're their partners. But I, I think the, you know, it's what I said before, the, the tools are catching them, but they're, it's not in a way that's useful for the IT pro and, but I, I don't think the SIM vendors have that ecosystem of insight across network cloud endpoint. Right. Which is what you need in order to make a sim useful. >>CISO at an ETR round table said, if, if it weren't for my regulators, I would chuck my sim. >>Yes. >>But that's the only reason that, that this person was keeping it. No. >>Yeah. And I think the, the fact that most of those companies have moved to a perpetual MO or a a recurring revenue model actually helps unseat them. Typically when you pour a bunch of money into something, you remember the old computer associate says nobody ever took it out cuz the sunk dollars you spent to do it. But now that you're paying an annual recurring fee, it's actually makes it easier to take out. So >>Yeah, it's just an ebb and flow, right? Yeah. Because the maintenance costs were, you know, relatively low. Maybe it was 20% of the total. And then, you know, once every five years you had to do a refresh and you were still locked into the sort of maintenance and, and so yeah, I think you're right. The switching costs with sas, you know, in theory anyway, should be less >>Yeah. As long as you can migrate the data over. And I think they've got a pretty good handle on that. So, >>Yeah. So guys, I wanna get your perspective as a whole bunch of announcements here. We've only been here for a couple days, not a big conference as, as you can see from behind us. What Zs in your opinion was Palo Alto's main message and and what do you think about it main message at this event? And then same question for you. >>Yeah, I, I think their message largely wrapped around disruption, right? And, and they, and The's keynote already talked about that, right? And where they disrupted the firewall market by creating a NextGen firewall. In fact, if you look at all the new services they added to their firewall, you, you could almost say it's a NextGen NextGen firewall. But, but I do think the, the work they've done in the area of cloud and cortex actually I think is, is pretty impressive. And I think that's the, the SOC is ripe for disruption because it's for, for the most part, most socks still, you know, run off legacy playbooks. They run off legacy, you know, forensic models and things and they don't work. It's why we have so many breaches today. The, the dirty little secret that nobody ever wants to talk about is the bad guys are using machine learning, right? And so if you're using a signature based model, all they gotta do is tweak their model a little bit and it becomes, it bypasses them. So I, I think the only way to fight the the bad guys today is with you're gonna fight fire with fire. And I think that's, that's the path they've, they've headed >>Down. Yeah. The bad guys are hiding in plain sight, you know? Yeah, >>Yeah. Well it's, it's not hard to do now with a lot of those legacy tools. So >>I think, I think for me, you know, the stat that we threw out earlier, I think yesterday at our keynote analysis was, you know, the ETR data shows that are, that are that last survey around 35% of the respondents said we are actively consolidating, sorry, 44%, sorry, 35 says who are actively consolidating vendors, redundant vendors today that number's up to 44%. Yeah. It's by far the number one cost optimization technique. That's what these guys are pitching. And I think it's gonna resonate with people and, and I think to your point, they're integrating at the backend, their beeps are technical, right? I mean, they can deal with that complexity. Yeah. And so they don't need eye candy. Eventually they, they, they want to have that cuz it'll allow 'em to have deeper market penetration and make people more productive. But you know, that consolidation message came through loud and clear. >>Yeah. The big change in this industry too is all the new startups are all cloud native, right? They're all built on Amazon or Google or whatever. Yeah. And when your cloud native and you buy a cloud native integration is fast. It's not like having to integrate this big monolithic software stack anymore. Right. So I, I think their pace of integration will only accelerate from here because everything's now cloud native. >>If a customer comes to you or when a customer comes to you and says, Zs help us with this cyber transformation we have, our board isn't necessarily aligned with our executives in terms of execution of a security strategy. How do you advise them where Palo Alto is concerned? >>Yeah. You know, a lot, a lot of this is just fighting legacy mindset. And I've, I was talking with some CISOs here from state and local governments and things and they're, you know, they can't get more budget. They're fighting the tide. But what they did find is through the use of automation technology, they're able to bring their people costs way down. Right. And then be able to use that budget to invest in a lot of new projects. And so with that, you, you have to start with your biggest pain points, apply automation where you can, and then be able to use that budget to reinvest back in your security strategy. And it's good for the IT pros too, the security pros, my advice to the IT pros is, is if you're doing things today that aren't resume building, stop doing them. Right. Find a way to automate the money your job. And so if you're patching systems and you're looking through log files, there's no reason machines can't do that. And you go do something a lot more interesting. >>So true. It's like storage guys 10 years ago, provisioning loans. Yes. It's like, stop doing that. Yeah. You're gonna be outta a job. So who, last question I have is, is who do you see as the big competitors, the horses on the track question, right? So obviously Cisco kind of service has led for a while and you know, big portfolio company, CrowdStrike coming at it from end point. You know who, who, who do you see as the real players going for that? You know, right now the market's three to 4%. The leader has three, three 4% of the market. You know who they're all going for? 10, 15, maybe 20% of the market. Who, who are the likely candidates? Yeah, >>I don't know if CrowdStrike really has the breadth of portfolio to compete long term though. I I think they've had a nice run, but I, we might start to see the follow 'em. I think Microsoft is gonna be for middle. They've laid down the gauntlet, right? They are a security vendor, right? We, we were at Reinvent and a AWS is the platform for security vendors. Yes. Middle, somewhere in the middle. But Microsoft make no mistake, they're in security. They've got some good products. I think a lot of 'em are kind of good enough and they, they tie it to the licensing and I'm not sure that works in security, but they've certainly got the ear of a lot of it pros. >>It might work in smb. >>Yeah, yeah. It, it might. And, and I do like Zscaler. I, I know these guys poo poo the proxy model, but they've, they've done about as much with prox as you can. And I, I think it's, it's a battle of, I love the, the, the near, you know, proxies are dead and Jay's model, you know, Jay over at csca, throw 'em back at 'em. So I, it's good to see that kind of fight going on between the >>Two. Oh, it's great. Well, and, and again, ZScaler's coming at it from their cloud security angle. CrowdStrike's coming at it from endpoint. I, I do think CrowdStrike has an opportunity to build out the portfolio through m and a and maybe ecosystem. And then obviously, you know, Palo Alto's getting it done. How about Cisco? >>Yeah, Cisco's interesting. And I I think if Cisco can make the network matter in security and it should, right? We're talking about how a lot of you need a lot of forensics to fight security today. Well, they're gonna see things long before anybody else because they have all that network data. If they can tie network security, I, I mean they could really have that business take off. But we've been saying that about Cisco for 20 years. >>But big install based though. Yeah. It's hard for a company, any company to say, okay, hey Cisco customer sweep the floor and come with us. That's, that's >>A tough thing. They have a lot of good peace parts, right? And like duo's a good product and umbrella's a good product. They've, they've not done a good job. >>They're the opposite of these guys. >>They've not done a good job of the backend integration and that, that's where Cisco needs to, to focus. And I do think g G two Patel there fixed the WebEx group and I think he's now, in fact when you talk to him, he's doing very little on WebEx that that group's running itself and he's more focused in security. So I, I think we could see a resurgence there. But you know, they have a, from a revenue perspective, it's a little misleading cuz they have this big legacy base that's in decline while they're moving to cloud and stuff. So, but they, but they, there's a lot of Rick there trying to, to tie to network. >>Lots of fuel for conversation. We're gonna have to carry this on, on Silicon angle.com guys. Yes. And Wi KeePon. Lets do see us. Thank you so much for joining Dave and me giving us your insights as to this event. Where are gonna be next? Are you gonna be on >>Vacation? There's nothing more fun than mean on the cube. So what's outside of that though? Yeah, you know, Christmas coming up, I gotta go see family and be the obligatory, although for me that's a lot of travel, so I guess >>More planes. Yeah. >>Hopefully not in Vegas. >>Not in Vegas. >>Awesome. Nothing against Vegas. Yeah, no, >>We love it. We love >>It. Although I will say my year started off with ces. Yeah. And it's finishing up with Palo Alto here. The bookends. Yeah, exactly. In Vegas bookends. >>Well thanks so much for joining us. Thank you Dave. Always a pleasure to host a show with you and hear your insights. Reading your breaking analysis always kicks off my prep for show. And it, it's always great to see, but predictions come true. So thank you for being my co-host bet. All right. For Dave Valante Enz as Carla, I'm Lisa Martin. You've been watching The Cube, the leader in live, emerging and enterprise tech coverage. Thanks for watching.

>> Narrator: theCUBE presents Ignite 22. Brought to you by Palo Alto Networks. >> Hey, welcome back to Las Vegas. Lisa Martin here with Dave Vellante. This is day two of theCUBE's coverage of Palo Alto Ignite 2022. Dave we're just talking about how many times we're in Vegas. And we were here two weeks ago with our guest who's back in Alumni. And it's a blur, right? >> It's true, I lost count. Luckily I'm not flying red eye tonight. So that's good. >> I'm impressed. >> Excited about that. >> Yeah >> I'm actually going to enjoy the, nightlife here for a period of time. And, you know, we were at re-Invent. >> Yeah. >> And what a difference. This is nice and relaxed. You have time. You're not getting bumped in the hallway. >> Right. >> A lot of time for learning. So it's been great show. >> It's been great. And one of the things that we've been talking about is the supply chain. Securing the modern software supply chain is really complicated. We've got an Alumni back with us, to talk about what Palo Alto is doing in that respect. Ankur Shah joins us. The SVP and GM of Cloud Security at Palo Alto Networks. Welcome back. >> Yeah, happy to be back. Good to see you again. Dave and Lisa. >> It's been two long weeks. >> Ankur: I know. It's been two weeks, yeah >> Dave: It's kind of crazy. I mean, ReInvent really was a blur. And it's like you had everything coming at you. And there was obviously a big chunk of security, but you. It was just so much to absorb. >> Yeah. >> Right? >> Yeah, and I couldn't get into any of the sessions versus at Ignite. I mean, you could, you could learn a lot. To your point Dave. And 70,000 people versus 3000 in change. Big difference. >> Dave: Yeah. >> Lisa: Huge difference. >> Yeah. >> Lisa: Huge difference. So we touched on the Cider acquisition. >> Ankur: Yeah. >> Which was announced the intent to acquire last month. Let's dig into a little bit more of that, and then some of the great things that had been announced. >> Ankur: Yeah. >> In the last couple of days. >> Oh, absolutely. So, this is something that we have been marinating for last nine months. Thinking about how best to secure supply chain. And this is software supply chain. The modern application software is fairly complex. You know, back in the days when I was a developer, it was a simple three tier application. Ship the code once a year, et cetera. But now with microservices, new architectures, Kubernetes Public Cloud, we talked about this. It's getting super complicated, and the customers are really worried about securing their entire supply chain. Which is nothing but the software pipeline. And so we started looking at a whole bunch of companies and Cider really stood out. I mean, they had, they were the innovators in this space. Very early days, we've seen supply chain attack. But there hasn't been a really good and strong solution in that space. And Cider just delivered that incredible team. Great technology, super excited about what that integration will look like. in the coming quarters. >> What do we need to know about them? I mean, I'll be honest with you, I wasn't familiar with Cider until I saw you guys made the announcement of the intent to acquire them. What, what should we know about them? Why Cider? What was it that attracted you to them? >> Ankur: Yeah, so, you know, we have a history of technology acquisitions as you know, over the last four years, just in the public cloud. We acquire over half a a dozen companies, small and large. And typically we are always looking for companies who have the next gen technology available. Technology that is more in tune with how application software is going to look like in future. So we're not always going after companies that are making you know, tens of hundreds of millions of dollars in a year and all. We're looking for the right tech. The future. And that's what we found in Cider. Like they have a really strong application security background. And AppSec just broadly speaking, supply chain is part of it. But application security, just broadly speaking, is right for disruption. You've got a lot of vendors, who have been around for like last two decades. Old school stuff, lots and lots of false positives. So we've been bolstering, beefing up our portfolio in the application security space. And Cider really fits right nicely into it. Because it can like I said, secure a lot of technology and tooling, that software developers use as part of their software supply chain. So, great founding team, great technology. It was a perfect fit. >> Talk about integration. We spoke with Nikesh yesterday, with Nir, with a whole bunch of folks. Lee this morning. BJ yesterday as well. And one of the things that seems to stick out at me. With all the shows that we do, is the focus that Palo Alto has on ensuring that it's making the right acquisitions. But that it's the integration, is really seems to be like leading part of the strategy. That seems to be a little bit of a differentiator to me. >> Yeah, it absolutely is. There are two ways to integrate a technology into an existing platform. And Prisma Cloud is a platform as you know. Code-to-cloud, CNAPP platform as we call it. One is just kind of slotted in, put the whole thing in a box. And that's basically making one plus one equal to two. We're looking for high leverage in integrations, whereby once that integration comes along. It makes the rest of the platform even better and superior. It makes that technology look even better. So that's why there's a lot of focus on ensuring that we're delivering the right type of integration, that delivers instant customer value. And that makes the overall platform even superior. So customers don't feel like hey, like there's just one more add-on, on top of the other thing. >> Lisa: Right, not a bolt on. >> So that's why there's a lot of focus on that. Getting the strategy nailed. Because the founding teams generally have a preconceived notion about how the world looks like. Then they understand how Prisma cloud and Palo Alto Networks think about it. And then, we sort of merge the two ideas, and build something that's incredible. So I am, we're spending a lot of time in integration. That honeymoon phase of like, let's high five acquisitions done, that's over. Now it's the grinding work of actually getting this right. And you know, getting hundreds and thousands of customers. >> Well I like how you don't have the private equity mentality. It's not about EBITDA and cashflow. We'll take care of that. >> Ankur: Yeah. >> You know, it's about getting that integration. Getting that flywheel effect, inside the platform. You know, we said one plus one equals, maybe even more than two. Can you explain Prisma Cloud Secrets Security? What is that all about? What do we need to know about that? >> Ankur: Absolutely. So, the developers, you know generally store some stuff in the code repo for their automation work to build application. And that thing, the API keys or as Secrets are stored in code repo. It shouldn't be. Or even if they are, they should be encrypted, or locked down and things of that nature. But, you know, the need for speed trumps everything else. Developers want to go fast. And sometimes they're like, okay well. I guess my application needs this particular, you know API access token or secret. I'm just going to stick it in the code. Now the challenge with that is that, if somebody gets hold of your code repo. Now not only is your code repo, which has all your sensitive data. Your code is the life and blood of a technology company. That's in trouble. But also those secrets and API access keys can be used to log into your cloud accounts. And there you may have sensitive customer data. Everything that you have as a technology company stored in that public cloud accounts. So that's the worry. It's usually the initial access for the kill chain. Because that's where the attacks start. Let me get the secret, let me get the API access key. And let me see what I can do in public cloud. So we are now giving customers the visibility into where the secrets are stored. More importantly, it just right there on developer's face. In the code repo as they're checking in the code. They say why, hey, there's a secret here. Are you sure you want to, you want to keep it like this, no? Okay, well then you can either encrypt it, or just get rid of it. So we're making, we're bringing security where the developers are in their code repo, et cetera. >> So I can see a lot of developers saying, yeah, go ahead, encrypt it. So I don't have to do anything else, you know, extra. It's almost, the analogy is a very small you know, version of this. Its like, use a password manager. You store all your passwords in your contacts on your phone, right? I mean, somebody gets a hold of your contacts, you're screwed. >> Ankur: That's exactly right. >> And so, but I could still see a lot of developers say, check in the box. Say, yeah just encrypt it, leave it there. But you're saying best practice is to not to do that, right? >> Yeah, usually you're not supposed to, you know, store all your secrets, et cetera in code repo to begin with. But if you do, you know, you use a key wall like technology to really encrypt it and store it in a secret manner, yeah. >> Dave: There's an old saying, bad user behavior trump's great security every time. >> Ankur: Every time. >> But this is an example where, we know you're going to have bad behavior. So we're going to protect the bad behavior. >> Yeah, and actually, sorry Lisa, just to that point. The bad user behavior trumps good security. The classic example, this happened three weeks ago. Three, four weeks ago, where Dropbox, one of the file sharing companies there. 120 plus code repos were exposed. And the way their attack started, was a simple social engineering attack. Bad user behavior. There was an email, hey, like your passwords are updated for your, you know, this code plugin. Can you enter the password? And boom, now you have access to the code repo. And now if you have secrets inside of it, now, you know all bets are off. >> Are there hard-coded secrets versus like, I mean, like I think like, like you were saying, Dave. Like usernames and passwords and tokens, versus like soft coded secrets. >> Ankur: It's, I think it, this is more so two forms of it, you know. The most primary one is what we call the API access keys. And this keys are used to access cloud accounts, workloads and things of that nature. But there are actually secret secrets. Could be database login passwords, et cetera. The application is using it to spin up databases. Now, you know, you have access to the data stores. Any other application, there's a login password, all of that stuff. So it's less about the user password, but more the application and databases and things of that nature. >> Dave: So again, and, again, everybody should be using password managers. But when you use a password manager, it's going to give you a long list of passwords, that are either been compromised or are weak. And you just go uh, okay. So can you help? How do you help customers identify what the high risk? You know, API, you know, access are versus those ones that they may not have to worry about. >> Ankur: Yeah, look. You know, secrets aside. Risk prioritization is one of the biggest topics that our customers have across the board, in cloud security. All the security vendors are really, really good at one thing, generating alerts. Everybody does it. They generate an alert. You know, your ring camera, if you've got one. I mean this pop up every day, like every minute rather. Well like can you prioritize it for me? What should I really look at it? So that's a number one thing. What Prisma Cloud does is, you know, contextualize it. What the real risk is? They can tell you like, hey, here's the kill chain. If this thing, you know, goes to public internet. These are the potential exposures that you have. So we provide a prioritized risk of critical alerts that customers have to take care of before they can start taking care of more hygiene type of stuff, right? So that's how we do it. Like we leverage a lot of technology. We apply a lot of context. We tell you like, hey, this code repo is not protected by multifactor authentication. And then there's a secret inside. Are you sure, you know, you don't want to fix it? So that's what we do. But it's a great question. Top of mind for all our customers. And that's how we think about it across the board. Versus generating just alerts all the time. >> Dave: Is the strategy, Because we all know phishing is the sort of most, you know obvious way to. It's the top way in which people get hacked. >> Ankur: Yeah. >> Is your strategy essentially to say. Okay we know that's going to happen, so we're going to try to protect it at the back end. How much of the, maybe it's an industry question. more so than just a Palo Alto specifically, How much emphasis is do you think the industry is taking or should be taking on stopping that, you know that those phishing attacks? Because if that's the number one problem you know, maybe that's where we should be starting. >> Yeah, it's a great question. It's typically the initial vector, for a lot of attacks to your point. But there is one thing that technology and AI cannot solve. Which is the user behavior, to your point. Like we can't get into the heads of the user. I mean, you can train them, you can do everything. You can't prevent somebody from clicking a button. Of course there's technology out there for email security that does that. But your point is, right, it's going to happen. Now what do you do? How do you protect your applications, your crown jewel? You know, whether it's in the cloud or it's in the code repo. So a lot of what we are trying to do in code security, or cloud security, or in general at Palo Alto Networks. is to protect those crown jewel. Because we can't prevent somebody from doing something. User behavior is hard to change. >> Dave: So it's almost like, okay, you left your front door open. Somebody's going to walk in, but oh, they walk into a vault. And they don't know where to go. And there's nowhere they can- >> Ankur: Yeah. >> You know, nothing they can take. They can't get to the silverware or the jewelry. >> I think that's it, yeah. >> What are some of the things, like as we look at, we're wrapping up calendar year '22 heading into '23. That customers can look to Palo Alto Networks to help them achieve? One of the things that we talked about with Nikesh and Niri yesterday, is consolidation. Like, and you guys just did a recent, survey. >> Ankur: Yeah. >> About the state of Cyber, and organizations on average have 366 apps in their environment. 31 security tools, 30 to 50 security tools. >> Ankur: Yeah. >> Consolidation is really key there. What are some of the things that you are excited about to deliver to customers where consolidation is concerned? >> Ankur: Yeah. >> Where software supply chain security is concerned in the next year? >> Yeah, absolutely. Look, there are over 3000 security vendors. And this can be, I mean you talked about average customer having 300. I was talking to a CSO, this was last year for one of the largest financial institution I go, "How many security tools do you have?" He got 120. I said, why? He goes, we have a no vendor left behind policy. >> Wow. >> It's crazy. >> Dave: What? >> Obviously he was joking, but it's crazy, right? Like that's how the CSO's are. >> Dave: I mean, he was kidding. >> Yeah. >> Dave: But recognized that. Wow. >> Yeah, and, this is the state the security industry is in. And our mission has been, and Lee and Nikesh and Niri talked about it. Is just platforms, will platforms take moonshots, things long term. And especially the, macro headwinds that we're seeing. We're hearing more and more from the customers that, look we're not going to buy point product. Then we got to buy another product that stitches it all together. We need platforms, whether it's for zero trust, Prisma SaaS, whether it's cloud. Prisma cloud or for your sock transformation. You know XIM and Cortex line of products. So I think you're going to see more and more of that in 2023. I'm confident in that. >> We heard from Lee today, the world record's 400. >> Yes. >> Yeah. >> That's crazy. >> He's going for it. He's got a ways to go. 120 He's got to... >> Maybe he wasn't, that guy wasn't kidding about his no vendor left behind policy. (laughing) Do you have Ankur, a favorite customer story that really articulates the value of what Palo Alto delivers and continues to. You know, 'cause one of the things that Nikesh said in his keynote was that you know, security's a data problem. Well every company these days, in every industry has to be a data company. But really what they need to be able to be is a secured data company. >> Ankur: Yeah. >> How are you guys enabling that? >> Oh, absolutely. Look, many customer examples come to mind, but speaking of data. You know, one of, some of our largest customers who are protecting their PCI workers where they have sensitive data. They're using for example, Prisma Cloud, to ensure that malicious attacks don't happen. And those workloads are used for credit card processing. They're processing tens of thousands of credit card transactions a second. And make sure that nobody gets hold of that. And that's why they have to make sure that nobody is. No attacker is trying to get hold of the sensitive data, to your point, So we have customers across financial services, media and entertainment technology company. Where we are helping them go as fast as possible in public cloud. Go through digital transformation, by securing their applications. >> Dave: What's the T-shirt say? I see code. >> Oh yeah. >> Dave: Secure from Code to Cloud. >> Lisa: Shift Happens. >> Shift Happens, Secrets from Code to Cloud. >> I love that. I was looking at that, going back to that, what's next in cyber survey? >> Ankur: Yeah. >> It said 74% of respondents, and I believe there was 1300 CIO's, CXO's that were surveyed globally. Where they said security is slowing down DevOps. Can customers look to Palo Alto Networks to help them? >> Ankur: Be enablers? >> Yes. >> Yeah, hundred percent. Look, the conversation over the last few years have changed now. Security used to say like, oh, I don't know about these people who are building applications. The DevOps is like security slowing down. I think there's an opportunity for companies like Palo Alto Networks, to build the bridge between the two. And the way we do it is make the securities easy, simple and not super intrusive. Where developers have to do a natural thing. And one part of it, and I talked about it earlier, is bring security where the developers are. In their code repo, in their IDE. Make it super simple. Don't make them do unnatural things. And it just, this is no different from changing the behavior of our kids. Right? Like you make them do unnatural things, they're not going to do it. But if it is part of their regular, you know, day-to-day operating procedures. I think they're going to be more open to change. Yeah. So I think it's possible. And Palo Alto has a huge responsibility to bridge the divide between the apps team, or the DevOps and the security organization. >> Lisa: Lots of great stuff to come. We thank you so much for coming back, two weeks. Only being on two weeks ago. We appreciate your insights, learning more information. It's great to see you at Palo Alto Ignite. And we'll have to have you back on. 'Cause we know that there's so much more to follow with respect to what you're doing. And shifting left, shift happens. >> Awesome. Lisa, Dave, thank you so much. It's been a pleasure. >> Lisa: Thank you so much. For Ankur Shah and Dave Vellante. I'm Lisa Martin. You're watching theCUBE. The leader in live and emerging tech coverage.

>> Announcer: TheCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Welcome back to Las Vegas. It's theCUBE covering Palo Alto Networks '22, from the MGM Grand, Lisa Martin with Dave Vellante. Dave, we are going to unpack in the next few minutes what we heard and saw at day one of Palo Alto Networks, Ignite. A lot of great conversations, some great guests on the program today. >> Yeah last event, CUBE event of the year. Probably last major tech event of the year. It's kind of an interesting choice of timing, two weeks after reInvent. But you know, this crowd is it's a lot of like network engineers, SecOps pros. There's not a lot of suits here. I think they were here yesterday, all the partners. >> Yeah. >> We talked to Carl Sunderland about, Hey, these, these guys want to know how do I grow my business? You know, so it was a lot of C level executives talking about their business, and how they partner with Palo Alto to grow. The crowd today is really, you know hardcore security professionals. >> Yeah. >> So we're hearing a story of consolidation. >> Yes. >> No surprise. We've talked about that and reported on it, you know, quite extensively. The one big takeaway, and I want, I came in, as you know, wanting to understand, okay, can you through m and a maintain, you know, build a suite of great, big portfolio and at the same time maintain best of breed? And the answer was consistent. We heard it from Nikesh, we heard it from Nir Zuk. The answer was you can't be best of breed without having that large portfolio, single data lake, you know? Single version of the truth, of there is such a thing. That was interesting, that in security, you have to have that visibility. I would imagine, that's true for a lot of things. Data, see what Snowflake and Databricks are both trying to do, now AWS. So to join, we heard that last week, so that was one of the big takeaways. What were your, some of your thoughts? >> Just impressed with the level of threat intelligence that Unit 42 has done. I mean, we had Wendy Whitmer on, and she was one of the alumni, great guest. The landscape has changed so dramatically. Every business, in any industry, nobody's safe. They have such great intelligence on what's going on with malware, with ransomware, with Smishing, that they're able to get, help organizations on their way to becoming cyber resilient. You know, we've been talking a lot about cyber resiliency lately. I always want to understand, well what does it mean? How do different organizations and customers define it? Can they actually really get there? And Wendy talked about yes, it is a journey, but organizations can achieve cyber resiliency. But they need to partner with Palo Alto Networks to be able to understand the landscape and ensure that they've got security established across their organization, as it's now growingly Multicloud. >> Yeah, she's a blonde-haired Wonder Woman, superhero. I always ask security pros that question. But you know, when you talk to people like Wendy Whitmore, Kevin Mandy is somebody else. And the people at AWS, or the big cloud companies, who are on the inside, looking at the threat intelligence. They have so much data, and they have so much knowledge. They can, they analyze, they could identify the fingerprints of nation states, different, you know, criminal organizations. And the the one thing, I think it was Wendy who said, maybe it was somebody else, I think it was Wendy, that they're they're tearing down and reforming, right? >> Yes. >> After they're discovered. Okay, they pack up and leave. They're like, you know, Oceans 11. >> Yep. >> Okay. And then they recruit them and bring them back in. So that was really fascinating. Nir Zuk, we'd never had him on theCUBE before. He was tremendous founder and and CTO of Palo Alto Networks, very opinionated. You know, very clear thinker, basically saying, look you're SOC is going to be run by AI >> Yeah. >> within the next five years. And machines are going to do things that humans can't do at scale, is really what he was saying. And then they're going to get better at that, and they're going to do other things that you have done well that they haven't done well, and then they're going to do well. And so, this is an interesting discussion about you know, I remember, you know we had an event with MIT. Eric Brynjolfsson and Andy McAfee, they wrote the book "Second Machine Age." And they made the point, machines have always replaced humans. This is the first time ever that machines are replacing humans in cognitive functions. So what does that mean? That means that humans have to rely on, you know, creativity. There's got to be new training, new thinking. So it's not like you're going to be out of a job, you're just going to be doing a different job. >> Right. I thought Nir Zuk did a great job of explaining that. We often hear people that are concerned with machines taking jobs. He did a great job of, and you did a great recap, of articulating the value that both bring, and the opportunities to the humans that the machines actually deliver as well. >> Yeah so, you know, we didn't, we didn't get deep into the products today. Tomorrow we're going to have a little bit more deep dive on products. We did, we had some partners on, AWS came on, talked about their ecosystem. BJ Jenkins so, you know, BJ Jenkins again I mean super senior executive. And if I were Nikesh, he's doing exactly what I would do. Putting him on a plane and saying, go meet with customers, go make rain, right? And that's what he's doing is, he's an individual who really knows how to interact with the C-suite, has driven value, you know, over the years. So they've got that angle goin', they're driving go to market. They've got the technology piece and they've, they got to build out the ecosystem. That I think is the big opportunity for them. You know, if they're going to double as a company, this ecosystem has to quadruple. >> Yeah, yeah. >> In my opinion. And I, we saw the same thing at CrowdStrike. We said the same thing about Service Now in 2013. And so, what's happened is the GSIs, the global system integrators start to get involved. They start to partner with them and then they get to get that flywheel effect. And then there's a supercloud, I think that, you know I think Nir Zuk said, Hey, we are basically building out that, he didn't use the term supercloud. But, we're building out that cross cloud capability. You don't need another stove pipe for the edge. You know, so they got on-prem, they got AWS, Azure, you said you have to, absolutely have to run on Microsoft. 'Cause I don't believe today, right? Today they run on, I heard somebody say they run on AWS and Google. >> Yeah. >> I haven't heard much about Microsoft. >> Right. >> Both AWS and Google are here. Microsoft, the bigger competitor in security, but Nir Zuk was unequivocal. Yes, of course you have to run, you got to run it on an Alibaba cloud. He didn't say that, but if you want to secure the China cloud, you got to run on Alibaba. >> Absolutely. >> And Oracle he said. Didn't mention IBM, but no reason they can't run on IBM's cloud. But unless IBM doesn't want 'em to. >> Well they're very customer focused and customer first. So it'll be interesting to see if customers take them in that direction. >> Well it's a good point, right? If customers say, Hey we want you running in this cloud, they will. And, but he did call out Oracle, which I thought was interesting. And so, Oracle's all about mission critical data, mission critical apps. So, you know, that's a good sign. You know, I mean there's so much opportunity in cyber, but so much confusion. You know, sneak had a raise today. It was a down round, no surprise there. But you know, these companies are going to start getting tight on cash, and you've seen layoffs, right? And so, I dunno who said it, I think it was Carl at the end said in a downturn, the strongest companies come out stronger. And that's generally, generally been the case. That kind of rich get richer. We see that in the last downturn? Yes and no, to a certain extent. It's still all about execution. I mean I think about EMC coming out of the last downturn. They did come out stronger and then they started to rocket, but then look what happened. They couldn't remain independent. They were just using m and a as a technique to hide the warts. You know so, what Nir Zuk said that was most interesting to me is when we acquire, we acquire with the intent of integrating. ServiceNow has a similar philosophy. I think that's why they've been somewhat successful. And Oracle, for sure, has had a similar philosophy. So, and that idea of shifting labor into vendor R and D has always been a winning formula. >> I think we heard that today. Excited for day two tomorrow. We've got some great conversations. We're going to be able to talk with some customers, the chief product officer is on. So we have more great content coming from our last live show over the year. Dave, it's been great co-hosting day one with you. Look forward to doing it tomorrow. >> Yeah, thanks for doing this. >> All right. >> All right. For Dave Vellante, I'm Lisa Martin. You've been watching theCUBE, the leader in live enterprise and emerging tech coverage. See you tomorrow. (gentle music fades)

>>The Cube is live with three different stages here at AW S Reinvent in fabulous Las Vegas, Nevada. My name's Savannah Peterson, and I gotta tell you, even though the cube has been at AW w s reinvent for over a decade, this is my first year and wow, is it just buzzing in here? >>It's >>Busy, it's crowd, it's loud. >>So exciting to be here with you all. >>We're hearing north of 50,000 people, and I'm hearing hundreds of thousands online. >>No, it's going great. There's lots of buzz, lots of excitement this year, of course, three times a number of people, but it's fantastic. >>Everyone at the same place at the same time. Energy is just pretty special. So it's >>Fun. >>I mean, AWS is a friendly place for security companies and I'm excited to talk about that. >>Let's be here. We have a lot coming for you. We're super excited and if you think about it, it's price, performance, it's data, it's security, and it's solutions for purpose-built use cases. >>Great job. Congratulations. I love the mess. I love how you guys had the theme. I thought his keynote was great and it's great to see Amazon continue to innovate. >>My name is Savannah Peterson. We are the Cube and we are the leading source for high tech coverage.

>>Hey everyone, welcome to the Cube's coverage of AWS Reinvent 2022. Lisa Martin here with you with Subaru ier, one of our alumni who's now the CEO of Aerospike. Sabu. Great to have you on the program. Thank you for joining us. >>Great as always, to be on the cube. Luisa, good to meet you. >>So, you know, every company these days has got to be a data company, whether it's a retailer, a manufacturer, a grocer, a automotive company. But for a lot of companies, data is underutilized, yet a huge asset that is value added. Why do you think companies are struggling so much to make data a value added asset? >>Well, you know, we, we see this across the board when I talk to customers and prospects. There's a desire from the business and from it actually to leverage data to really fuel newer applications, newer services, newer business lines, if you will, for companies. I think the struggle is one, I think one the, you know, the plethora of data that is created, you know, surveys say that over the next three years data is gonna be, you know, by 2025, around 175 zetabytes, right? A hundred and zetabytes of data is gonna be created. And that's really a, a, a growth of north of 30% year over year. But the more important, and the interesting thing is the real time component of that data is actually growing at, you know, 35% cagr. And what enterprises desire is decisions that are made in real time or near real time. >>And a lot of the challenges that do exist today is that either the infrastructure that enterprises have in place was never built to actually manipulate data in real time. The second is really the ability to actually put something in place which can handle spikes yet be cost efficient if you'll, so you can build for really peak loads, but then it's very expensive to operate that particular service at normal loads. So how do you build something which actually works for you, for both you, both users, so to speak? And the last point that we see out there is even if you're able to, you know, bring all that data, you don't have the processing capability to run through that data. So as a result, most enterprises struggle with one, capturing the data, you know, making decisions from it in real time and really operating it at the cost point that they need to operate it at. >>You know, you bring up a great point with respect to real time data access. And I think one of the things that we've learned the last couple of years is that access to real time data, it's not a nice to have anymore. It's business critical for organizations in any industry. Talk about that as one of the challenges that organizations are facing. >>Yeah. When, when, when we started Aerospike, right when the company started, it started with the premise that data is gonna grow, number one, exponentially. Two, when applications open up to the internet, there's gonna be a flood of users and demands on those applications. And that was true primarily when we started the company in the ad tech vertical. So ad tech was the first vertical where there was a lot of data both on the supply side and the demand side from an inventory of ads that were available. And on the other hand, they had like microseconds or milliseconds in which they could make a decision on which ad to put in front of you and I so that we would click or engage with that particular ad. But over the last three to five years, what we've seen is as digitization has actually permeated every industry out there, the need to harness data in real time is pretty much present in every industry. >>Whether that's retail, whether that's financial services, telecommunications, e-commerce, gaming and entertainment. Every industry has a desire. One, the innovative companies, the small companies rather, are innovating at a pace and standing up new businesses to compete with the larger companies in each of these verticals. And the larger companies don't wanna be left behind. So they're standing up their own competing services or getting into new lines of business that really harness and are driven by real time data. So this compelling pressures, one, the customer exp you know, customer experience is paramount and we as customers expect answers in, you know, an instant in real time. And on the other hand, the way they make decisions is based on a large data set because you know, larger data sets actually propel better decisions. So there's competing pressures here, which essentially drive the need. One from a business perspective, two from a customer perspective to harness all of this data in real time. So that's what's driving an inces need to actually make decisions in real or near real time. >>You know, I think one of the things that's been in short supply over the last couple of years is patients we do expect as consumers, whether we're in our business lives, our personal lives that we're going to be getting, be given information and data that's relevant, it's personal to help us make those real time decisions. So having access to real time data is really business critical for organizations across any industries. Talk about some of the main capabilities that modern data applications and data platforms need to have. What are some of the key capabilities of a modern data platform that need to be delivered to meet demanding customer expectations? >>So, you know, going back to your initial question Lisa, around why is data really a high value but underutilized or underleveraged asset? One of the reasons we see is a lot of the data platforms that, you know, some of these applications were built on have been then around for a decade plus and they were never built for the needs of today, which is really driving a lot of data and driving insight in real time from a lot of data. So there are four major capabilities that we see that are essential ingredients of any modern data platform. One is really the ability to, you know, operate at unlimited scale. So what we mean by that is really the ability to scale from gigabytes to even petabytes without any degradation in performance or latency or throughput. The second is really, you know, predictable performance. So can you actually deliver predictable performance as your data size grows or your throughput grows or your concurrent user on that application of service grows? >>It's really easy to build an application that operates at low scale or low throughput or low concurrency, but performance usually starts degrading as you start scaling one of these attributes. The third thing is the ability to operate and always on globally resilient application. And that requires a, a really robust data platform that can be up on a five, nine basis globally, can support global distribution because a lot of these applications have global users. And the last point is, goes back to my first answer, which is, can you operate all of this at a cost point? Which is not prohibitive, but it makes sense from a TCO perspective. Cuz a lot of times what we see is people make choices of data platforms and as ironically their service or applications become more successful and more users join their journey, the revenue starts going up, the user base starts going up, but the cost basis starts crossing over the revenue and they're losing money on the service, ironically, as the service becomes more popular. So really unlimited scale, predictable performance always on, on a globally resilient basis and low tco. These are the four essential capabilities of any modern data platform. >>So then talk to me with those as the four main core functionalities of a modern data platform. How does aerospace deliver that? >>So we were built, as I said, from the from day one to operate at unlimited scale and deliver predictable performance. And then over the years as we work with customers, we build this incredible high availability capability which helps us deliver the always on, you know, operations. So we have customers who are, who have been on the platform 10 years with no downtime for example, right? So we are talking about an amazing continuum of high availability that we provide for customers who operate these, you know, globally resilient services. The key to our innovation here is what we call the hybrid memory architecture. So, you know, going a little bit technically deep here, essentially what we built out in our architecture is the ability on each node or each server to treat a bank of SSDs or solid state devices as essentially extended memory. So you're getting memory performance, but you're accessing these SSDs, you're not paying memory prices, but you're getting memory performance as a result of that. >>You can attach a lot more data to each node or each server in your distributed cluster. And when you kind of scale that across basically a distributed cluster you can do with aerospike, the same things at 60 to 80% lower server count and as a result 60 to 80% lower TCO compared to some of the other options that are available in the market. Then basically, as I said, that's the key kind of starting point to the innovation. We layer around capabilities like, you know, replication change, data notification, you know, synchronous and asynchronous replication. The ability to actually stretch a single cluster across multiple regions. So for example, if you're operating a global service, you can have a single aerospace cluster with one node in San Francisco, one northern New York, another one in London. And this would be basically seamlessly operating. So that, you know, this is strongly consistent. >>Very few no SQL data platforms are strongly consistent or if they are strongly consistent, they will actually suffer performance degradation. And what strongly consistent means is, you know, all your data is always available, it's guaranteed to be available, there is no data lost anytime. So in this configuration that I talked about, if the node in London goes down, your application still continues to operate, right? Your users see no kind of downtime and you know, when London comes up, it rejoins the cluster and everything is back to kind of the way it was before, you know, London left the cluster so to speak. So the op, the ability to do this globally resilient, highly available kind of model is really, really powerful. A lot of our customers actually use that kind of a scenario and we offer other deployment scenarios from a higher availability perspective. So everything starts with HMA or hybrid memory architecture and then we start building out a lot of these other capabilities around the platform. >>And then over the years, what our customers have guided us to do is as they're putting together a modern kind of data infrastructure, we don't live in a silo. So aerospace gets deployed with other technologies like streaming technologies or analytics technologies. So we built connectors into Kafka, pulsar, so that as you're ingesting data from a variety of data sources, you can ingest them at very high ingest speeds and store them persistently into Aerospike. Once the data is in Aerospike, you can actually run spark jobs across that data in a, in a multithreaded parallel fashion to get really insight from that data at really high, high throughput and high speed, >>High throughput, high speed, incredibly important, especially as today's landscape is increasingly distributed. Data centers, multiple public clouds, edge IOT devices, the workforce embracing more and more hybrid these days. How are you ex helping customers to extract more value from data while also lowering costs? Go into some customer examples cause I know you have some great ones. >>Yeah, you know, I think we have, we have built an amazing set of customers and customers actually use us for some really mission critical applications. So, you know, before I get into specific customer examples, let me talk to you about some of kind of the use cases which we see out there. We see a lot of aerospace being used in fraud detection. We see us being used in recommendations and since we use get used in customer data profiles or customer profiles, customer 360 stores, you know, multiplayer gaming and entertainment, these are kind of the repeated use case digital payments. We power most of the digital payment systems across the globe. Specific example from a, from a specific example perspective, the first one I would love to talk about is PayPal. So if you use PayPal today, then you know when you actually paying somebody your transaction is, you know, being sent through aero spike to really decide whether this is a fraudulent transaction or not. >>And when you do that, you know, you and I as a customer not gonna wait around for 10 seconds for PayPal to say yay or me, we expect, you know, the decision to be made in an instant. So we are powering that fraud detection engine at PayPal for every transaction that goes through PayPal before us, you know, PayPal was missing out on about 2% of their SLAs, which was essentially millions of dollars, which they were losing because, you know, they were letting transactions go through and taking the risk that it, it's not a fraudulent transaction with the aerospace. They can now actually get a much better sla and the data set on which they compute the fraud score has gone up by, you know, several factors. So by 30 x if you will. So not only has the data size that is powering the fraud engine actually grown up 30 x with Aerospike. Yeah. But they're actually making decisions in an instant for, you know, 99.95% of their transactions. So that's, >>And that's what we expect as consumers, right? We want to know that there's fraud detection on the swipe regardless of who we're interacting with. >>Yes. And so that's a, that's a really powerful use case and you know, it's, it's a great customer, great customer success story. The other one I would talk about is really Wayfair, right? From retail and you know, from e-commerce. So everybody knows Wayfair global leader in really, you know, online home furnishings and they use us to power their recommendations engine and you know, it's basically if you're purchasing this, people who bought this but also bought these five other things, so on and so forth, they have actually seen the card size at checkout go by up to 30% as a result of actually powering their recommendations in G by through Aerospike. And they, they were able to do this by reducing the server count by nine x. So on one ninth of the servers that were there before aerospace, they're now powering their recommendation engine and seeing card size checkout go up by 30%. Really, really powerful in terms of the business outcome and what we are able to, you know, drive at Wayfair >>Hugely powerful as a business outcome. And that's also what the consumer wants. The consumer is expecting these days to have a very personalized, relevant experience that's gonna show me if I bought this, show me something else that's related to that. We have this expectation that needs to be really fueled by technology. >>Exactly. And you know, another great example you asked about, you know, customer stories, Adobe, who doesn't know Adobe, you know, they, they're on a, they're on a mission to deliver the best customer experience that they can and they're talking about, you know, great customer 360 experience at scale and they're modernizing their entire edge compute infrastructure to support this. With Aerospike going to Aerospike, basically what they have seen is their throughput go up by 70%, their cost has been reduced by three x. So essentially doing it at one third of the cost while their annual data growth continues at, you know, about north of 30%. So not only is their data growing, they're able to actually reduce their cost to actually deliver this great customer experience by one third to one third and continue to deliver great customer 360 experience at scale. Really, really powerful example of how you deliver Customer 360 in a world which is dynamic and you know, on a dataset which is constantly growing at north, north of 30% in this case. >>Those are three great examples, PayPal, Wayfair, Adobe talking about, especially with Wayfair when you talk about increasing their cart checkout sizes, but also with Adobe increasing throughput by over 70%. I'm looking at my notes here. While data is growing at 32%, that's something that every organization has to contend with data growth is continuing to scale and scale and scale. >>Yep. I, I'll give you a fun one here. So, you know, you may not have heard about this company, it's called Dream 11 and it's a company based out of India, but it's a very, you know, it's a fun story because it's the world's largest fantasy sports platform and you know, India is a nation which is cricket crazy. So you know, when, when they have their premier league going on, you know, there's millions of users logged onto the dream alone platform building their fantasy lead teams and you know, playing on that particular platform, it has a hundred million users, a hundred million plus users on the platform, 5.5 million concurrent users and they have been growing at 30%. So they are considered a, an amazing success story in, in terms of what they have accomplished and the way they have architected their platform to operate at scale. And all of that is really powered by aerospace where think about that they are able to deliver all of this and support a hundred million users, 5.5 million concurrent users all with you know, 99 plus percent of their transactions completing in less than one millisecond. Just incredible success story. Not a brand that is you know, world renowned but at least you know from a what we see out there, it's an amazing success story of operating at scale. >>Amazing success story, huge business outcomes. Last question for you as we're almost out of time is talk a little bit about Aerospike aws, the partnership GRAVITON two better together. What are you guys doing together there? >>Great partnership. AWS has multiple layers in terms of partnerships. So you know, we engage with AWS at the executive level. They plan out, really roll out of new instances in partnership with us, making sure that, you know, those instance types work well for us. And then we just released support for Aerospike on the graviton platform and we just announced a benchmark of Aerospike running on graviton on aws. And what we see out there is with the benchmark, a 1.6 x improvement in price performance and you know, about 18% increase in throughput while maintaining a 27% reduction in cost, you know, on graviton. So this is an amazing story from a price performance perspective, performance per wat for greater energy efficiencies, which basically a lot of our customers are starting to kind of talk to us about leveraging this to further meet their sustainability target. So great story from Aero Aerospike and aws, not just from a partnership perspective on a technology and an executive level, but also in terms of what joint outcomes we are able to deliver for our customers. >>And it sounds like a great sustainability story. I wish we had more time so we would talk about this, but thank you so much for talking about the main capabilities of a modern data platform, what's needed, why, and how you guys are delivering that. We appreciate your insights and appreciate your time. >>Thank you very much. I mean, if, if folks are at reinvent next week or this week, come on and see us at our booth. We are in the data analytics pavilion. You can find us pretty easily. Would love to talk to you. >>Perfect. We'll send them there. So Ira, thank you so much for joining me on the program today. We appreciate your insights. >>Thank you Lisa. >>I'm Lisa Martin. You're watching The Cubes coverage of AWS Reinvent 2022. Thanks for watching.

hi everybody welcome back to re invent 2022. this is thecube's exclusive coverage we're here at the satellite set it's up on the fifth floor of the Venetian Conference Center and this is part of the global startup program the AWS startup showcase series that we've been running all through last year and and into this year with AWS and featuring some of its its Global Partners Ed wallson series the CEO of chaos search many times Cube Alum and Kevin Miller there's also a cube Alum vice president GM of S3 at AWS guys good to see you again yeah great to see you Dave hi Kevin this is we call this our Super Bowl so this must be like your I don't know uh World Cup it's a pretty big event yeah it's the World Cup for sure yeah so a lot of S3 talk you know I mean that's what got us all started in 2006 so absolutely what's new in S3 yeah it's been a great show we've had a number of really interesting launches over the last few weeks and a few at the show as well so you know we've been really focused on helping customers that are running Mass scale data Lakes including you know whether it's structured or unstructured data we actually announced just a few just an hour ago I think it was a new capability to give customers cross-account access points for sharing data securely with other parts of the organization and that's something that we'd heard from customers is as they are growing and have more data sets and they're looking to to get more out of their data they are increasingly looking to enable multiple teams across their businesses to access those data sets securely and that's what we provide with cross-count access points we also launched yesterday our multi-region access point failover capabilities and so again this is where customers have data sets and they're using multiple regions for certain critical workloads they're now able to to use that to fail to control the failover between different regions in AWS and then one other launch I would just highlight is some improvements we made to storage lens which is our really a very novel and you need capability to help customers really understand what storage they have where who's accessing it when it's being accessed and we added a bunch of new metrics storage lens has been pretty exciting for a lot of customers in fact we looked at the data and saw that customers who have adopted storage lens typically within six months they saved more than six times what they had invested in turning storage lens on and certainly in this environment right now we have a lot of customers who are it's pretty top of mind they're looking for ways to optimize their their costs in the cloud and take some of those savings and be able to reinvest them in new innovation so pretty exciting with the storage lens launch I think what's interesting about S3 is that you know pre-cloud Object Store was this kind of a niche right and then of course you guys announced you know S3 in 2006 as I said and okay great you know cheap and deep storage simple get put now the conversations about how to enable value from from data absolutely analytics and it's just a whole new world and Ed you've talked many times I love the term yeah we built chaos search on the on the shoulders of giants right and so the under underlying that is S3 but the value that you can build on top of that has been key and I don't think we've talked about his shoulders and Giants but we've talked about how we literally you know we have a big Vision right so hard to kind of solve the challenge to analytics at scale we really focus on the you know the you know Big Data coming environment get analytics so we talk about the on the shoulders Giants obviously Isaac Newton's you know metaphor of I learned from everything before and we layer on top so really when you talk about all the things come from S3 like I just smile because like we picked it up naturally we went all in an S3 and this is where I think you're going Dave but everyone is so let's just cut the chase like so any of the data platforms you're using S3 is what you're building but we did it a little bit differently so at first people using a cold storage like you said and then they ETL it up into a different platforms for analytics of different sorts now people are using it closer they're doing caching layers and cashing out and they're that's where but that's where the attributes of a scale or reliability are what we did is we actually make S3 a database so literally we have no persistence outside that three and that kind of comes in so it's working really well with clients because most of the thing is we pick up all these attributes of scale reliability and it shows up in the clients environments and so when you launch all these new scalable things we just see it like our clients constantly comment like one of our biggest customers fintech in uh Europe they go to Black Friday again black Friday's not one days and they lose scale from what is it 58 terabytes a day and they're going up to 187 terabytes a day and we don't Flinch they say how do you do that well we built our platform on S3 as long as you can stream it to S3 so they're saying I can't overrun S3 and it's a natural play so it's it's really nice that but we take out those attributes but same thing that's why we're able to you know help clients get you know really you know Equifax is a good example maybe they're able to consolidate 12 their divisions on one platform we couldn't have done that without the scale and the performance of what you can get S3 but also they saved 90 I'm able to do that but that's really because the only persistence is S3 and what you guys are delivering but and then we really for focus on shoulders Giants we're doing on top of that innovating on top of your platforms and bringing that out so things like you know we have a unique data representation that makes it easy to ingest this data because it's kind of coming at you four v's of big data we allow you to do that make it performant on s3h so now you're doing hot analytics on S3 as if it's just a native database in memory but there's no memory SSC caching and then multi-model once you get it there don't move it leverage it in place so you know elasticsearch access you know Cabana grafana access or SQL access with your tools so we're seeing that constantly but we always talk about on the shoulders of giants but even this week I get comments from our customers like how did you do that and most of it is because we built on top of what you guys provided so it's really working out pretty well and you know we talk a lot about digital transformation of course we had the pleasure sitting down with Adam solipski prior John Furrier flew to Seattle sits down his annual one-on-one with the AWS CEO which is kind of cool yeah it was it's good it's like study for the test you know and uh and so but but one of the interesting things he said was you know we're one of our challenges going forward is is how do we go Beyond digital transformation into business transformation like okay well that's that's interesting I was talking to a customer today AWS customer and obviously others because they're 100 year old company and they're basically their business was they call them like the Uber for for servicing appliances when your Appliance breaks you got to get a person to serve it a service if it's out of warranty you know these guys do that so they got to basically have a you know a network of technicians yeah and they gotta deal with the customers no phone right so they had a completely you know that was a business transformation right they're becoming you know everybody says they're coming a software company but they're building it of course yeah right on the cloud so wonder if you guys could each talk about what's what you're seeing in terms of changing not only in the sort of I.T and the digital transformation but also the business transformation yeah I know I I 100 agree that I think business transformation is probably that one of the top themes I'm hearing from customers of all sizes right now even in this environment I think customers are looking for what can I do to drive top line or you know improve bottom line or just improve my customer experience and really you know sort of have that effect where I'm helping customers get more done and you know it is it is very tricky because to do that successfully the customers that are doing that successfully I think are really getting into the lines of businesses and figuring out you know it's probably a different skill set possibly a different culture different norms and practices and process and so it's it's a lot more than just a like you said a lot more than just the technology involved but when it you know we sort of liquidate it down into the data that's where absolutely we see that as a critical function for lines of businesses to become more comfortable first off knowing what data sets they have what data they they could access but possibly aren't today and then starting to tap into those data sources and then as as that progresses figuring out how to share and collaborate with data sets across a company to you know to correlate across those data sets and and drive more insights and then as all that's being done of course it's important to measure the results and be able to really see is this what what effect is this having and proving that effect and certainly I've seen plenty of customers be able to show you know this is a percentage increase in top or bottom line and uh so that pattern is playing out a lot and actually a lot of how we think about where we're going with S3 is related to how do we make it easier for customers to to do everything that I just described to have to understand what data they have to make it accessible and you know it's great to have such a great ecosystem of partners that are then building on top of that and innovating to help customers connect really directly with the businesses that they're running and driving those insights well and customers are hours today one of the things I loved that Adam said he said where Amazon is strategically very very patient but tactically we're really impatient and the customers out there like how are you going to help me increase Revenue how are you going to help me cut costs you know we were talking about how off off camera how you know software can actually help do that yeah it's deflationary I love the quote right so software's deflationary as costs come up how do you go drive it also free up the team and you nail it it's like okay everyone wants to save money but they're not putting off these projects in fact the digital transformation or the business it's actually moving forward but they're getting a little bit bigger but everyone's looking for creative ways to look at their architecture and it becomes larger larger we talked about a couple of those examples but like even like uh things like observability they want to give this tool set this data to all the developers all their sres same data to all the security team and then to do that they need to find a way an architect should do that scale and save money simultaneously so we see constantly people who are pairing us up with some of these larger firms like uh or like keep your data dog keep your Splunk use us to reduce the cost that one and one is actually cheaper than what you have but then they use it either to save money we're saving 50 to 80 hard dollars but more importantly to free up your team from the toil and then they they turn around and make that budget neutral and then allowed to get the same tools to more people across the org because they're sometimes constrained of getting the access to everyone explain that a little bit more let's say I got a Splunk or data dog I'm sifting through you know logs how exactly do you help so it's pretty simple I'll use dad dog example so let's say using data dog preservability so it's just your developers your sres managing environments all these platforms are really good at being a monitoring alerting type of tool what they're not necessarily great at is keeping the data for longer periods like the log data the bigger data that's where we're strong what you see is like a data dog let's say you're using it for a minister for to keep 30 days of logs which is not enough like let's say you're running environment you're finding that performance issue you kind of want to look to last quarter in last month in or maybe last Black Friday so 30 days is not enough but will charge you two eighty two dollars and eighty cents a gigabyte don't focus on just 280 and then if you just turn the knob and keep seven days but keep two years of data on us which is on S3 it goes down to 22 cents plus our list price of 80 cents goes to a dollar two compared to 280. so here's the thing what they're able to do is just turn a knob get more data we do an integration so you can go right from data dog or grafana directly into our platform so the user doesn't see it but they save money A lot of times they don't just save the money now they use that to go fund and get data dog to a lot more people make sense so it's a creativity they're looking at it and they're looking at tools we see the same thing with a grafana if you look at the whole grafana play which is hey you can't put it in one place but put Prometheus for metrics or traces we fit well with logs but they're using that to bring down their costs because a lot of this data just really bogs down these applications the alerting monitoring are good at small data they're not good at the big data which is what we're really good at and then the one and one is actually less than you paid for the one so it and it works pretty well so things are really unpredictable right now in the economy you know during the pandemic we've sort of lockdown and then the stock market went crazy we're like okay it's going to end it's going to end and then it looked like it was going to end and then it you know but last year it reinvented just just in that sweet spot before Omicron so we we tucked it in which which was awesome right it was a great great event we really really missed one physical reinvent you know which was very rare so that's cool but I've called it the slingshot economy it feels like you know you're driving down the highway and you got to hit the brakes and then all of a sudden you're going okay we're through it Oh no you're gonna hit the brakes again yeah so it's very very hard to predict and I was listening to jassy this morning he was talking about yeah consumers they're still spending but what they're doing is they're they're shopping for more features they might be you know buying a TV that's less expensive you know more value for the money so okay so hopefully the consumer spending will get us out of this but you don't really know you know and I don't yeah you know we don't seem to have the algorithms we've never been through something like this before so what are you guys seeing in terms of customer Behavior given that uncertainty well one thing I would highlight that I think particularly going back to what we were just talking about as far as business and digital transformation I think some customers are still appreciating the fact that where you know yesterday you may have had to to buy some Capital put out some capital and commit to something for a large upfront expenditure is that you know today the value of being able to experiment and scale up and then most importantly scale down and dynamically based on is the experiment working out am I seeing real value from it and doing that on a time scale of a day or a week or a few months that is so important right now because again it gets to I am looking for a ways to innovate and to drive Top Line growth but I I can't commit to a multi-year sort of uh set of costs to to do that so and I think plenty of customers are finding that even a few months of experimentation gives them some really valuable insight as far as is this going to be successful or not and so I think that again just of course with S3 and storage from day one we've been elastic pay for what you use if you're not using the storage you don't get charged for it and I think that particularly right now having the applications and the rest of the ecosystem around the storage and the data be able to scale up and scale down is is just ever more important and when people see that like typically they're looking to do more with it so if they find you usually find these little Department projects but they see a way to actually move faster and save money I think it is a mix of those two they're looking to expand it which can be a nightmare for sales Cycles because they take longer but people are looking well why don't you leverage this and go across division so we do see people trying to leverage it because they're still I don't think digital transformation is slowing down but a lot more to be honest a lot more approvals at this point for everything it is you know Adam and another great quote in his in his keynote he said if you want to save money the Cloud's a place to do it absolutely and I read an article recently and I was looking through and I said this is the first time you know AWS has ever seen a downturn because the cloud was too early back then I'm like you weren't paying attention in 2008 because that was the first major inflection point for cloud adoption where CFO said okay stop the capex we're going to Opex and you saw the cloud take off and then 2010 started this you know amazing cycle that we really haven't seen anything like it where they were doubling down in Investments and they were real hardcore investment it wasn't like 1998 99 was all just going out the door for no clear reason yeah so that Foundation is now in place and I think it makes a lot of sense and it could be here for for a while where people are saying Hey I want to optimize and I'm going to do that on the cloud yeah no I mean I've obviously I certainly agree with Adam's quote I think really that's been in aws's DNA from from day one right is that ability to scale costs with with the actual consumption and paying for what you use and I think that you know certainly moments like now are ones that can really motivate change in an organization in a way that might not have been as palatable when it just it didn't feel like it was as necessary yeah all right we got to go give you a last word uh I think it's been a great event I love all your announcements I think this is wonderful uh it's been a great show I love uh in fact how many people are here at reinvent north of 50 000. yeah I mean I feel like it was it's as big if not bigger than 2019. people have said ah 2019 was a record when you count out all the professors I don't know it feels it feels as big if not bigger so there's great energy yeah it's quite amazing and uh and we're thrilled to be part of it guys thanks for coming on thecube again really appreciate it face to face all right thank you for watching this is Dave vellante for the cube your leader in Enterprise and emerging Tech coverage we'll be right back foreign

>>The Cube is live with three different stages here at aws Reinvent in fabulous Las Vegas, Nevada, and wow, is it just buzzing in here? It is absolutely overwhelming, but also thrilling to be here in Las Vegas.

>>Oh, MABA. It so good. It, so, so. Oh my, to make my move a in nothing. Lose open. Let go. Here I go. Ready to >>Ready. >>Ready for Are you >>Ready? Coverage of AWS Reinvent 22 continues in a moment.

>>Oh, MABA. It so good. It, so, so. Oh my, to make my move a in nothing. Lose open. Let go. Here I go. Are ready to go. >>Ready. >>Ready for more? Are you >>Ready? Coverage of AWS Reinvent 22 continues in a moment.

>>Welcome to Las Vegas. It's the cube live at AWS Reinvent 22. >>The show floor doors just opened people pouring in, and you can certainly feel the excitement here. >>I've heard it's the largest >>Reinvent ever. I call that NextGen Cloud NextGen. It is. It's happening. It's happening right now. >>It is hot. It's a hot >>Show. >>Knowing you in covering your company, that this is not just yesterday, you came up with this idea that now everyone's talking about >>The >>Cube, where we are the leader in high tech coverage. >>It is. It's happening. It's happening right now. >>Wow. That was good. Woo. All right. There we go. Nice job guys.

(upbeat music) >> From theCube Studios in Palo Alto in Boston, bringing you data driven insights from theCube and ETR. This is Breaking Analysis with Dave Vellante. >> While by no means a safe haven, the cybersecurity sector has outpaced the broader tech market by a meaningful margin, that is up until very recently. Cybersecurity remains the number one technology priority for the C-suite, but as we've previously reported the CISO's budget has constraints just like other technology investments. Recent trends show that economic headwinds have elongated sales cycles, pushed deals into future quarters, and just like other tech initiatives, are pacing cybersecurity investments and breaking them into smaller chunks. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis we explain how cybersecurity trends are reverting to the mean and tracking more closely with other technology investments. We'll make a couple of valuation comparisons to show the magnitude of the challenge and which cyber firms are feeling the heat, which aren't. There are some exceptions. We'll then show the latest survey data from ETR to quantify the contraction in spending momentum and close with a glimpse of the landscape of emerging cybersecurity companies, the private companies that could be ripe for acquisition, consolidation, or disruptive to the broader market. First, let's take a look at the recent patterns for cyber stocks relative to the broader tech market as a benchmark, as an indicator. Here's a year to date comparison of the bug ETF, which comprises a basket of cyber security names, and we compare that with the tech heavy NASDAQ composite. Notice that on April 13th of this year the cyber ETF was actually in positive territory while the NAS was down nearly 14%. Now by August 16th, the green turned red for cyber stocks but they still meaningfully outpaced the broader tech market by more than 950 basis points as of December 2nd that Delta had contracted. As you can see, the cyber ETF is now down nearly 25%, year to date, while the NASDAQ is down 27% and change. Now take a look at just how far a few of the high profile cybersecurity names have fallen. Here are six security firms that we've been tracking closely since before the pandemic. We've been, you know, tracking dozens but let's just take a look at this data and the subset. We show for comparison the S&P 500 and the NASDAQ, again, just for reference, they're both up since right before the pandemic. They're up relative to right before the pandemic, and then during the pandemic the S&P shot up more than 40%, relative to its pre pandemic level, around February is what we're using for the pre pandemic level, and the NASDAQ peaked at around 65% higher than that February level. They're now down 85% and 71% of their previous. So they're at 85% and 71% respectively from their pandemic highs. You compare that to these six companies, Splunk, which was and still is working through a transition is well below its pre pandemic market value and 44, it's 44% of its pre pandemic high as of last Friday. Palo Alto Networks is the most interesting here, in that it had been facing challenges prior to the pandemic related to a pivot to the Cloud which we reported on at the time. But as we said at that time we believe the company would sort out its Cloud transition, and its go to market challenges, and sales compensation issues, which it did as you can see. And its valuation jumped from 24 billion prior to Covid to 56 billion, and it's holding 93% of its peak value. Its revenue run rate is now over 6 billion with a healthy growth rate of 24% expected for the next quarter. Similarly, Fortinet has done relatively well holding 71% of its peak Covid value, with a healthy 34% revenue guide for the coming quarter. Now, Okta has been the biggest disappointment, a darling of the pandemic Okta's communication snafu, with what was actually a pretty benign hack combined with difficulty absorbing its 7 billion off zero acquisition, knocked the company off track. Its valuation has dropped by 35 billion since its peak during the pandemic, and that's after a nice beat and bounce back quarter just announced by Okta. Now, in our view Okta remains a viable long-term leader in identity. However, its recent fiscal 24 revenue guide was exceedingly conservative at around 16% growth. So either the company is sandbagging, or has such poor visibility that it wants to be like super cautious or maybe it's actually seeing a dramatic slowdown in its business momentum. After all, this is a company that not long ago was putting up 50% plus revenue growth rates. So it's one that bears close watching. CrowdStrike is another big name that we've been talking about on Breaking Analysis for quite some time. It like Okta has led the industry in a key ETR performance indicator that measures customer spending momentum. Just last week, CrowdStrike announced revenue increased more than 50% but new ARR was soft and the company guided conservatively. Not surprisingly, the stock got absolutely crushed as CrowdStrike blamed tepid demand from smaller and midsize firms. Many analysts believe that competition from Microsoft was one factor along with cautious spending amongst those midsize and smaller customers. Notably, large customers remain active. So we'll see if this is a longer term trend or an anomaly. Zscaler is another company in the space that we've reported having great customer spending momentum from the ETR data. But even though the company beat expectations for its recent quarter, like other companies its Outlook was conservative. So other than Palo Alto, and to a lesser extent Fortinet, these companies and others that we're not showing here are feeling the economic pinch and it shows in the compression of value. CrowdStrike, for example, had a 70 billion valuation at one point during the pandemic Zscaler top 50 billion, Okta 45 billion. Now, having said that Palo Alto Networks, Fortinet, CrowdStrike, and Zscaler are all still trading well above their pre pandemic levels that we tracked back in February of 2020. All right, let's go now back to ETR'S January survey and take a look at how much things have changed since the beginning of the year. Remember, this is obviously pre Ukraine, and pre all the concerns about the economic headwinds but here's an X Y graph that shows a net score, or spending momentum on the y-axis, and market presence on the x-axis. The red dotted line at 40% on the vertical indicates a highly elevated net score. Anything above that we think is, you know, super elevated. Now, we filtered the data here to show only those companies with more than 50 responses in the ETR survey. Still really crowded. Note that there were around 20 companies above that red 40% mark, which is a very, you know, high number. It's a, it's a crowded market, but lots of companies with, you know, positive momentum. Now let's jump ahead to the most recent October survey and take a look at what, what's happening. Same graphic plotting, spending momentum, and market presence, and look at the number of companies above that red line and how it's been squashed. It's really compressing, it's still a crowded market, it's still, you know, plenty of green, but the number of companies above 40% that, that key mark has gone from around 20 firms down to about five or six. And it speaks to that compression and IT spending, and of course the elongated sales cycles pushing deals out, taking them in smaller chunks. I can't tell you how many conversations with customers I had, at last week at Reinvent underscoring this exact same trend. The buyers are getting pressure from their CFOs to slow things down, do more with less and, and, and prioritize projects to those that absolutely are critical to driving revenue or cutting costs. And that's rippling through all sectors, including cyber. Now, let's do a bit more playing around with the ETR data and take a look at those companies with more than a hundred citations in the survey this quarter. So N, greater than or equal to a hundred. Now remember the followers of Breaking Analysis know that each quarter we take a look at those, what we call four star security firms. That is, those are the, that are in, that hit the top 10 for both spending momentum, net score, and the N, the mentions in the survey, the presence, the pervasiveness in the survey, and that's what we show here. The left most chart is sorted by spending momentum or net score, and the right hand chart by shared N, or the number of mentions in the survey, that pervasiveness metric. that solid red line denotes the cutoff point at the top 10. And you'll note we've actually cut it off at 11 to account for Auth 0, which is now part of Okta, and is going through a go to market transition, you know, with the company, they're kind of restructuring sales so they can take advantage of that. So starting on the left with spending momentum, again, net score, Microsoft leads all vendors, typical Microsoft, very prominent, although it hadn't always done so, it, for a while, CrowdStrike and Okta were, were taking the top spot, now it's Microsoft. CrowdStrike, still always near the top, but note that CyberArk and Cloudflare have cracked the top five in Okta, which as I just said was consistently at the top, has dropped well off its previous highs. You'll notice that Palo Alto Network Palo Alto Networks with a 38% net score, just below that magic 40% number, is healthy, especially as you look over to the right hand chart. Take a look at Palo Alto with an N of 395. It is the largest of the independent pure play security firms, and has a very healthy net score, although one caution is that net score has dropped considerably since the beginning of the year, which is the case for most of the top 10 names. The only exception is Fortinet, they're the only ones that saw an increase since January in spending momentum as ETR measures it. Now this brings us to the four star security firms, that is those that hit the top 10 in both net score on the left hand side and market presence on the right hand side. So it's Microsoft, Palo Alto, CrowdStrike, Okta, still there even not accounting for a Auth 0, just Okta on its own. If you put in Auth 0, it's, it's even stronger. Adding then in Fortinet and Zscaler. So Microsoft, Palo Alto, CrowdStrike, Okta, Fortinet, and Zscaler. And as we've mentioned since January, only Fortinet has shown an increase in net score since, since that time, again, since the January survey. Now again, this talks to the compression in spending. Now one of the big themes we hear constantly in cybersecurity is the market is overcrowded. Everybody talks about that, me included. The implication there, is there's a lot of room for consolidation and that consolidation can come in the form of M&A, or it can come in the form of people consolidating onto a single platform, and retiring some other vendors, and getting rid of duplicate vendors. We're hearing that as a big theme as well. Now, as we saw in the previous, previous chart, this is a very crowded market and we've seen lots of consolidation in 2022, in the form of M&A. Literally hundreds of M&A deals, with some of the largest companies going private. SailPoint, KnowBe4, Barracuda, Mandiant, Fedora, these are multi billion dollar acquisitions, or at least billion dollars and up, and many of them multi-billion, for these companies, and hundreds more acquisitions in the cyberspace, now less you think the pond is overfished, here's a chart from ETR of emerging tech companies in the cyber security industry. This data comes from ETR's Emerging Technologies Survey, ETS, which is this diamond in a rough that I found a couple quarters ago, and it's ripe with companies that are candidates for M&A. Many would've liked, many of these companies would've liked to, gotten to the public markets during the pandemic, but they, you know, couldn't get there. They weren't ready. So the graph, you know, similar to the previous one, but different, it shows net sentiment on the vertical axis and that's a measurement of, of, of intent to adopt against a mind share on the X axis, which measures, measures the awareness of the vendor in the community. So this is specifically a survey that ETR goes out and, and, and fields only to track those emerging tech companies that are private companies. Now, some of the standouts in Mindshare, are OneTrust, BeyondTrust, Tanium and Endpoint, Net Scope, which we've talked about in previous Breaking Analysis. 1Password, which has been acquisitive on its own. In identity, the managed security service provider, Arctic Wolf Network, a company we've also covered, we've had their CEO on. We've talked about MSSPs as a real trend, particularly in small and medium sized business, we'll come back to that, Sneek, you know, kind of high flyer in both app security and containers, and you can just see the number of companies in the space this huge and it just keeps growing. Now, just to make it a bit easier on the eyes we filtered the data on these companies with with those, and isolated on those with more than a hundred responses only within the survey. And that's what we show here. Some of the names that we just mentioned are a bit easier to see, but these are the ones that really stand out in ERT, ETS, survey of private companies, OneTrust, BeyondTrust, Taniam, Netscope, which is in Cloud, 1Password, Arctic Wolf, Sneek, BitSight, SecurityScorecard, HackerOne, Code42, and Exabeam, and Sim. All of these hit the ETS survey with more than a hundred responses by, by the IT practitioners. Okay, so these firms, you know, maybe they do some M&A on their own. We've seen that with Sneek, as I said, with 1Password has been inquisitive, as have others. Now these companies with the larger footprint, these private companies, will likely be candidate for both buying companies and eventually going public when the markets settle down a bit. So again, no shortage of players to affect consolidation, both buyers and sellers. Okay, so let's finish with some key questions that we're watching. CrowdStrike in particular on its earnings calls cited softness from smaller buyers. Is that because these smaller buyers have stopped adopting? If so, are they more at risk, or are they tactically moving toward the easy button, aka, Microsoft's good enough approach. What does that mean for the market if smaller company cohorts continue to soften? How about MSSPs? Will companies continue to outsource, or pause on on that, as well as try to free up, to try to free up some budget? Adam Celiski at Reinvent last week said, "If you want to save money the Cloud's the best place to do it." Is the cloud the best place to save money in cyber? Well, it would seem that way from the standpoint of controlling budgets with lots of, lots of optionality. You could dial up and dial down services, you know, or does the Cloud add another layer of complexity that has to be understood and managed by Devs, for example? Now, consolidation should favor the likes of Palo Alto and CrowdStrike, cause they're platform players, and some of the larger players as well, like Cisco, how about IBM and of course Microsoft. Will that happen? And how will economic uncertainty impact the risk equation, a particular concern is increase of tax on vulnerable sectors of the population, like the elderly. How will companies and governments protect them from scams? And finally, how many cybersecurity companies can actually remain independent in the slingshot economy? In so many ways the market is still strong, it's just that expectations got ahead of themselves, and now as earnings forecast come, come, come down and come down to earth, it's going to basically come down to who can execute, generate cash, and keep enough runway to get through the knothole. And the one certainty is nobody really knows how tight that knothole really is. All right, let's call it a wrap. Next week we dive deeper into Palo Alto Networks, and take a look at how and why that company has held up so well and what to expect at Ignite, Palo Alto's big user conference coming up later this month in Las Vegas. We'll be there with theCube. Okay, many thanks to Alex Myerson on production and manages the podcast, Ken Schiffman as well, as our newest edition to our Boston studio. Great to have you Ken. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Silicon Angle. He does some great editing for us. Thank you to all. Remember these episodes are all available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibond.com and siliconangle.com, or you can email me directly David.vellante@siliconangle.com or DM me @DVellante, or comment on our LinkedIn posts. Please do checkout etr.ai, they got the best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis. (upbeat music)

>>Hey everyone. Welcome back to Las Vegas. It's the Cube live at AWS Reinvent 2022. This is our fourth day of coverage. Lisa Martin here with Paul Gillen. Paul, we started Monday night, we filmed and streamed for about three hours. We have had shammed pack days, Tuesday, Wednesday, Thursday. What's your takeaway? >>We're routed final turn as we, as we head into the home stretch. Yeah. This is as it has been since the beginning, this show with a lot of energy. I'm amazed for the fourth day of a conference, how many people are still here I am too. And how, and how active they are and how full the sessions are. Huge. Proud for the keynote this morning. You don't see that at most of the day four conferences. Everyone's on their way home. So, so people come here to learn and they're, and they're still >>Learning. They are still learning. And we're gonna help continue that learning path. We have an alumni back with us, Toron joins us, the CPO and co-founder of Dremeo. Tomer, it's great to have you back on the program. >>Yeah, thanks for, for having me here. And thanks for keeping the, the best session for the fourth day. >>Yeah, you're right. I like that. That's a good mojo to come into this interview with Tomer. So last year, last time I saw you was a year ago here in Vegas at Reinvent 21. We talked about the growth of data lakes and the data lake houses. We talked about the need for open data architectures as opposed to data warehouses. And the headline of the Silicon Angle's article on the interview we did with you was, Dremio Predicts 2022 will be the year open data architectures replace the data warehouse. We're almost done with 2022. Has that prediction come true? >>Yeah, I think, I think we're seeing almost every company out there, certainly in the enterprise, adopting data lake, data lakehouse technology, embracing open source kind of file and table formats. And, and so I think that's definitely happening. Of course, nothing goes away. So, you know, data warehouses don't go away in, in a year and actually don't go away ever. We still have mainframes around, but certainly the trends are, are all pointing in that direction. >>Describe the data lakehouse for anybody who may not be really familiar with that and, and what it's, what it really means for organizations. >>Yeah. I think you could think of the data lakehouse as the evolution of the data lake, right? And so, you know, for, for, you know, the last decade we've had kind of these two options, data lakes and data warehouses and, you know, warehouses, you know, having good SQL support, but, and good performance. But you had to spend a lot of time and effort getting data into the warehouse. You got locked into them, very, very expensive. That's a big problem now. And data lakes, you know, more open, more scalable, but had all sorts of kind of limitations. And what we've done now as an industry with the Lake House, and especially with, you know, technologies like Apache Iceberg, is we've unlocked all the capabilities of the warehouse directly on object storage like s3. So you can insert and update and delete individual records. You can do transactions, you can do all the things you could do with a, a database directly in kind of open formats without getting locked in at a much lower cost. >>But you're still dealing with semi-structured data as opposed to structured data. And there's, there's work that has to be done to get that into a usable form. That's where Drio excels. What, what has been happening in that area to, to make, I mean, is it formats like j s o that are, are enabling this to happen? How, how we advancing the cause of making semi-structured data usable? Yeah, >>Well, I think first of all, you know, I think that's all changed. I think that was maybe true for the original data lakes, but now with the Lake house, you know, our bread and butter is actually structured data. It's all, it's all tables with the schema. And, you know, you can, you know, create table insert records. You know, it's, it's, it's really everything you can do with a data warehouse you can now do in the lakehouse. Now, that's not to say that there aren't like very advanced capabilities when it comes to, you know, j s O and nested data and kind of sparse data. You know, we excel in that as well. But we're really seeing kind of the lakehouse take over the, the bread and butter data warehouse use cases. >>You mentioned open a minute ago. Talk about why it's, why open is important and the value that it can deliver for customers. >>Yeah, well, I think if you look back in time and you see all the challenges that companies have had with kind of traditional data architectures, right? The, the, the, a lot of that comes from the, the, the problems with data warehouses. The fact that they are, you know, they're very expensive. The data is, you have to ingest it into the data warehouse in order to query it. And then it's almost impossible to get off of these systems, right? It takes an enormous effort, tremendous cost to get off of them. And so you're kinda locked in and that's a big problem, right? You also, you're dependent on that one data warehouse vendor, right? You can only do things with that data that the warehouse vendor supports. And if you contrast that to data lakehouse and open architectures where the data is stored in entirely open formats. >>So things like par files and Apache iceberg tables, that means you can use any engine on that data. You can use s SQL Query Engine, you can use Spark, you can use flin. You know, there's a dozen different engines that you can use on that, both at the same time. But also in the future, if you ever wanted to try something new that comes out, some new open source innovation, some new startup, you just take it and point out the same data. So that data's now at the core, at the center of the architecture as opposed to some, you know, vendors logo. Yeah. >>Amazon seems to be bought into the Lakehouse concept. It has big announcements on day two about eliminating the ETL stage between RDS and Redshift. Do you see the cloud vendors as pushing this concept forward? >>Yeah, a hundred percent. I mean, I'm, I'm Amazon's a great, great partner of ours. We work with, you know, probably 10 different teams there. Everything from, you know, the S3 team, the, the glue team, the click site team, you know, everything in between. And, you know, their embracement of the, the, the lake house architecture, the fact that they adopted Iceberg as their primary table format. I think that's exciting as an industry. We're all coming together around standard, standard ways to represent data so that at the end of the day, companies have this benefit of being able to, you know, have their own data in their own S3 account in open formats and be able to use all these different engines without losing any of the functionality that they need, right? The ability to do all these interactions with data that maybe in the past you would have to move the data into a database or, or warehouse in order to do, you just don't have to do that anymore. Speaking >>Of functionality, talk about what's new this year with drio since we've seen you last. >>Yeah, there's a lot of, a lot of new things with, with Drio. So yeah, we now have full Apache iceberg support, you know, with DML commands, you can do inserts, updates, deletes, you know, copy into all, all that kind of stuff is now, you know, fully supported native part of the platform. We, we now offer kind of two flavors of dr. We have, you know, Dr. Cloud, which is our SaaS version fully hosted. You sign up with your Google or, you know, Azure account and, and, and you're up in, you're up and running in, in, in a minute. And then dral software, which you can self host usually in the cloud, but even, even even outside of the cloud. And then we're also very excited about this new idea of data as code. And so we've introduced a new product that's now in preview called Dr. >>Arctic. And the idea there is to bring the concepts of GI or GitHub to the world of data. So things like being able to create a branch and work in isolation. If you're a data scientist, you wanna experiment on your own without impacting other people, or you're a data engineer and you're ingesting data, you want to transform it and test it before you expose it to others. You can do that in a branch. So all these ideas that, you know, we take for granted now in the world of source code and software development, we're bringing to the world of data with Jamar. And when you think about data mesh, a lot of people talking about data mesh now and wanting to kind of take advantage of, of those concepts and ideas, you know, thinking of data as a product. Well, when you think about data as a product, we think you have to manage it like code, right? You have to, and that's why we call it data as code, right? The, all those reasons that we use things like GI have to build products, you know, if we wanna think of data as a product, we need all those capabilities also with data. You know, also the ability to go back in time. The ability to undo mistakes, to see who changed my data and when did they change that table. All of those are, are part of this, this new catalog that we've created. >>Are you talk about data as a product that's sort of intrinsic to the data mesh concept. Are you, what's your opinion of data mesh? Is the, is the world ready for that radically different approach to data ownership? >>You know, we are now in dozens of, dozens of our customers that are using drio for to implement enterprise-wide kind of data mesh solutions. And at the end of the day, I think it's just, you know, what most people would consider common sense, right? In a large organization, it is very hard for a centralized single team to understand every piece of data, to manage all the data themselves, to, you know, make sure the quality is correct to make it accessible. And so what data mesh is first and foremost about is being able to kind of federate the, or distribute the, the ownership of data, the governance of the data still has to happen, right? And so that is, I think at the heart of the data mesh, but thinking of data as kind of allowing different teams, different domains to own their own data to really manage it like a product with all the best practices that that we have with that super important. >>So we we're doing a lot with data mesh, you know, the way that cloud has multiple projects and the way that Jamar allows you to have multiple catalogs and different groups can kind of interact and share data among each other. You know, the fact that we can connect to all these different data sources, even outside your data lake, you know, with Redshift, Oracle SQL Server, you know, all the different databases that are out there and join across different databases in addition to your data lake, that that's all stuff that companies want with their data mesh. >>What are some of your favorite customer stories that where you've really helped them accelerate that data mesh and drive business value from it so that more people in the organization kind of access to data so they can really make those data driven decisions that everybody wants to make? >>I mean, there's, there's so many of them, but, you know, one of the largest tech companies in the world creating a, a data mesh where you have all the different departments in the company that, you know, they, they, they were a big data warehouse user and it kinda hit the wall, right? The costs were so high and the ability for people to kind of use it for just experimentation, to try new things out to collaborate, they couldn't do it because it was so prohibitively expensive and difficult to use. And so what they said, well, we need a platform that different people can, they can collaborate, they can ex, they can experiment with the data, they can share data with others. And so at a big organization like that, the, their ability to kind of have a centralized platform but allow different groups to manage their own data, you know, several of the largest banks in the world are, are also doing data meshes with Dr you know, one of them has over over a dozen different business units that are using, using Dremio and that ability to have thousands of people on a platform and to be able to collaborate and share among each other that, that's super important to these >>Guys. Can you contrast your approach to the market, the snowflakes? Cause they have some of those same concepts. >>Snowflake's >>A very closed system at the end of the day, right? Closed and very expensive. Right? I think they, if I remember seeing, you know, a quarter ago in, in, in one of their earnings reports that the average customer spends 70% more every year, right? Well that's not sustainable. If you think about that in a decade, that's your cost is gonna increase 200 x, most companies not gonna be able to swallow that, right? So companies need, first of all, they need more cost efficient solutions that are, you know, just more approachable, right? And the second thing is, you know, you know, we talked about the open data architecture. I think most companies now realize that the, if you want to build a platform for the future, you need to have the data and open formats and not be locked into one vendor, right? And so that's kind of another important aspect beyond that's ability to connect to all your data, even outside the lake to your different databases, no sequel databases, relational databases, and drs semantic layer where we can accelerate queries. And so typically what you have, what happens with data warehouses and other data lake query engines is that because you can't get the performance that you want, you end up creating lots and lots of copies of data. You, for every use case, you're creating a, you know, a pre-joy copy of that data, a pre aggregated version of that data. And you know, then you have to redirect all your data. >>You've got a >>Governance problem, individual things. It's expensive. It's expensive, it's hard to secure that cuz permissions don't travel with the data. So you have all sorts of problems with that, right? And so what we've done because of our semantic layer that makes it easy to kind of expose data in a logical way. And then our query acceleration technology, which we call reflections, which transparently accelerates queries and gives you subsecond response times without data copies and also without extracts into the BI tools. Cause if you start doing bi extracts or imports, again, you have lots of copies of data in the organization, all sorts of refresh problems, security problems, it's, it's a nightmare, right? And that just collapsing all those copies and having a, a simple solution where data's stored in open formats and we can give you fast access to any of that data that's very different from what you get with like a snowflake or, or any of these other >>Companies. Right. That, that's a great explanation. I wanna ask you, early this year you announced that your Dr. Cloud service would be a free forever, the basic DR. Cloud service. How has that offer gone over? What's been the uptake on that offer? >>Yeah, it, I mean it is, and thousands of people have signed up and, and it's, I think it's a great service. It's, you know, it's very, very simple. People can go on the website, try it out. We now have a test drive as well. If, if you want to get started with just some sample public sample data sets and like a tutorial, we've made that increasingly easy as well. But yeah, we continue to, you know, take that approach of, you know, making it, you know, making it easy, democratizing these kind of cloud data platforms and, and kinda lowering the barriers to >>Adoption. How, how effective has it been in driving sales of the enterprise version? >>Yeah, a lot of, a lot of, a lot of business with, you know, that, that we do like when it comes to, to selling is, you know, folks that, you know, have educated themselves, right? They've started off, they've followed some tutorials. I think generally developers, they prefer the first interaction to be with a product, not with a salesperson. And so that's, that's basically the reason we did that. >>Before we ask you the last question, I wanna just, can you give us a speak peek into the product roadmap as we enter 2023? What can you share with us that we should be paying attention to where Drum is concerned? >>Yeah. You know, actually a couple, couple days ago here at the conference, we, we had a press release with all sorts of new capabilities that we, we we just released. And there's a lot more for, for the coming year. You know, we will shortly be releasing a variety of different performance enhancements. So we'll be in the next quarter or two. We'll be, you know, probably twice as fast just in terms of rock qu speed, you know, that's in addition to our reflections and our career acceleration, you know, support for all the major clouds is coming. You know, just a lot of capabilities in Inre that make it easier and easier to use the platform. >>Awesome. Tomer, thank you so much for joining us. My last question to you is, if you had a billboard in your desired location and it was going to really just be like a mic drop about why customers should be looking at Drio, what would that billboard say? >>Well, DRIO is the easy and open data lake house and, you know, open architectures. It's just a lot, a lot better, a lot more f a lot more future proof, a lot easier and a lot just a much safer choice for the future for, for companies. And so hard to argue with those people to take a look. Exactly. That wasn't the best. That wasn't the best, you know, billboards. >>Okay. I think it's a great billboard. Awesome. And thank you so much for joining Poly Me on the program, sharing with us what's new, what some of the exciting things are that are coming down the pipe. Quite soon we're gonna be keeping our eye Ono. >>Awesome. Always happy to be here. >>Thank you. Right. For our guest and for Paul Gillin, I'm Lisa Martin. You're watching The Cube, the leader in live and emerging tech coverage.

(upbeat music) (air whooshing) >> Good afternoon, cloud nerds, and welcome back to beautiful Las Vegas, Nevada. We are at AWS re:invent day four. Afternoon of day four here on theCUBE. I'm Savannah Peterson, joined by my fabulous cohost, Paul Gillin. Paul, you look sharp today. How you doing? >> Oh, you're just as fabulous, Savannah. You always look sharp. >> I appreciate that. They pay you enough to keep me buttered up over here at- (Paul laughing) It's wonderful. >> You're holding up well. >> Yeah, thank you. I am excited about our next conversation. Two fabulous gentlemen. Please welcome Sam and Monty, welcome to the show. >> Thank you. >> And it was great. Of the PR 2%, the most interesting man alive. (Paul and Savannah laughing) >> In person. Yeah, yeah. >> In the flesh. Our favorite guests so far. So how's the show been for you guys? >> Sam: It's been phenomenal. >> Just spending a lot of time with customers and partners and AWS. It's been great. It's been great. >> It is great. It's really about the community. It feels good to be back. >> Monty: Eating good food, getting my steps in above goals. >> I feel like the balance is good. We walk enough of these convention centers that you can enjoy the libations and the delicious food that's in Las Vegas and still not go home feeling like a cow. It is awesome. It's a win-win. >> To Sam's point though, meeting with customers, meeting with other technology providers that we may be able to partner with. And most importantly, in my role especially, meeting with all of our AWS key stakeholders in the partnership. So yeah, it's been great. >> Everyone's here. It's just different having a conversation in person. Even like us right now. So just in case folks aren't familiar, tell me about Talend. >> Yeah. Well, Talend is a data integration company. We've been around for a while. We have tons of different ways to get data from point A to point B, lots of different sources, lots of different connectors, and it's all about creating accessibility to that data. And then on top of that, we also have a number of solutions around governance, data health, data quality, data observability, which I think is really taking off. And so that's kind of how we're changing the business here. >> Casual change, data and governance. I don't know if anyone's talking about that at all on the snow floor. >> Been on big topic here. We've had a lot of conversations with the customers about that. >> So governance, what new dynamics has the cloud introduced into data governance? >> Well, I think historically, customers have been able to have their data on-prem. They put it into things like data lakes. And now having the flexibility to be able to bring that data to the clouds, it opens up a lot of doors, but it also opens up a lot of risks. So if you think about the chief data officer role, where you have, okay, I want to be able to bring my data to the users. I want to be able to do that at scale, operationally. But at the same time you have a tension then between the governance and the rules that really restrict the way that you can do that. Very strong tension between those two things. >> It really is a delicate balance. And especially as people are trying to accelerate and streamline their cloud projects, a lot to consider. How do you all help them do that? Monty, let's go to you. >> Yeah, we keep saying data, data, what is it really? It's ones and zeros. In this day and age, everything we see, we touch, we do, we either use data, or we create data, and then that... >> Savannah: We are data quite literally. >> We literally are data. And so then what you end up with is all these disparate data silos and different applications with different data, and how do you bring all that together? And that's where customers really struggle. And what we do is we bring it all together, and we make it actionable for the customer. We make it very simple for them to take the data, use it for the outcomes that they're looking for in their business initiatives. >> Expand on that. What do you mean make it actionable? Do you tag it? Do you organize it in some way? What's different about your approach? >> I mean, it's a really flexible platform. And I think we're part of a broader ecosystem. Even internally, we are a data driven company. Coming into the company in April, I was able to come in and get this realtime view of like, "Hey, here's where our teams are." And it's all in front of me in a Tableau dashboard that's populated from Talend integration, bringing data out of our different systems, different systems like Workday where we're giving offers out to people. And so everything from managing headcount to where our AWS spend is, all of that stuff. >> Now, we've heard a lot of talk about data and in fact the keynote yesterday that was focused mainly on data and getting data out of silos. How do you play with AWS in that role? Because AWS has other data integration partners. >> Sam: For sure. >> What's different about your relationship? Yeah. >> Go ahead. >> Yeah, we've had a strong relationship with AWS for many years now. We've got more than 80 connectors into the different AWS services. So we're not new to the AWS game. We align with the sales teams, we align with the partner teams, and then of course, we align with all the different business units and verticals so that we can enact that co-sell motion together with AWS. >> Sam: Yeah. And I think from our product standpoint, again, just being a hyper flexible platform, being able to put, again, any different type of source of data, to any type of different destination, so things like Redshift, being able to bring data into those cloud data warehouses is really how we do that. And then I think we have between bringing data from A to B, we're also able to do that along a number of different dimensions. Whether that's just like, "Hey, we just need to do this once a day to batch, all the way down to event driven things, streaming and the like. >> That customization must be really valuable for your customers as well. So one of the big themes of the show has been cost reduction. Obviously with the economic times as we're potentially dipping our toes into as well, is just in general, always wanting to increase margins. How do you help customers cut cost? >> Well, it's cost cutting, but it's also speed to market. The faster you can get a product to market, the faster you can help your customers. Let's say healthcare life sciences, pharmaceutical companies, patient outcomes. >> Great and timely example there. >> Patient outcomes, how do they get drugs to market quicker? Well, AstraZeneca leveraged our platform along with AWS. And they even said >> Cool. >> for every dollar that they spend on data initiatives, they get $40 back. That's a billion dollars >> Wow. >> savings by getting a drug to market one month faster. >> Everybody wins. >> How do you accelerate that process? >> Well, by giving them the right data, taking all the massive data that I mentioned, siloed in everywhere, and making it so that the data scientists can take all of this data and make use of it, makes sense of it, and move their drug production along much quicker. >> Yeah. And I think there's other things too like being very flexible in the way that it's deployed. Again, I think like you have this historical story of like, it takes forever for data to get updated, to get put together. >> Savannah: I need it now. And in context. >> And I think where we're coming from is almost more of a developer focus where your jobs are able to be deployed in any way you want. If you want to containerize those, you want to scale them, you need to schedule them that way. We plug into a lot of different ecosystems. I think that's a differentiation as well. >> I want to hang out on this one just for a second 'cause it's such a great customer success story and so powerful. I mean, in VC land, if you can take a dollar and make two, they'll give you a 10x valuation, 40. That is so compelling. I mean, do you think other customers could expect that kind of savings? A billion dollars is nothing to laugh at especially when we're talking about developing a vaccine. Yeah, go for it, Sam. >> It really depends on the use case. I think what we're trying to do is being able to say, "Hey, it's not just about cost cutting, but it's about tailoring the offerings." We have other customers like major fast food vendors. They have mobile apps and when you pull up that mobile app and you're going to do a delivery, they want to be able to have a customized offering. And it's not like mass market, 20% off. It's like, they want to have a very tailored offer to that customer or to that person that's pulling open that app. And so we're able to help them architect and bring that data together so that it's immediately available and reliable to be able to give those promotions. >> We had ARP on the show yesterday. We're talking about 50 million subscribers and how they customize each one of their experiences. We all want it to be about us. We don't want that generic at... Yeah, go for it, Paul. >> Oh, okay. >> Yeah. >> Well, I don't want to break break the rhythm here, but one area where you have differentiated, about two years ago you introduced something called the trust score. >> Sam: Yeah. >> Can you explain what that is and how that has resonated with your customers? >> Yeah, let's talk about this. >> Yeah, the thing about the trust score is, how many times have you gotten a set of data? And you look at it and you say, "Where did you get this data? Something doesn't look right here." And with the trust score, what we're able to do is quantify and value the different attributes of the data. Whether it's how much this is being used. We can profile the data, and we have a trust score that runs over time where you can actually then look at each of these data sets. You can look at aggregates of data sets to then say... If you're the data engineer, you can say, "Oh my, something has gone wrong with this particular dataset." Go in, quickly pull up the data. You can see if some third party integration has polluted your data source. I mean, this happens all the time. And I think if you sort of compare this to the engineering world, you're always looking to solve those problems sooner, earlier in the chain. You don't want your consumer calling you saying, "Hey, I've got a problem with the data, or I've got a problem- >> You don't want them to know there was ever a problem in theory. >> Yeah, the trust score helps those data engineers and those people that are taking care of the data address those problems sooner. >> How much data does somebody need to be able to get to the point where they can have a trust score? If you know what I'm trying to say. How do we train that? >> I mean, it can be all the way from just like a single data source that's getting updated, all the way to very large complex ones. That's where we've introduced this hierarchy of data sets. So it's not just like, "Hey, you've got a billion data sources here and here are the trust scores." But it's like, you can actually architect this to say like, "Okay, well, I have these data sets that belong to finance." And then finance will actually get, "Here's the trust score for these data sets that they rely on." >> What causes datasets to become untrustworthy? >> Yeah. Yeah. I mean, it happens all the time. >> A of different things, right? >> In my history, in the different companies that I've been at, on the product side, we have seen different integrations that maybe somebody changes something. In upstream, some of those integrations can actually be quite brittle. And as a consumer of that data, it's not necessarily your fault, but that data ends up getting put into your production database. All of a sudden your data engineering team is spending two days unwinding those transactions, fixing the data that's in there. And all the while, that bad data that's in your production system, is causing a problem for somebody that is ultimately relying on that. >> Is that usually a governance problem? >> I think governance is probably a separate set of constraints. This is sort of the tension between wanting to get all of the data available to your consumers versus wanting to have the quality around it as well. >> It's tough balance. And I think that it's really interesting. Everybody wants great data, and you could be making decisions that affect people's wellness, quite frankly. >> For sure. >> Very dramatically if you're ill-informed. So that's very exciting. >> To your point, we are all data. So if the data is bad, we're not going to get the outcomes that we want ultimately, >> I know. We certainly want the best outcomes for ourselves. >> We track that data health for its entire life cycle throughout the process. >> That's cool. And that probably increases your confidence in the trust score as well 'cause you're looking at so much data all the time. You got a smart thing going on over here. I like it. I like it a lot. >> We believe in it and so does AWS because they are a strong partner of ours, and so do customers. I think we mentioned we've had some phenomenal customer conversations along with- >> What a success story and case study. I want to dust your shoulders off right now if I wasn't tethered in. That's super impressive. So what's next for you all? >> Yeah, so I think we're going to continue down this path of data health and data governance. Again, I kind of talked about the... you're talking about data health being this differentiator on top of just moving the data around and being really good at that. I think you're also going to have different things around country level or state level governance, literal laws that you need to comply with. And so like- >> Savannah: CCPA- >> I mean, a long list- >> Oodles. Yeah. Yeah, yeah, yeah. >> I think we're going to be doing some interesting things there. We are continuing to proliferate the sources of data that we connect to. We're always looking for the latest and greatest things to put the data into. I think you're going to see some interesting things come out of that too. >> And we continue to grow our relationship with AWS, our already strong relationship. So you can procure Talend products to the AWS marketplace. We just announced Redshift serverless support for Talend. >> All their age. >> Which sounds amazing, but because we've been doing this for so long with AWS, dirty little secret, that was easy for us to do because we're already doing all this stuff. So we made the announcement and everyone was like, "Congratulations." Like, "Thanks." >> Look at you all. Full of the humble brags. I love it. >> Talend has gone through some twists and turns over the last couple of years. Company went private, was purchased by Thoma Bravo about a year and a half ago. At that time, your CEO said that it was a chance to really refocus the company on some core strategic initiatives and move forward. Both of you joined obviously after that happened. But what did you see about sort of the new Talend that attracted you, made you want to come over here? >> For sure. Yeah. I think, when I got a chance to talk to the board and talk to Chris, our chair, we talked about there being the growth thesis behind it. So I think Thoma been a great partner to Talend. I think we're able to do some things internally that would be I think, fairly challenging for companies that are in the public markets right now. I think especially, just a lot of pressure on different prices and the cost capital and all of that. >> Right now. >> That was a really casual way of stating that. But yeah, just a little pressure. >> Little bit of pressure. And who knows? Who knows how long that's going to last, right? But I think we've got a great board in place. They've been very strong strategic partner for us talking about all the different ways that we can grow. I think it's been a good partner for us- >> One of the strengths of Thoma's strategy is synergy between the companies they've acquired. >> Oh, for sure. >> They've acquired about 40 software companies. Are you seeing synergy? You talk to those other companies a lot? >> Yeah, so I have an operating partner. I talk with him on a weekly, sometimes daily basis. If we have questions or like, "Hey, what are you seeing in this space?" We can get plugged in to advisors very quickly. I think it's been a very helpful thing where... otherwise, you're relying on your personal network or things like that. >> This is why Monty was saying it was easy for you guys to go serverless. >> And we keep talking about trust, but in this case, Thoma Bravo really trusts our senior leadership team to make the right decisions that Sam and I are here making as we move forward. It's a great relationship. >> Sam: A good team. >> It sounds like it. All the love. I can feel the love even from you guys talking about it, it's genuine. You're not just getting paid to show this. That's fantastic. >> Are we getting paid for this or... >> Yeah. (Savannah giggling) (Paul laughing) I mean, some folks in the audience are probably going to want your autograph after this, although you get that a lot- >> Pictures are available after- >> Yeah, selfies are 10 bucks. That's how I get my boos budget. So last question for you. We have a challenge here on the theCUBE re:invent. We're looking for your 32nd hot take. Think of it as your thought leadership sizzle reel. Biggest takeaway, key themes from the show or looking forward into 2023? Sam, you're ready to rock, go. >> Yeah, totally. >> I think you're going to continue to hear the tension between being able to bring the data to the masses versus the simplicity and being able to do that in a way that is compliant with all the different laws, and then clean data. It's like a lot of different challenges that arise when you do this at scale. And so I think if you look at the things that AWS is announcing, I think you look at any sort of vendor in the data space are announcing, you see them sort of coming around to that set of ideas. Gives me a lot of confidence in the direction that we're going that we're doing the right stuff and we're meeting customers and prospects and partners, and everybody is like... We kind of get into this conversation and I'll say, "Yeah, that's it. We want to get involved in that." >> You can really feel the momentum. Yeah, it's true. It's great. What about you, Monty? >> I mean, I don't need 30 seconds. I mentioned it. >> Great. >> Between Talend and AWS, we're aligned from the sales teams to the product teams, the partner teams and the alliances. We're just moving forward and growing this relationship. >> I love it. That was perfect. And on that note, Sam, Monty, thank you so much for joining us. >> Yeah, thanks for having us. >> I'm sure your careers are going to continue to be rad at Talend and I can't wait to continue the conversation. >> Sam: Yeah, it's a great team. >> Yeah, clearly. I mean, look at you two. If you're any representation of the culture over there, they're doing something great. (Monty laughing) I thank all of you for tuning in to our nearly... Well, shoot. I think now over 100 interviews at AWS Reinvent in Sin City. We are hanging out here. Paul and I've got a couple more for you. So we hope to see you tuning in with Paul Gillin. I'm Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (upbeat music)

>>Welcome back to the Cube, as a continued coverage here from AWS Reinvent 22. It's day three of our coverage here at the Venetian in Las Vegas, and we're part of the AWS Global Startup Showcase. With me to talk about what Kong's to in that regard is Marco Palladino, who's the, the CTO and the co-founder of Con Marco. Good >>To see you. Well, thanks for having me >>Here. Yeah, I was gonna say, by the way, I, I, you've got a beautiful exhibit down on the show floor. How's the week been for you so far as an exhibitor here? >>It's been very busy. You know, to this year we made a big investment at the WS reinvent. You know, I think this is one of the best conferences in the industry. There is technology developers, but it's also business oriented. So you can learn about all the business outcomes that our, you know, customers or, you know, people are trying to make when, when adopting these new technologies. So it's very good so far. >>Good, good, good to hear. Alright, so in your world, the API world, you know, it used to be we had this, you know, giant elephant. Now we're cutting down the little pieces, right? That's right. We're all going micro now these days. That's right. Talk about that trend a little bit, what you're seeing, and we'll jump in a little deeper as to how you're addressing that. >>Well, I think the industry learned a long time ago that running large code bases is actually quite problematic when it comes to scaling the organization and capturing new opportunities. And so, you know, we're transitioning to microservices because we want to get more opportunities in our business. We want to be able to create new products, fasters, we want to be able to leverage existing services or data that we have built, like an assembly line of software, you know, picking up APIs that other developers are building, and then assemble them together to create new experiences or new products, enter new markets. And so microservices are fantastic for that, except microservices. They also introduce significant concerns on the networking layer, on the API layer. And so this is where Kong specializes by providing API infrastructure to our customers. >>Right. So more about the problems, more about the challenges there, because you're right, it, opportunities always create, you know, big upside and, and I, I don't wanna say downside, but they do introduce new complexities. >>That's right. And introducing new complexity. It's a little bit the biggest enemy of any large organization, right? We want to reduce complexity, we want to move faster, we want to be more agile, and, and we need an API vision to be able to do that. Our teams, you know, I'm speaking with customers here at Reinvent, they're telling me that in the next five years, the organization is going to be creating more APIs than all the APIs they've created up until now. Right? So how do you >>Support, that's a mind boggling number, right? >>It's mind boggling. Yeah, exactly. How do you support that type of growth? And things have been moving so fast. I feel like there is a big dilemma in, you know, with certain organizations where, you know, we have not taught a long term strategy for APIs, whereas we do have a long term strategy for our business, but APIs are running the business. We must have a long term strategy for our APIs, otherwise we're not gonna be able to execute. And that's a big dilemma right now. Yeah. >>So, so how do we get the horse back in front of the cart then? Because it's like you said, it's almost as if we've, we're, we're reprioritizing, you know, incorrectly or inaccurately, right? You're, you're getting a little bit ahead of ourselves. >>Well, so, you know, whenever we have a long-term strategy for pretty much anything in the organization, right? We know what we want to do. We know the outcome that we want to achieve. We work backwards to, you know, determine what are the steps that are gonna bring us there. And, and the responsibility for thinking long term in, in every organization, including for APIs at the end of the day, always falls on the leaders and the should on the shoulders of the leadership and, and to see executives of the organization, right? And so we're seeing, you know, look at aws by the way. Look at Amazon. This conference would not have been possible without a very strong API vision from Amazon. And the CEO himself, Jeff Bezos, everybody talks about wanting to become an API first organization. And Amazon did that with the famous Jeff Bezos mandate today, aws, it's a hundred billion revenue for Amazon. You see, Amazon was not the first organization with, with an e-commerce, but if it was the first one that married a very strong e-commerce business execution with a very strong API vision, and here we are. >>So yeah, here we are putting you squarely in, in, in a pretty good position, right? In terms of what you're offering to the marketplace who has this high demand, you see this trend starting to explode. The hockey sticks headed up a little bit, right? You know, how are you answering that call specifically at how, how are you looking at your client's needs and, and trying to address what they need and when they need it, and how they need it. Because everybody's in a kind of a different place right now. >>Right? That's exactly right. And so you have multiple teams at different stages of their journey, right? With technology, some of them are still working on legacy, some of them are moving to the cloud. Yep. Some of them are working in containers and in microservices and Kubernetes. And so how do you, how do we provide an API vision that can fulfill the needs of the entire organization in such a way that we reduce that type of fragmentation and we don't introduce too much complexity? Well, so at con, we do it by essentially splitting the API platform in three different components. Okay. One is API management. When, whenever we want to expose APIs internally or to an ecosystem of partners, right? Or to mobile, DRA is a service mesh. You know, as we're splitting these microservices into smaller parts, we have a lot of connectivity, all, you know, across all the services that the teams are building that we need to, to manage. >>You know, the network is unreliable. It's by default, not secure, not observable. There is nothing that that works in there. And so how do we make that network reliable without asking our teams to go and build these cross-cut concerns whenever they create a new service. And so we need a service match for that, right? And then finally, we could have the best AP infrastructure in the world, millions of APIs and millions of microservices. Everything is working great. And with no API consumption, all of that would be useless. The value of our APIs and the value of our infrastructure is being driven by the consumption that we're able to drive to all of these APIs. And so there is a whole area of API productivity and discovery and design and testing and mocking that enables the application teams to be successful with APIs, even when they do have a, the proper API infrastructure in place that's made of meshes and management products and so on and so forth. Right. >>Can you gimme some examples? I mean, at least with people that you've been working with in terms of addressing maybe unique needs. Cuz again, as you've addressed, journeys are in different stages now. Some people are on level one, some people are on level five. So maybe just a couple of examples Yeah. Of clients with whom you've been working. Yeah, >>So listen, I I was talking with many organizations here at AWS Reinvent that are of course trying to migrate to the cloud. That's a very common common transformation that pretty much everybody's doing in the world. And, and how do you transition to the cloud by de-risking the migration while at the same time being able to get all the benefits of, of running in the cloud? Well, we think that, you know, we can do that in two, two ways. One, by containerizing our workloads so that we can make them portable. But then we also need to lift and shift the API connectivity in such a way that we can determine how much traffic goes to the legacy and how much traffic goes to the new cloud infrastructure. And by doing that, we're able to deal with some of these transformations that can be quite complex. And then finally, API infrastructure must support every team in the organization. >>And so being able to run on a single cloud, multi-cloud, single cluster, multi cluster VMs containers, that's important and essential because we want the entire organization to be on board. Because whenever we do not do that, then the developers will make short term decisions that are not going to be fitting into the organizational outcomes that we want to achieve. And we look at any outcome that your organization wants to achieve the cloud transformation, improving customer retention, creating new products, being more agile. At the end of the day, there is an API that's powering that outcome. >>Right? Right. Well, and, and there's always a security component, right? That you have to be concerned about. So how are you raising that specter with your clients to make them aware? Because sometimes it, I wouldn't say it's an afterthought, but sometimes it's not the first thought. And, and obviously with APIs and with their integral place, you know, in, in the system now security's gotta be included in that, right? >>API security is perhaps the biggest, biggest request that we're hearing from customers. You know, 83% of the world's internet traffic at the end of the day runs on APIs, right? That's a lot of traffic. As a matter of fact, APIs are the first attack vector for any, you know, malicious store party. Whenever there is a breach, APIs must be secured. And we can secure APIs on different layers of our infrastructure. We can secure APIs at the L four mesh layer by implementing zero trust security, for example, encrypting all the traffic, assigning an identity to every service, removing the concept of trust from our systems because trust is exploitable, right? And so we need to remove the cut zero trust, remove the concept of trust, and then once we have that underlying networking that's being secure and encrypted, we want to secure access to our APIs. >>And so this is the typical authentication, authorization concerns. You know, we can use patterns like op, op or opa open policy agent to create a security layer that does not rely on the team's writing code every time they're creating a new service. But the infrastructure is enforcing the type of layer. So for example, last week I was in Sweden, as a matter of fact speaking with the largest bank in Sweden while our customers, and they were telling us that they are implementing GDPR validation in the service mesh on the OPPA layer across every service that anybody's building. Why? Well, because you can embed the GDPR settings of the consumer into a claim in a gel token, and then you can use OPPA to validate in a blanket way that Jo Token across every service in the mesh, developers don't have to do that. It just comes out of the box like that. And then finally, so networking, security, API security for access and, and management of those APIs. And then finally we have deep inspection of our API traffic. And here you will see more exotic solutions for API security, where we essentially take a subset of our API traffic and we try to inspect it to see if there is anybody doing anything that they shouldn't be doing and, and perhaps block them or, you know, raise, raise, raise the flag, so to speak. >>Well, the answer is probably yes, they are. Somebody's trying to, somebody's trying to, yeah, you're trying to block 'em out. Before I let you go, you've had some announcements leading up here to the show that's just to hit a few of those highlights, if you would. >>Well, you know, Kong is an organization that you know, is very proud of the technology that we create. Of course, we started with a, with the API gateway Con Gateway, which was our first product, the most adopted gateway in the world. But then we've expanded our platform with service mesh. We just announced D B P F support in the service mesh. For example, we made our con gateway, which was already one of the fastest gateway, if not the fastest gateway out there, 30% faster with Con Gateway 3.0. We have shipped an official con operator for Kubernetes, both community and enterprise. And then finally we're doubling down on insomnia, insomnia's, our API productivity application that essentially connects the developers with the APIs that are creating and allows them to create a discovery mechanism for testing, mocking the bagging, those APIs, all of this, we of course ship it OnPrem, but then also on the cloud. And you know, in a cloud conference right now, of course, cloud, right? Right. Is a very important part of our corporate strategy. And our customers are asking us that. Why? Because they don't wanna manage the software, they want the API platform, they don't, don't wanna manage it. >>Well, no, nobody does. And there are a few stragglers, >>A few, a few. And for them there is the on-prem >>Platform. Fine, let 'em go. Right? Exactly. But if you wanna make it a little quick and dirty, hand it off, right? Oh, >>That's exactly right. Yes. >>Let Con do the heavy lifting for you. Hey Marco, thanks for the time. Yeah, thank you so much. We appreciate, and again, congratulations on what appears to be a pretty good show for you guys. Yeah, thank you. Well done. All right, we continue our discussions here at aws. Reinvent 22. You're watching the Cube, the leader in high tech coverage. >>Okay.

(lively music) >> Good afternoon cloud community and welcome back to beautiful Las Vegas, Nevada. We are here at AWS re:Invent, day four of our wall to wall coverage. It is day four in the afternoon and we are holding strong. I'm Savannah Peterson, joined by my fabulous co-host Paul Gillen. Paul, how you doing? >> I'm doing well, fine Savannah. You? >> You look great. >> We're in the home stretch here. >> Yeah, (laughs) we are. >> You still look fresh as a daisy. I don't know how you do it. >> (laughs) You're too kind. You're too kind, but I'm vain enough to take that compliment. I'm very excited about the conversation that we're going to have up next. We get to get a little DevRel and we got a little swagger on the stage. Welcome, Austin. How you doing? >> Hey, great to be here. Thanks for having me. >> Savannah: Yeah, it's our pleasure. How's the show been for you so far? >> Busy, exciting. Feels a lot like, you know it used to be right? >> Yeah, I know. A little reminiscent of the before times. >> Well, before times. >> Before we dig into the technical stuff, you're the most intriguingly dressed person we've had on the show this week. >> Austin: I feel extremely underdressed. >> Well, and we were talking about developer fancy. Talk to me a little bit about your approach to fashion. Wasn't expecting to lead with this, but I like this but I like this actually. >> No, it's actually good with my PR. You're going to love it. My approach, here's the thing, I give free advice all the time about developer relations, about things that work, have worked, and don't work in community and all that stuff. I love talking about that. Someone came up to me and said, "Where do you get your fashion tips from? What's the secret Discord server that I need to go on?" I'm like, "I will never tell." >> Oh, okay. >> This is an actual trait secret. >> Top secret. Wow! Talk about. >> If someone else starts wearing the hat, then everyone's going to be like, "There's so many white guys." Look, I'm a white guy with a beard that works in technology. >> Savannah: I've never met one of those. >> Exactly, there's none of them at all. So, you have to do something to kind stand out from the crowd a little bit. >> I love it, and it's a talk trigger. We're talking about it now. Production team loved it. It's fantastic. >> It's great. >> So your DevRel for Lightstep, in case the audience isn't familiar tell us about Lightstep. >> So Lightstep is a cloud native observability platform built at planet scale, and it powers observability at some places you've heard of like Spotify, GitHub, right? We're designed to really help developers that are working in the cloud with Kubernetes, with these huge distributed systems, understand application performance and being able to find problems, fix problems. We're also part of the ServiceNow family and as we all know ServiceNow is on a mission to help the world of work work better by powering digital transformation around IT and customer experiences for their many, many, many global 2000 customers. We love them very much. >> You know, it's a big love fest here. A lot of people have talked about the collaboration, so many companies working together. You mentioned unified observability. What is unified observability? >> So if you think about a tradition, or if you've heard about this traditional idea of observability where you have three pillars, right? You have metrics, and you have logs, and you have traces. All those three things are different data sources. They're picked up by different tools. They're analyzed by different people for different purposes. What we believe and what we're working to accomplish right now is to take all that and if you think those pillars, flip 'em on their side and think of them as streams of data. If we can take those streams and integrate them together and let you treat traces and metrics and logs not as these kind of inviolate experiences where you're kind of paging between things and going between tab A to tab B to tab C, and give you a standard way to query this, a standard way to display this, and letting you kind of find the most relevant data, then it really unlocks a lot of power for like developers and SREs to spend less time like managing tools. You know, figuring out where to build their query or what dashboard to check, more just being able to like kind of ask a question, get an answer. When you have an incident or an outage that's the most important thing, right? How quickly can you get those answers that you need so that you can restore system health? >> You don't want to be looking in multiple spots to figure out what's going on. >> Absolutely. I mean, some people hear unified observability and they go to like tool consolidation, right? That's something I hear from a lot of our users and a lot of people in re:Invent. I'll talk to SREs, they're like, "Yeah, we've got like six or seven different metrics products alone, just on services that they cover." It is important to kind of consolidate that but we're really taking it a step lower. We're looking at the data layer and trying to say, "Okay, if the data is all consistent and vendor neutral then that gives you flexibility not only from a tool consolidation perspective but also you know, a consistency, reliability. You could have a single way to deploy your observability out regardless of what cloud you're on, regardless if you're using Kubernetes or Fargate or whatever else. or even just Bare Metal or EC2 Bare Metal, right? There's been so much historically in this space. There's been a lot of silos and we think that unify diversability means that we kind of break down those silos, right? The way that we're doing it primarily is through a project called OpenTelemetry which you might have heard of. You want to talk about that in a minute? . >> Savannah: Yeah, let's talk about it right now. Why don't you tell us about it? Keep going, you're great. You're on a roll. >> I am. >> Savannah: We'll just hang out over here. >> It's day four. I'm going to ask the questions and answer the questions. (Savannah laughs) >> Yes, you're right. >> I do yeah. >> Open Tele- >> OpenTelemetry . >> Explain what OpenTelemetry is first. >> OpenTelemetry is a CNCF project, Cloud Native Computing Foundation. The goal is to make telemetry data, high quality telemetry data, a builtin feature of cloud native software right? So right now if you wanted to get logging data out, depending on your application stack, depending on your application run time, depending on language, depending on your deployment environment. You might have a lot... You have to make a lot of choices, right? About like, what am I going to use? >> Savannah: So many different choices, and the players are changing all the time. >> Exactly, and a lot of times what people will do is they'll go and they'll say like, "We have to use this commercial solution because they have a proprietary agent that can do a lot of this for us." You know? And if you look at all those proprietary agents, what you find very quickly is it's very commodified right? There's no real difference in what they're doing at a code level and what's stopped the industry from really adopting a standard way to create this logs and metrics and traces, is simply just the fact that there was no standard. And so, OpenTelemetry is that standard, right? We've got dozens of companies many of them like very, many of them here right? Competitors all the same, working together to build this open standard and implementation of telemetry data for cloud native software and really any software right? Like we support over 12 languages. We support Kubernetes, Amazon. AWS is a huge contributor actually and we're doing some really exciting stuff with them on their Amazon distribution of OpenTelemetry. So it's been extremely interesting to see it over the past like couple years go from like, "Hey, here's this like new thing that we're doing over here," to really it's a generalized acceptance that this is the way of the future. This is what we should have been doing all along. >> Yeah. >> My opinion is there is a perception out there that observability is kind of a commodity now that all the players have the same set of tools, same set of 15 or 17 or whatever tools, and that there's very little distinction in functionality. Would you agree with that? >> I don't know if I would characterize it that way entirely. I do think that there's a lot of duplicated effort that happens and part of the reason is because of this telemetry data problem, right? Because you have to wind up... You know, there's this idea of table stakes monitoring that we talk about right? Table stakes monitoring is the stuff that you're having to do every single day to kind of make sure your system is healthy to be able to... When there's an alert, gets triggered, to see why it got triggered and to go fix it, right? Because everyone has the kind of work on that table stake stuff and then build all these integrations, there's very little time for innovation on top of that right? Because you're spending all your time just like working on keeping up with technology. >> Savannah: Doing the boring stuff to make sure the wheels don't fall off, basically. >> Austin: Right? What I think the real advantage of OpenTelemetry is that it really, from like a vendor perspective, like it unblocks us from having to kind of do all this repetitive commodified work. It lets us help move that out to the community level so that... Instead of having to kind of build, your Kubernetes integration for example, you can just have like, "Hey, OpenTelemetry is integrated into Kubernetes and you just have this data now." If you are a commercial product, or if you're even someone that's interested in fixing a, scratching a particular itch about observability. It's like, "I have this specific way that I'm doing Kubernetes and I need something to help me really analyze that data. Well, I've got the data now I can just go create a project. I can create an analysis tool." I think that's what you'll see over time as OpenTelemetry promulgates out into the ecosystem is more people building interesting analysis features, people using things like machine learning to analyze this large amount, large and consistent amount of OpenTelemetry data. It's going to be a big shakeup I think, but it has the potential to really unlock a lot of value for our customers. >> Well, so you're, you're a developer relations guy. What are developers asking for right now out of their observability platforms? >> Austin: That's a great question. I think there's two things. The first is that they want it to just work. It's actually the biggest thing, right? There's so many kind of... This goes back to the tool proliferation, right? People have too much data in too many different places, and getting that data out can still be really challenging. And so, the biggest thing they want is just like, "I want something that I can... I want a lot of these questions I have to ask, answered already and OpenTelemetry is going towards it." Keep in mind it's the project's only three years old, so we obviously have room to grow but there are people running it in production and it works really well for them but there's more that we can do. The second thing is, and this isn't what really is interesting to me, is it's less what they're asking for and more what they're not asking for. Because a lot of the stuff that you see people, saying around, "Oh, we need this like very specific sort of lower level telemetry data, or we need this kind of universal thing." People really just want to be able to get questions or get questions answered, right? They want tools that kind of have these workflows where you don't have to be an expert because a lot of times this tooling gets locked behind sort of is gate kept almost in a organization where there are teams that's like, "We're responsible for this and we're going to set it up and manage it for you, and we won't let you do things outside of it because that would mess up- >> Savannah: Here's your sandbox and- >> Right, this is your sandbox you can play in and a lot of times that's really useful and very tuned for the problems that you saw yesterday, but people are looking at like what are the problems I'm going to get tomorrow? We're deploying more rapidly. We have more and more intentional change happening in the system. Like it's not enough to have this reactive sort of approach where our SRE teams are kind of like or this observability team is building a platform for us. Developers want to be able to get in and have these kind of guided workflows really that say like, "Hey, here's where you're starting at. Let's get you to an answer. Let's help you find the needle in the haystack as it were, without you having to become a master of six different or seven different tools." >> Savannah: Right, and it shouldn't be that complicated. >> It shouldn't be. I mean we've certainly... We've been working on this problem for many years now, starting with a lot of our team that started at Google and helped build Google's planet scale monitoring systems. So we have a lot of experience in the field. It's actually one... An interesting story that our founder or now general manager tells BHS, Ben Sigelman, and he told me this story once and it's like... He had built this really cool thing called Dapper that was a tracing system at Google, and people weren't using it. Because they were like, "This is really cool, but I don't know how to... but it's not relevant to me." And he's like, the one thing that we did to get to increase usage 20 times over was we just put a link. So we went to the place that people were already looking for that data and we added a link that says, "Hey, go over here and look at this." It's those simple connections being able to kind of draw people from like point A to point B, take them from familiar workflows into unfamiliar ones. You know, that's how we think about these problems right? How is this becoming a daily part of someone's usage? How is this helping them solve problems faster and really improve their their life? >> Savannah: Yeah, exactly. It comes down to quality of life. >> Warner made the case this morning that computer architecture should be inherently event-driven and that we are moving toward a world where the person matters less than what the software does, right? The software is triggering events. Does this complicate observability or simplify it? >> Austin: I think that at the end of the day, it's about getting the... Observability to me in a lot of ways is about modeling your system, right? It's about you as a developer being able to say this is what I expect the system to do and I don't think the actual application architecture really matters that much, right? Because it's about you. You are building a system, right? It can be event driven, can be support request response, can be whatever it is. You have to be able to say, "This is what I expect to... For these given inputs, this is the expected output." Now maybe there's a lot of stuff that happens in the middle that you don't really care about. And then, I talk to people here and everyone's talking about serverless right? Everyone... You can see there's obviously some amazing statistics about how many people are using Lambda, and it's very exciting. There's a lot of stuff that you shouldn't have to care about as a developer, but you should care about those inputs and outputs. You will need to have that kind of intermediate information and understand like, what was the exact path that I took through this invented system? What was the actual resources that were being used? Because even if you trust that all this magic behind the scenes is just going to work forever, sometimes it's still really useful to have that sort of lower level abstraction, to say like, "Well, this is what actually happened so that I can figure out when I deployed a new change, did I make performance better or worse?" Or being able to kind of segregate your data out and say like... Doing AB testing, right? Doing canary releases, doing all of these things that you hear about as best practices or well architected applications. Observability is at the core of all that. You need observability to kind of do any of, ask any of those higher level interesting questions. >> Savannah: We are here at ReInvent. Tell us a little bit more about the partnership with AWS. >> So I would have to actually probably refer you to someone at Service Now on that. I know that we are a partner. We collaborate with them on various things. But really at Lightstep, we're very focused on kind of the open source part of this. So we work with AWS through the OpenTelemetry project, on things like the AWS distribution for OpenTelemetry which is really... It's OpenTelemetry, again is really designed to be like a neutral standard but we know that there are going to be integrators and implementers that need to package up and bundle it in a certain way to make it easy for their end users to consume it. So that's what Amazon has done with ADOT which is the shortening for it. So it's available in several different ways. You can use it as like an SDK and drop it into your application. There's Lambda layers. If you want to get Lambda observability, you just add this extension in and then suddenly you're getting OpenTelemetry data on the other side. So it's really cool. It's been a really exciting to kind of work with people on the AWS side over the past several years. >> Savannah: It's exciting, >> I've personally seen just a lot of change. I was talking to a PM earlier this week... It's like, "Hey, two years ago I came and talked to you about OpenTelemetry and here we are today. You're still talking about OpenTelemetry." And they're like, "What changes?" Our customers have started coming to us asking for OpenTelemetry and we see the same thing now. >> Savannah: Timing is right. >> Timing is right, but we see the same thing... Even talking to ServiceNow customers who are... These very big enterprises, banks, finance, healthcare, whatever, telcos, it used to be... You'd have to go to them and say like, "Let me tell you about distributed tracing. Let me tell you about OpenTelemetry. Let me tell you about observability." Now they're coming in and saying, "Yeah, so we're standard." If you think about Kubernetes and how Kubernetes, a lot of enterprises have spent the past five-six years standardizing, and Kubernetes is a way to deploy applications or manage containerized applications. They're doing the same journey now with OpenTelemetry where they're saying, "This is what we're betting on and we want partners we want people to help us go along that way." >> I love it, and they work hand in hand in all CNCF projects as well that you're talking about. >> Austin: Right, so we're integrated into Kubernetes. You can find OpenTelemetry and things like kept in which is application standards. And over time, it'll just like promulgate out from there. So it's really exciting times. >> A bunch of CNCF projects in this area right? Prometheus. >> Prometheus, yeah. Yeah, so we inter-operate with Prometheus as well. So if you have Prometheus metrics, then OpenTelemetry can read those. It's a... OpenTelemetry metrics are like a super set of Prometheus. We've been working with the Prometheus community for quite a while to make sure that there's really good compatibility because so many people use Prometheus you know? >> Yeah. All right, so last question. New tradition for us here on theCUBE. We're looking for your 32nd hot take, Instagram reel, biggest theme, biggest buzz for those not here on the show floor. >> Oh gosh. >> Savannah: It could be for you too. It could be whatever for... >> I think the two things that are really striking to me is one serverless. Like I see... I thought people were talking about servers a lot and they were talking about it more than ever. Two, I really think it is observability right? Like we've gone from observability being kind of a niche. >> Savannah: Not that you're biased. >> Huh? >> Savannah: Not that you're biased. >> Not that I'm biased. It used to be a niche. I'd have to go niche thing where I would go and explain what this is to people and nowpeople are coming up. It's like, "Yeah, yeah, we're using OpenTelemetry." It's very cool. I've been involved with OpenTelemetry since the jump, since it was started really. It's been very exciting to see and gratifying to see like how much adoption we've gotten even in a short amount of time. >> Yeah, absolutely. It's a pretty... Yeah, it's been a lot. That was great. Perfect soundbite for us. >> Austin: Thanks, I love soundbites. >> Savannah: Yeah. Awesome. We love your hat and your soundbites equally. Thank you so much for being on the show with us today. >> Thank you for having me. >> Savannah: Hey, anytime, anytime. Will we see you in Amsterdam, speaking of KubeCon? Awesome, we'll be there. >> There's some real exciting OpenTelemetry stuff coming up for KubeCon. >> Well, we'll have to get you back on theCUBE. (talking simultaneously) Love that for us. Thank you all for tuning in two hour wall to wall coverage here, day four at AWS re:Invent in fabulous Las Vegas, Nevada, with Paul Gillin. I'm Savannah Peterson and you're watching theCUBE, the leader in high tech coverage. (lively music)