Boomi World 2024 | Ann Maya | Boomi & Ranjan Parthasarathy | Apica

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Ann Maya

EMEA CTO

Boomi

Ranjan Parthasarathy

CPTO

Apica

Apica integrates complex data management capabilities to enhance application observability

Application monitoring and data management startup Apica AB has finally delivered on the integrations it promised following its recent acquisitions of Logiq.ai LLC and Circonus Inc.The company said today that its newly enhanced products will provide deeper insights into data management and make it easier for customers to benefit from them. The revamped Apica Ascent 2.0 platform comes as the company announced it has closed on a $16 million funding round, saying it will use the money to expand the office of its chief technology officer and add to its engineering, research and product development teams

Boomi partners with Apica for improved runtime observability and workflow health management

The surge in artificial intelligence has brought with it a need for improved data monitoring across multiple operating environments. Application programming interfaces are one such scenario.In keeping with the company ethos of accelerating digital connectedness through its platform, Boomi LP has allied itself with Apica Inc. for cellular-level runtime observability as enterprises leverage data resources for artificial intelligence

play_circle_outline 3. Discussion about the partnership between Apica and Boomi for data management

play_circle_outline 4. Explanation of Runtime Observability and its role in ensuring continuous system performance

play_circle_outline 7. Focus on integrations, data management, and API management in the iPaaS market

play_circle_outline Example of a customer using data in greenhouse management

Info

Ann Maya | Boomi & Ranjan Parthasarathy | Apica

Ann Maya

EMEA CTO Boomi

Ranjan Parthasarathy

CPTO Apica

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>> Good afternoon, cloud community, and welcome back to Denver, Colorado. My name's Savannah Peterson here, joined by John Furrier for day two of Boomi World 2024. It's been a really cool show so far.

>> Boomi has great customers, they've got great software, and we're going to hear more about how those acquisitions are rolling out on the big keynote today. It's great news.

>> Yeah, a lot of great news. Speaking of, we've got, Ron, John and Ann here. We've got and Boomi. You were on stage earlier, Ann. First of all, welcome, both of you. Thank you for taking the time. And can you give us a little recap of that awesome keynote that you did earlier?

Ann Maya

>> Sure. And just to clarify, our CEO did a keynote, Steve Lucas. I won't take his credit. I did come on board on stage.

>> You were on there. That counts.

Ann Maya

>> I was on the stage, sure.

>> I was going to say, come on. I think Steve would want to-

>> You did the demos, were great.

>> Yeah, exactly.

Ann Maya

>> I did the demo, so I was like, yeah, the demo monkey.

>> You were co-leading there for a nice

Ann Maya

>> Yeah, we are pretty excited here at Boomi. We have had such amazing innovation and growth over the last few years, and that's one of the things Steve was talking about, that power connectivity, how as humans, we've been drawn and that endurance and connectivity. But also through our platform, we've been able to accelerate and help through the digital connectedness. Now, we're seeing AI coming on board, and it's really putting a lot of fire into spaces like data management and API management and processes and applications that we know are just proliferating at break next week. So we're advancing our vision and our platform in those areas, and we made some pretty exciting announcements today about our API solution.

>> You got great M&A going on. You got organic innovation on the product side. We've been covering you guys for many years. I love the interface, by the way. You guys always did great on the interface, a lot easier to use.

Ann Maya

>> That is so good to hear, because that was an old, I probably shouldn't say this, but a complaint that we used to get. So it was like they're telling us our baby's ugly, and we didn't like it.

>> Compared to some ugly babies out there, I've seen some other model gardens and some other gardens with-

Ann Maya

>> But now, it's just like you saying your baby's beautiful, so thank you.

>> I'm very impressed, and the demos are fantastic. It's a great innovation. M&A brings on a new category, setting the table for the innovation that's going to come with the new category of generative AI. Customers have their own workflows, they have their own data. That's the new IP. We've been reporting that. But also the ecosystem, we did this little Savannah on the street reporting yesterday to some of your partners. You guys are here. The partnership ecosystem is booming, pun intended.

Ann Maya

>> Nicely done. Nicely done....

>> because of the integrations of team sport.

Ann Maya

>> Yes.

>> Could you guys explain the partnership piece and how that's working? Because this will be table stakes, having great partnerships in the GenAI era.

Ranjan Parthasarathy

>> Sure. Yeah, so I'll talk a little bit about the partnership that Apica is doing with Boomi. So Apica is a data management platform, and the partnership with Boomi is in particularly about observability data that we manage through our platform. It's an offering called Runtime Observability. It works with existing Boomi environments, so customers are already running workflows. And true to the mission of Boomi, which is connect everyone to everything, today, customers run complex workflows on critical mission critical applications inside the Boomi platform. What we are offering as part of RTO is the ability to now look inside these environments where we gather metrics around health and performance to make sure that these critical systems are always up and running.

>> So RTO is Runtime Observability,

Ranjan Parthasarathy

>> Observability.

>> Just make sure you get that.

Ranjan Parthasarathy

>> Yes.

>> Define that for us. What is Runtime Observability. Let's set the context up because this becomes a really interesting piece for what is turning out to be a real time market with GenAI because it's generating things. It's not like some program response. It's like generating at runtime. So take us through. What is Runtime Observability?

Ranjan Parthasarathy

>> Yeah, so Runtime Observability is a little bit different than pure GenAI. This is more around very operational bread and butter stuff. So you take a Boomi platform, you're running critical workloads, you're running it on servers, virtual machines. You need to make sure these are running at all times, right? There is no downtime. There is no impact to services. There is no impact to users. Now, ensuring that requires a couple of things, right? One is Boomi application itself giving insights, which they have in the form of Boomi Insights. But RTO now giving them insights into the runtime environment where you may be running the application in a Linux machine or a Windows machine. You want to see exactly what's happening inside the operating system. Things like network performance, memory usage, CPU usage, storage, file system, gather all of that in a way that it's streamlined, centralized, makes it easy for somebody to know what is working, what is failing. Are the failures coming from the application stack, or is it coming from the runtime stack? And that, what that means is meantime to root cause really comes down. So that is our fundamental offering in RTO. It's really a compliment to Boomi Insights, which is insights into their application.

>> And what's the impact of Boomi? You see stuff that they don't see or in their app? Explain the impact a little bit.

Ranjan Parthasarathy

>> Yeah, we see stuff that is more inside the runtime environment, which is not available through say the Boomi APIs or the Boomi user interface, but it's still critical for somebody to ensure that this is running up and running at all times.

>> And the benefits of the Boomi customer is more visibility into what's happening or-

Ann Maya

>> Well, as John mentioned, there's critical systems that are running on Boomi, and we say we run on anything anywhere. So any device that has... That's one of the specialties, our patented runtime. It can run absolutely anywhere. So we're collecting all that information, and we're passing that information, we're doing integrations. That's what's keeping that company running. Parts of their business are functional because they're running. So if those go down, that's problematic. With this technology here with our partners, they're able to provide insights, collecting a lot of the audit logs, collecting a lot of the information from the atoms themselves and the molecules, and being able to report out insights to help improve operational efficiency, to help improve how things are running, but also maybe predict what could be an issue later to make sure that everything hums.

>> So they're set up. If I translate that into what it means for, say GenAI is that the data's all there. So the data's kind of accessible. Okay, so the dots going forward, okay, I got a good operational efficiency. I'm seeing things. I got predictive analytics, good old school last year's

Ann Maya

>> If your run time's not running, you're not getting that data as quickly. Let's say it's an event and that data needs to come in real time. It's a real time event. If your run time is down, then you're not going to be able to get access to, just like if your API is down. So this is a way of making sure everything under the covers is working really well.

>> And working most efficiently all the time, it's essentially saving you money and optimizing your throughput and productivity at its simplest, right?

Ann Maya

>> Absolutely. Yeah.

>> And who doesn't want to do that?

>> Yeah.

>> Check utilization, that sort of thing. We have the customers who are using more and more, Steve mentioned it in his keynote, more and more processing every single day, like an incredible amount of transactions. So you can start to see, well, am I reaching the peak of my optimizing in this environment? Do I need to scale out? Maybe they become so efficient, they need to ramp down on certain things and they get savings there. So these are the type of insights that are super powerful, and especially in our world of AI where we know data's going to go up, the need to integrate is going to go up, the need to manage is going to go up.

>> It's nice when a plan comes together. I got to ask you about the iPaaS market because Boomi has been on the right track. And apps are involved, so you've got to manage all the runtime, all that good stuff. Now that you've got the generative AI, how's that next gen going to look? Because if you have iPaaS, which we are on our research team, we're showing that's going to be a compound annual growth rate, about 35% just in the traditional integrated platform as a service market, just straight up iPaaS. Now, you've got net new capabilities with the API management. You got the trusted brokering with the data piece, with the compliance and governance. Where does this go? What changes with iPaaS? I was just talking to an investor I saw floating in the hallways. He's like, "What's the TAM for Boomi?" I'm thinking that with your positioning, your TAM will increase from iPaaS. Or what's that next market? Or can you share your vision on what Steve was getting at on stage here?

Ann Maya

>> Yeah, what Steve was getting at is that we still have integration, tele-integration automation as our core functioning pillar, but we also do data management, and we also do API management. We're investing heavily in these adjacent technologies to not just be a part of Boomi integration, but to be stand-alone, rich services in and of themselves. So what we're seeing is that I think iPaaS today is a core function. It's no longer if, it's when. You must use it today. It's just no... Absolutely no way you would go back and try to do manual integrations anymore. Just you could not work at that pace. So we're saying the data management is going to become a very key aspect because data's a fuel for AI. And garbage in, garbage out if your data's not good. And I also think that data, the role of data will be like a force multiplier in AI. So as bad as your data is today, bad, bad.

>> Order of magnitude.

Ann Maya

>> Right. Exactly.

>> each level, and evolution either, whether that's a new tool, new tech, or at a different scale for a different solution, it just gets messier and more expensive extremely fast.

Ann Maya

>> Right.And also the garbage and garbage out, that's because you're talking about processes there, right? So there's process in the middle, it's using that data to come through. Do you want to introduce rust and dirt into your beautiful processes? Why would you do that? You're just going to create data out. And so that full multiplier will make you need to have better data.

>> And you mentioned you can run anywhere with Boomi. Obviously, let's take this to the next level with API management, and you got the partnerships. When you think about cloud, cloud operations, you got on-premises, you got public cloud and edge, all emerging. You have different clouds, different environments. Is the idea to be a agnostic, unified layer around this piece? Because some API gateways, there's tool sprawl out there. And 85% of all internet traffic is API traffic, so it's a natural evolution to say LLMs and foundation models will interface through say APIs. So the flip of the script is coming, or is it? Tell us through what's happening?

Ann Maya

>> I think you're absolutely spot on. So yes, the platform, the environment must be composable, and it must be agnostic, in my opinion. If you create an ecosystem where... I'm going to try not to use an example-

>> Go ahead. It's okay, we're live.

>> We like it.

>> Whatever you say will-

>> Your secret is safe with us and everyone watching.

Ann Maya

>> You create an app ecosystem, that means that you have to use a technology that fits with that ecosystem. You're tying your customers and your partners to potentially inferior technologies. We believe best of breed. Use whatever best works for you and your business. We will be the glue that holds it together, and we will help you manage it, and we will help you extend it through APIs as well. That's the whole concept of composability and agnostic in nature.

>> We won't call out any of the customers you can't call out just because of that. But I am curious, you see customers, both of you do, across different verticals. It feels like the whole world is trying to adopt AI in a way that's efficient and quick and actually practical at this stage. What are the other trends that you're seeing that maybe are either industry specific or that go across verticals? I'm going to start with you.

Ranjan Parthasarathy

>> So I think one of the things that we are noticing is around data engineering. And really, an openness there is becoming more and more critical because, and this is my view on it, is I think the integrations is the new cloud. That's what is happening.

>> That's good. I like that.

Ranjan Parthasarathy

>> Right? Integrations is the new cloud, and powering all of that has to be good data, right? And the more data gets locked down, the less valuable it gets. So openness on the data side, whether it's consumption collection, storage is something that we are seeing enterprises demand more and more, right? And so that's what I see as another trend, is data needs to be open on all fronts, collection, consumption, storage, right?

>> Well, the open source market's definitely pointing in that direction. You look at all the... Just on the foundation models alone, including the LLMs, open source, both velocity of adoption and size and performance is matching up with the proprietary models in open source. So you're starting to see LAMA3 coming out. That was killer reviews. That's open source. So then you've got the proprietary one. So as this comes together, they're going to have a lot of errors. So we reported on SiliconANGLE and our CUBE research team, this power law, we said a year ago that specialty models would become the core, where companies their own version of their data. That's well understood and frankly accurate. It's their data. It's proprietary to them, hence their new intellectual property. So do you guys agree that customers workloads and data is their new intellectual property? Because what you're saying here is that if you can automate that, you're going to turn that into new value, potentially if you have this the next level in the automation. So how should companies be... First of all, do you agree with that statement that that's an intellectual property of the company? And two, what should companies do to, one, not blow the opportunity of harvesting that value? What can they do to make sure that they preserve that opportunity?

Ann Maya

>> Yeah. Well, I think we mentioned garbage in, garbage out. We mentioned the garbage. But if your data is good, that force multiplier is great, right? So as you said, orders of magnitude better. And how do you make sure that you can take the richness of data that belongs to your business and that can give you that edge over your competitors? How do you do that? Well, we are looking at this concept of data management. That was the other piece Steve talked about on stage today, and the data management sphere of taking your multiple disparate data sources, combining that data together so you can find the golden source, the golden record in that, and then be able to leverage those insights in other parts of your business, so you can get better and better and better. That's how innovation is born, is being able to see something other people can't see. But people have data in their environment today that they can't see, or they have insights or nuggets of beautiful information or advantages that they're not privy to because they just don't... They have it in so many different places. So that's what we think is create that kind of data reservoir that we spoke about earlier where you're combining the data that sits in your environment protected within your virtual private cloud or your firewall or whatever, the data that you have, keep it secure, but leverage that to create that business

>> So you're saying optimize for data cleanliness upfront versus optimizing the managing of the cleanup if it gets dirty or it's bad data because bad data is hallucinations and or critical failure in an application, right?

Ranjan Parthasarathy

>> Yeah, so I think the one aspect here is it's not always that data has to be cleansed. The most important thing from a compliance perspective is data has to be in full fidelity as you collected it, right? Now, the views on that can change over time. Today, I may take care of 10 attributes from it. Tomorrow, I may need 50 attributes from it. It's the data engineering that ensures this over time where these views can change and seamlessly connect into systems where they need it the most, I think is where the future is.

>> Do you think data engineering's going to be a bigger position in companies, or is that going to be a position that's going to be human plus AI together or both? What's your view on-

>> Oh, good question....

>> data engineering.

>> Yeah.

Ranjan Parthasarathy

>> Yeah, I think data engineering will become kind of... And things like AI and analytics will get joined at the hip in some ways because there is no GenAI without good quality data engineering, right? And that's where data management, API integrations, these will become more and more critical. And that has to first come together before you can power gen AI use cases, right?

Ann Maya

>> I think there's going to be a democratization of data roles. So as you're able to collect insights, people in different parts of business will be able to see patterns that data engineers may not necessarily see because they don't have that lens of finance or that lens of customer marketing or sales that you're bringing to the fore. So it's a really great way of saying if you could create these insights in a data reservoir, or whatever, data lake, whatever your choice is, and be able to surface up those insights so people can find their own, and be able to slice and dice using, say a GenAI, that, to me, I think is pretty eye-opening.

>> You're describing an operating system, essentially a systems architecture that's going to enable value from the creators themselves, the creative workers or the productivity side of it.

>> Well, and from the different subject matter experts within an organization. What I'm hearing from you is if I'm a pediatric nurse, for example, I might be able to think about data from a different question or ask my AI or my situation a different question than if I'm in cardiology or if I'm on the admin side of the diabetic wing or whatever it might be. But to your point, it's allowing... I love what you just brought up. I think that's an extraordinarily pivotal point. We talk about the democratization of AI a lot, but we talk about it as access for entrepreneurs or for other people, but it's beyond that. It's empowering people across the business to be able to ask questions of the internal systems and data that's within those systems that they weren't able to ask before, because to your point, you couldn't see the dark murky corner of the lake and you didn't know it was over there underneath the little sea cave.

Ann Maya

>> Yeah, and it's an overwhelming thing. I'm going to have to go into PowerBI now. So I think, Savannah, you just explained it incredibly well. And we do have a customer that has been using Boomi systems for some time, and they run greenhouses. And the greenhouses are very digital, so they have little sensors on their plants and stuff, and they have growers, and they were able to maximize the growers associated with each certain level of crop. So they had few growers, and they could manage more crops because they were using the data coming in. And I remember a comment made by I think their CEO. He said, "Our growers are now becoming green data scientists," or something like that. So they're able to use this information, say oxidation levels, hydration, whatever, and be able to tweak how they're managing their crops by using the data. And the more they learned about it, the more they could ask questions, like you said.

>> You can grow your own, as they say, with the good data.

>> Yeah, you can. I want to sit here just for a second because I think it's really important, and I think this is part of how we make AI real. It's how we make some of these new technologies real, is by making... Not that the AI's going to take their job, but that it's going to make everyone's job involve this as a tool in a really empowering and exciting way. So you mentioned the growers turning into data engineers to a degree, green data engineers. When I was doing my homework on you, I love that we're both Washington Huskies. Go dogs.

Ann Maya

>> Yes.

>> And I also liked that you came back and studied computing in IT a little bit after you had gotten your original degree from UDub. This is obviously a really exciting time in tech, huge hype curve moment. What would be your advice to someone... And I want to hear this from both of you because you're both seeing a lot of different things. What would be your advice to someone who's hearing all the excitement and buzz right now, wants to upskill, maybe wants to come work for a company like Apica or Boomi? What would you tell them? Ann, I'm going to start with you.

Ann Maya

>> I'm always going to say this, follow your passion, follow what interests you. So I actually studied engineering, and well, I got a degree in psychology because I wanted to play in the sun. You know what it's like in Seattle. And I was stuck in a dark physics basement for so long. I'm like, I just want to finish. So I went, got a degree in psychology.

>> I love that. I studied communications pretty much for the same reason.

Ann Maya

>> But I went to cognitive psychology. What I was fascinated by was how the brain memory gets stored. And if you think about it, what I've noticed is patterns between how brain and memory is stored and how AI works. So if you follow your passion, things start to come together. The patterns start to come together. And I thought my background in psychology and cognitive psychology and understanding of memory systems has really helped me understand the future of technology and computing and how computing systems work.

>> And systems. Two Different systems, just one's a little silicon powered and one's a little emotionally powered. I love that.

Ann Maya

>> I think you can strive to be the absolute best at what you do if you're working in something you enjoy, and that's what I've always followed.

>> It's a tough act to follow. Do you have anything you want ?

Ranjan Parthasarathy

>> Yeah. So a couple of things. Passion is definitely something that drives you to do the things that you really care about. And figure out what you're good at. Double down on that. And one thing that I've learned over the years is always be a student because there is always something new happening out there. And if you are curious at all times, if you're willing to pick up on new things, you will evolve with how the world is evolving in a positive way, right? And at the end of the day, we need to be students all our life, and that's the best way I think anybody can kind of fulfill their ambitions, their aspirations in terms of what they want to do in their life.

>> Love that. Stay curious. Well, I know I speak for John and I when we both say we've enjoyed learning from both of you today. Ann, Ron, John, thank you so much for being here with us. We look forward to seeing how the partnership evolves as things continue to build momentum and hearing where you're off at the next Boomi World. John, thank you for hanging out. This was a great little chat.

>> My pleasure.

>> I know. It's always-

>> It was great....

>> insightful. I love when you get to learn from the guests and get some definitions for theCUBE's AI, like Runtime Observability. That's great. And thank all of you for tuning in wherever you are on this beautiful earth. We're here in Denver, Colorado. It's day two of Boomi World 2024. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news. We like to add one layer of difficulty just at the very end. Oh, we get a break.

>> So we think data engineering's going to be like an SRE-like job.

Ann Maya | Boomi & Ranjan Parthasarathy | Apica

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>> Good afternoon, cloud community, and welcome back to Denver, Colorado. My name's Savannah Peterson here, joined by John Furrier for day two of Boomi World 2024. It's been a really cool show so far.

>> Boomi has great customers, they've got great software, and we're going to hear more about how those acquisitions are rolling out on the big keynote today. It's great news.

>> Yeah, a lot of great news. Speaking of, we've got, Ron, John and Ann here. We've got and Boomi. You were on stage earlier, Ann. First of all, welcome, both of you. Thank you for taking the time. And can you give us a little recap of that awesome keynote that you did earlier?

Ann Maya

>> Sure. And just to clarify, our CEO did a keynote, Steve Lucas. I won't take his credit. I did come on board on stage.

>> You were on there. That counts.

Ann Maya

>> I was on the stage, sure.

>> I was going to say, come on. I think Steve would want to-

>> You did the demos, were great.

>> Yeah, exactly.

Ann Maya

>> I did the demo, so I was like, yeah, the demo monkey.

>> You were co-leading there for a nice

Ann Maya

>> Yeah, we are pretty excited here at Boomi. We have had such amazing innovation and growth over the last few years, and that's one of the things Steve was talking about, that power connectivity, how as humans, we've been drawn and that endurance and connectivity. But also through our platform, we've been able to accelerate and help through the digital connectedness. Now, we're seeing AI coming on board, and it's really putting a lot of fire into spaces like data management and API management and processes and applications that we know are just proliferating at break next week. So we're advancing our vision and our platform in those areas, and we made some pretty exciting announcements today about our API solution.

>> You got great M&A going on. You got organic innovation on the product side. We've been covering you guys for many years. I love the interface, by the way. You guys always did great on the interface, a lot easier to use.

Ann Maya

>> That is so good to hear, because that was an old, I probably shouldn't say this, but a complaint that we used to get. So it was like they're telling us our baby's ugly, and we didn't like it.

>> Compared to some ugly babies out there, I've seen some other model gardens and some other gardens with-

Ann Maya

>> But now, it's just like you saying your baby's beautiful, so thank you.

>> I'm very impressed, and the demos are fantastic. It's a great innovation. M&A brings on a new category, setting the table for the innovation that's going to come with the new category of generative AI. Customers have their own workflows, they have their own data. That's the new IP. We've been reporting that. But also the ecosystem, we did this little Savannah on the street reporting yesterday to some of your partners. You guys are here. The partnership ecosystem is booming, pun intended.

Ann Maya

>> Nicely done. Nicely done....

>> because of the integrations of team sport.

Ann Maya

>> Yes.

>> Could you guys explain the partnership piece and how that's working? Because this will be table stakes, having great partnerships in the GenAI era.

Ranjan Parthasarathy

>> Sure. Yeah, so I'll talk a little bit about the partnership that Apica is doing with Boomi. So Apica is a data management platform, and the partnership with Boomi is in particularly about observability data that we manage through our platform. It's an offering called Runtime Observability. It works with existing Boomi environments, so customers are already running workflows. And true to the mission of Boomi, which is connect everyone to everything, today, customers run complex workflows on critical mission critical applications inside the Boomi platform. What we are offering as part of RTO is the ability to now look inside these environments where we gather metrics around health and performance to make sure that these critical systems are always up and running.

>> So RTO is Runtime Observability,

Ranjan Parthasarathy

>> Observability.

>> Just make sure you get that.

Ranjan Parthasarathy

>> Yes.

>> Define that for us. What is Runtime Observability. Let's set the context up because this becomes a really interesting piece for what is turning out to be a real time market with GenAI because it's generating things. It's not like some program response. It's like generating at runtime. So take us through. What is Runtime Observability?

Ranjan Parthasarathy

>> Yeah, so Runtime Observability is a little bit different than pure GenAI. This is more around very operational bread and butter stuff. So you take a Boomi platform, you're running critical workloads, you're running it on servers, virtual machines. You need to make sure these are running at all times, right? There is no downtime. There is no impact to services. There is no impact to users. Now, ensuring that requires a couple of things, right? One is Boomi application itself giving insights, which they have in the form of Boomi Insights. But RTO now giving them insights into the runtime environment where you may be running the application in a Linux machine or a Windows machine. You want to see exactly what's happening inside the operating system. Things like network performance, memory usage, CPU usage, storage, file system, gather all of that in a way that it's streamlined, centralized, makes it easy for somebody to know what is working, what is failing. Are the failures coming from the application stack, or is it coming from the runtime stack? And that, what that means is meantime to root cause really comes down. So that is our fundamental offering in RTO. It's really a compliment to Boomi Insights, which is insights into their application.

>> And what's the impact of Boomi? You see stuff that they don't see or in their app? Explain the impact a little bit.

Ranjan Parthasarathy

>> Yeah, we see stuff that is more inside the runtime environment, which is not available through say the Boomi APIs or the Boomi user interface, but it's still critical for somebody to ensure that this is running up and running at all times.

>> And the benefits of the Boomi customer is more visibility into what's happening or-

Ann Maya

>> Well, as John mentioned, there's critical systems that are running on Boomi, and we say we run on anything anywhere. So any device that has... That's one of the specialties, our patented runtime. It can run absolutely anywhere. So we're collecting all that information, and we're passing that information, we're doing integrations. That's what's keeping that company running. Parts of their business are functional because they're running. So if those go down, that's problematic. With this technology here with our partners, they're able to provide insights, collecting a lot of the audit logs, collecting a lot of the information from the atoms themselves and the molecules, and being able to report out insights to help improve operational efficiency, to help improve how things are running, but also maybe predict what could be an issue later to make sure that everything hums.

>> So they're set up. If I translate that into what it means for, say GenAI is that the data's all there. So the data's kind of accessible. Okay, so the dots going forward, okay, I got a good operational efficiency. I'm seeing things. I got predictive analytics, good old school last year's

Ann Maya

>> If your run time's not running, you're not getting that data as quickly. Let's say it's an event and that data needs to come in real time. It's a real time event. If your run time is down, then you're not going to be able to get access to, just like if your API is down. So this is a way of making sure everything under the covers is working really well.

>> And working most efficiently all the time, it's essentially saving you money and optimizing your throughput and productivity at its simplest, right?

Ann Maya

>> Absolutely. Yeah.

>> And who doesn't want to do that?

>> Yeah.

>> Check utilization, that sort of thing. We have the customers who are using more and more, Steve mentioned it in his keynote, more and more processing every single day, like an incredible amount of transactions. So you can start to see, well, am I reaching the peak of my optimizing in this environment? Do I need to scale out? Maybe they become so efficient, they need to ramp down on certain things and they get savings there. So these are the type of insights that are super powerful, and especially in our world of AI where we know data's going to go up, the need to integrate is going to go up, the need to manage is going to go up.

>> It's nice when a plan comes together. I got to ask you about the iPaaS market because Boomi has been on the right track. And apps are involved, so you've got to manage all the runtime, all that good stuff. Now that you've got the generative AI, how's that next gen going to look? Because if you have iPaaS, which we are on our research team, we're showing that's going to be a compound annual growth rate, about 35% just in the traditional integrated platform as a service market, just straight up iPaaS. Now, you've got net new capabilities with the API management. You got the trusted brokering with the data piece, with the compliance and governance. Where does this go? What changes with iPaaS? I was just talking to an investor I saw floating in the hallways. He's like, "What's the TAM for Boomi?" I'm thinking that with your positioning, your TAM will increase from iPaaS. Or what's that next market? Or can you share your vision on what Steve was getting at on stage here?

Ann Maya

>> Yeah, what Steve was getting at is that we still have integration, tele-integration automation as our core functioning pillar, but we also do data management, and we also do API management. We're investing heavily in these adjacent technologies to not just be a part of Boomi integration, but to be stand-alone, rich services in and of themselves. So what we're seeing is that I think iPaaS today is a core function. It's no longer if, it's when. You must use it today. It's just no... Absolutely no way you would go back and try to do manual integrations anymore. Just you could not work at that pace. So we're saying the data management is going to become a very key aspect because data's a fuel for AI. And garbage in, garbage out if your data's not good. And I also think that data, the role of data will be like a force multiplier in AI. So as bad as your data is today, bad, bad.

>> Order of magnitude.

Ann Maya

>> Right. Exactly.

>> each level, and evolution either, whether that's a new tool, new tech, or at a different scale for a different solution, it just gets messier and more expensive extremely fast.

Ann Maya

>> Right.And also the garbage and garbage out, that's because you're talking about processes there, right? So there's process in the middle, it's using that data to come through. Do you want to introduce rust and dirt into your beautiful processes? Why would you do that? You're just going to create data out. And so that full multiplier will make you need to have better data.

>> And you mentioned you can run anywhere with Boomi. Obviously, let's take this to the next level with API management, and you got the partnerships. When you think about cloud, cloud operations, you got on-premises, you got public cloud and edge, all emerging. You have different clouds, different environments. Is the idea to be a agnostic, unified layer around this piece? Because some API gateways, there's tool sprawl out there. And 85% of all internet traffic is API traffic, so it's a natural evolution to say LLMs and foundation models will interface through say APIs. So the flip of the script is coming, or is it? Tell us through what's happening?

Ann Maya

>> I think you're absolutely spot on. So yes, the platform, the environment must be composable, and it must be agnostic, in my opinion. If you create an ecosystem where... I'm going to try not to use an example-

>> Go ahead. It's okay, we're live.

>> We like it.

>> Whatever you say will-

>> Your secret is safe with us and everyone watching.

Ann Maya

>> You create an app ecosystem, that means that you have to use a technology that fits with that ecosystem. You're tying your customers and your partners to potentially inferior technologies. We believe best of breed. Use whatever best works for you and your business. We will be the glue that holds it together, and we will help you manage it, and we will help you extend it through APIs as well. That's the whole concept of composability and agnostic in nature.

>> We won't call out any of the customers you can't call out just because of that. But I am curious, you see customers, both of you do, across different verticals. It feels like the whole world is trying to adopt AI in a way that's efficient and quick and actually practical at this stage. What are the other trends that you're seeing that maybe are either industry specific or that go across verticals? I'm going to start with you.

Ranjan Parthasarathy

>> So I think one of the things that we are noticing is around data engineering. And really, an openness there is becoming more and more critical because, and this is my view on it, is I think the integrations is the new cloud. That's what is happening.

>> That's good. I like that.

Ranjan Parthasarathy

>> Right? Integrations is the new cloud, and powering all of that has to be good data, right? And the more data gets locked down, the less valuable it gets. So openness on the data side, whether it's consumption collection, storage is something that we are seeing enterprises demand more and more, right? And so that's what I see as another trend, is data needs to be open on all fronts, collection, consumption, storage, right?

>> Well, the open source market's definitely pointing in that direction. You look at all the... Just on the foundation models alone, including the LLMs, open source, both velocity of adoption and size and performance is matching up with the proprietary models in open source. So you're starting to see LAMA3 coming out. That was killer reviews. That's open source. So then you've got the proprietary one. So as this comes together, they're going to have a lot of errors. So we reported on SiliconANGLE and our CUBE research team, this power law, we said a year ago that specialty models would become the core, where companies their own version of their data. That's well understood and frankly accurate. It's their data. It's proprietary to them, hence their new intellectual property. So do you guys agree that customers workloads and data is their new intellectual property? Because what you're saying here is that if you can automate that, you're going to turn that into new value, potentially if you have this the next level in the automation. So how should companies be... First of all, do you agree with that statement that that's an intellectual property of the company? And two, what should companies do to, one, not blow the opportunity of harvesting that value? What can they do to make sure that they preserve that opportunity?

Ann Maya

>> Yeah. Well, I think we mentioned garbage in, garbage out. We mentioned the garbage. But if your data is good, that force multiplier is great, right? So as you said, orders of magnitude better. And how do you make sure that you can take the richness of data that belongs to your business and that can give you that edge over your competitors? How do you do that? Well, we are looking at this concept of data management. That was the other piece Steve talked about on stage today, and the data management sphere of taking your multiple disparate data sources, combining that data together so you can find the golden source, the golden record in that, and then be able to leverage those insights in other parts of your business, so you can get better and better and better. That's how innovation is born, is being able to see something other people can't see. But people have data in their environment today that they can't see, or they have insights or nuggets of beautiful information or advantages that they're not privy to because they just don't... They have it in so many different places. So that's what we think is create that kind of data reservoir that we spoke about earlier where you're combining the data that sits in your environment protected within your virtual private cloud or your firewall or whatever, the data that you have, keep it secure, but leverage that to create that business

>> So you're saying optimize for data cleanliness upfront versus optimizing the managing of the cleanup if it gets dirty or it's bad data because bad data is hallucinations and or critical failure in an application, right?

Ranjan Parthasarathy

>> Yeah, so I think the one aspect here is it's not always that data has to be cleansed. The most important thing from a compliance perspective is data has to be in full fidelity as you collected it, right? Now, the views on that can change over time. Today, I may take care of 10 attributes from it. Tomorrow, I may need 50 attributes from it. It's the data engineering that ensures this over time where these views can change and seamlessly connect into systems where they need it the most, I think is where the future is.

>> Do you think data engineering's going to be a bigger position in companies, or is that going to be a position that's going to be human plus AI together or both? What's your view on-

>> Oh, good question....

>> data engineering.

>> Yeah.

Ranjan Parthasarathy

>> Yeah, I think data engineering will become kind of... And things like AI and analytics will get joined at the hip in some ways because there is no GenAI without good quality data engineering, right? And that's where data management, API integrations, these will become more and more critical. And that has to first come together before you can power gen AI use cases, right?

Ann Maya

>> I think there's going to be a democratization of data roles. So as you're able to collect insights, people in different parts of business will be able to see patterns that data engineers may not necessarily see because they don't have that lens of finance or that lens of customer marketing or sales that you're bringing to the fore. So it's a really great way of saying if you could create these insights in a data reservoir, or whatever, data lake, whatever your choice is, and be able to surface up those insights so people can find their own, and be able to slice and dice using, say a GenAI, that, to me, I think is pretty eye-opening.

>> You're describing an operating system, essentially a systems architecture that's going to enable value from the creators themselves, the creative workers or the productivity side of it.

>> Well, and from the different subject matter experts within an organization. What I'm hearing from you is if I'm a pediatric nurse, for example, I might be able to think about data from a different question or ask my AI or my situation a different question than if I'm in cardiology or if I'm on the admin side of the diabetic wing or whatever it might be. But to your point, it's allowing... I love what you just brought up. I think that's an extraordinarily pivotal point. We talk about the democratization of AI a lot, but we talk about it as access for entrepreneurs or for other people, but it's beyond that. It's empowering people across the business to be able to ask questions of the internal systems and data that's within those systems that they weren't able to ask before, because to your point, you couldn't see the dark murky corner of the lake and you didn't know it was over there underneath the little sea cave.

Ann Maya

>> Yeah, and it's an overwhelming thing. I'm going to have to go into PowerBI now. So I think, Savannah, you just explained it incredibly well. And we do have a customer that has been using Boomi systems for some time, and they run greenhouses. And the greenhouses are very digital, so they have little sensors on their plants and stuff, and they have growers, and they were able to maximize the growers associated with each certain level of crop. So they had few growers, and they could manage more crops because they were using the data coming in. And I remember a comment made by I think their CEO. He said, "Our growers are now becoming green data scientists," or something like that. So they're able to use this information, say oxidation levels, hydration, whatever, and be able to tweak how they're managing their crops by using the data. And the more they learned about it, the more they could ask questions, like you said.

>> You can grow your own, as they say, with the good data.

>> Yeah, you can. I want to sit here just for a second because I think it's really important, and I think this is part of how we make AI real. It's how we make some of these new technologies real, is by making... Not that the AI's going to take their job, but that it's going to make everyone's job involve this as a tool in a really empowering and exciting way. So you mentioned the growers turning into data engineers to a degree, green data engineers. When I was doing my homework on you, I love that we're both Washington Huskies. Go dogs.

Ann Maya

>> Yes.

>> And I also liked that you came back and studied computing in IT a little bit after you had gotten your original degree from UDub. This is obviously a really exciting time in tech, huge hype curve moment. What would be your advice to someone... And I want to hear this from both of you because you're both seeing a lot of different things. What would be your advice to someone who's hearing all the excitement and buzz right now, wants to upskill, maybe wants to come work for a company like Apica or Boomi? What would you tell them? Ann, I'm going to start with you.

Ann Maya

>> I'm always going to say this, follow your passion, follow what interests you. So I actually studied engineering, and well, I got a degree in psychology because I wanted to play in the sun. You know what it's like in Seattle. And I was stuck in a dark physics basement for so long. I'm like, I just want to finish. So I went, got a degree in psychology.

>> I love that. I studied communications pretty much for the same reason.

Ann Maya

>> But I went to cognitive psychology. What I was fascinated by was how the brain memory gets stored. And if you think about it, what I've noticed is patterns between how brain and memory is stored and how AI works. So if you follow your passion, things start to come together. The patterns start to come together. And I thought my background in psychology and cognitive psychology and understanding of memory systems has really helped me understand the future of technology and computing and how computing systems work.

>> And systems. Two Different systems, just one's a little silicon powered and one's a little emotionally powered. I love that.

Ann Maya

>> I think you can strive to be the absolute best at what you do if you're working in something you enjoy, and that's what I've always followed.

>> It's a tough act to follow. Do you have anything you want ?

Ranjan Parthasarathy

>> Yeah. So a couple of things. Passion is definitely something that drives you to do the things that you really care about. And figure out what you're good at. Double down on that. And one thing that I've learned over the years is always be a student because there is always something new happening out there. And if you are curious at all times, if you're willing to pick up on new things, you will evolve with how the world is evolving in a positive way, right? And at the end of the day, we need to be students all our life, and that's the best way I think anybody can kind of fulfill their ambitions, their aspirations in terms of what they want to do in their life.

>> Love that. Stay curious. Well, I know I speak for John and I when we both say we've enjoyed learning from both of you today. Ann, Ron, John, thank you so much for being here with us. We look forward to seeing how the partnership evolves as things continue to build momentum and hearing where you're off at the next Boomi World. John, thank you for hanging out. This was a great little chat.

>> My pleasure.

>> I know. It's always-

>> It was great....

>> insightful. I love when you get to learn from the guests and get some definitions for theCUBE's AI, like Runtime Observability. That's great. And thank all of you for tuning in wherever you are on this beautiful earth. We're here in Denver, Colorado. It's day two of Boomi World 2024. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news. We like to add one layer of difficulty just at the very end. Oh, we get a break.

>> So we think data engineering's going to be like an SRE-like job.