Name: TMON Modernization 2025: AI and Data Streaming
Uploaded: 2025-11-12T16:15:05.423Z
Duration: 42 min 52 s
Description: TMON Modernization 2025: AI and Data Streaming

Transcript for "TMON Modernization 2025: AI and Data Streaming": Good day, everyone. Welcome to the webinar. Give everyone a few moments. Again, good morning. Good day, everyone. We'll start in a few moments. Let's give everyone a chance to come online. I don't see a counter as to how many people are are on at the moment, so can't tell if the popcorn is still is done popping. But good day, everyone. We'll start the webinar in just, okay. I I was told I can start. So good day, and welcome to this TMon modernization webinar introducing AI and data streaming for the TMon family of monitors. My name is Reid. I am the tMon product manager, and I'm joined by Rod, Satish, Nathan, and Sental from tMon software engineering. We will be presenting for the next thirty minutes, and we'll save fifteen minutes for q and a. Before we get started I'd like to go over a few housekeeping items. First all attendee lines are muted. We will be recording the webinar and we'll provide the recording links to you within a few days. Please feel free to ask questions at any time in the q and a message box. We have our team available to answer any of your questions throughout the session. Lastly, as you leave the webinar today, you will see a survey pop up. We value your feedback, so please fill out the form if possible. It will take you less than a minute to fill out. This is Rocket Software's disclaimer. The information we are sharing today is subject to possible change as TMon product direction evolves. In q four twenty twenty five, TMon is expanding our AI offering, providing data and metrics streaming, improving mainframe observability, keeping up with IBM's mainframe changes, and providing customer requested enhancements. TMon for KIX provides support for KIX TS 6.3 and ensures that TCE functions without error in a KIX environment. I will go over tMon for zOS enhancements on the next slide and then the team will provide a much deeper dive into tMon health AI and tMon data streaming. For tMon for zOS, we've added intelligent navigation to switch to historical views of logged LFS data from real time displays. Support was added for zOS system capture ratio which indicates system health as a measure of workload CPU time versus the total CPU time used by an LPAR. We also added GES2 privileged resource reporting so that GES2 resources may be reserved for privileged users to assist the systems programmer in the resolution of critical GES2 resource shortage conditions. We've increased the precision of CPU times. CPU times are now stored and reported in microseconds. Users are able to track USS shared memory usage, set exceptions against this data, and have the data aggregated via Naviplex. We also added a new screen to display UNIX file system utilization history and reduced the number of steps significantly to customize the installation of TMon for zOS. Satish, take it away. Thank you, Ureed. I'll screen. K. Hello, everyone. Thank you for joining us today, And today, I'm excited to talk about TMon Health AI, our new capability that helps, you detect issues before they impact your business. Right? So before sorry. Before we further dive into the topics, actual topics, right, let us try to catch up with some of the real challenges that businesses face today. In modern IT environments, applications and systems are deeply interconnected and highly complex. Even a small hiccup in one application can trigger a chain reaction. Transaction stall, customer satisfaction drops, and the financial impact that follows soon. So the stakes are high. So downtime is just not an inconvenience, but it is a business risk. And here's the challenge. Most organizations uses traditional monitoring tools, but these traditional monitoring are reactive and tell you what happened after the incident took place. By the time you see that alert, the damage is already underway. Customers are unhappy. Service levels are slipping, and your teams are scrambling to catch up with. And there is the scale. Thousands of metrics are running around the clock. It takes massive effort to watch them all. Even then, finding the root cause takes time. And while investigations are happening, the problem often gets worse. The truth is in today's complex environments, human alones cannot spot every subtle pattern in real time. Problems often show up in the show up first in the customer experience long before the dashboards catch up with. That's why businesses need to move from reactive firefighting to proactive prevention, and that's exactly where Timon Health AI comes in. Unlike traditional monitoring tools, Timon Health AI goes one step further. It shows you what's about to happen so that you can stay ahead of problems instead of chasing them. K. So how does Timon Health AI help? It uses an AI powered analytics. You can think of it as having a digital analyst who never sleeps, constantly scanning thousands of metrics and spotting subtle signals that humans might miss. It does an early detection. So instead of waiting for a performance slowdown or an outage, you get a heads up while the issue is still forming. This means you can intervene before customers even notice. It applies pattern recognition. Over a period of time, the system learns what normal looks like in an in your environment, whether that's a peak loads during month end processing or a quieter period or during overnight. So when something deviates from those existing patterns, it raises a flag immediately, and it offers customer alerts. You are not logged into generic thresholds. You can tailor your rules to business priorities. For example, you might set stricter alerts for customer facing or critical applications and more relaxed ones for batch jobs. So together, these capabilities shift monitoring from reactive firefighting to proactive prevention, giving your teams confidence and control. K. Let us look take a look at under, you know, the hood of t mon health AI. One of the strengths of t mon health AI is its integrated data ingestion. It does not just monitor one source. It pulls information from across all your environments like KIX, DB2, ZOS, MQN, IMS. That means you're not piecing together fragments. You get a complete unified view of system health. It is built with an end to end security. Every step of the process is protected. Data can be encrypted, access can be controlled, and handling can be made secure. In organizations where compliance and trust are critical, this is nonnegotiable. So finally, it offers an intuitive web interface, called Timon Scope, which offers a a a lot of clear dashboards and insights, you know, that highlights the anomalies, which is, easy and, for you to review. Teams can act quickly without needing to decode the complex reports. So when we say it's built for enterprise reliability, we mean it's designed to handle mission critical workloads with the same resilience and clarity you expect from rest of the t mon suite. So let's shift our focus for a while from features to, the outcome and benefits. Right? What does this mean for your business? Timon Health AI reduces your operational cost and reduces the downtime of, the application and systems. By catching issues early, you avoid expensive firefights and keeping your systems and application running smoothly. It offers real time visibility across, all the applications. You don't have to wait for daily report or rely on an anecdotal feedback. Health data is available instantly. It facilitates in faster instant resolution. Mean time, the resolution drops because your team spends less time searching for root cause and more time fixing problems. It offers improved performance and availability. Systems and applications stay responsive. Customers stay happy, and your business stays productive. It offers seamless integration with, team on suite. Don't need to reinvent workflows. Health AI fits right into it. And finally, it can take better it can help you take better decisions through clear dashboards and reports. Business leaders can see trends, understand risks, and make confident calls. In short, the benefit goes beyond IT. They translate directly, into business resilience and customer satisfaction. K. So let me walk you through, you know, how TMon LTE actually operates in your environment. As part of the data collection, the system pulls performance data from all your rocket t mon products like Kicks, d b two, ZOS, MQ, IMS, and more. This is a comprehensive data from across your entire mainframe environment. It uses your existing monitoring infrastructure, and there is no additional data collection burden. It's a continuous data flow into the AI system. And this is not a batch process that runs once a day or once an hour. This is a near real time continuous analysis of your environment. The system analysis both performance data and historical trends to build a baseline understanding of what normal operation looks like in your specific environment. This baseline is unique to your organization and your workloads. And, here's when the intelligence kicks in. Sorry. Sorry. Yep. Our AI algorithm analyzes the data streams and identify the patterns and correlations that human eyes would miss. The machine learning model detects anomalies across multiple dimensions, and the systems get smarter over time as it learns more about your environment. As soon as the system detect anomalies, it alerts you while those anomalies are still small and manageable. This might be usage spikes, unusual job patterns, performance degradation, or even emerging risk patterns. The key is that we are catching up these issues before they become critical. That's where the prediction becomes comes into action. Right? The system does not just, provide you some raw alerts. It provides contextualized information that your teams can immediately act upon. You get clear descriptions of what is, anomalous, and you get enough detail for your teams to investigate and respond quickly. Everything is presented through our modern web interface, which you would be seeing in next couple of minutes. You get these smart dashboards with multiple visualization options designed to make, the anomalies obvious. Your teams can drill down, investigate further, and respond to issues from anywhere. These visualizations are intuitive and actionable. These six steps work together seamlessly so that the performance metrics flows in, gets analyzed by the intelligent AI, transformed into insights, and presented to your teams through an interface designed for rapid response. This would result definitely, and help you in identifying and addressing issues before they impact your business. That's it about how it works, and, I will now hand it over to Nadine who will take us through the user interface for team on health AI. Over to you, Nadine. Alright. Am I audible? Yes. You are. Thank you. Thank you, Satish, and thanks, Reid as well. So as Satish was stating, you know, all the benefits of health AI and all the analysis that we perform gets presented in scope application. Scope is a web dashboard, which helps you in visualizing all the performance, metrics, and provide information at various levels. Okay. What we have in scope at a very high level is multiple views, which we call as perspectives. Okay. You get to see all the anomalies that are observed as part of HealthAI and in some cases from TMNT PA displayed in multiple views, which are either list view, which will give you a list, a heat map view, and an analysis view, which gives you a bubble chart of how things are, and a KPI view. Okay? Key performance indicators. Alright? So we will dive deep into each of the views, and we'll look at the actual screenshots of the application. Moving on, the first one is the, anomaly list view. Before I go into the list view, what you see on screen, you know, on the left side of the screen this is the scope application, by the way. On the left side of the screen is called, dimension filter panel. Okay. You get to see details about app views, platforms, and other categories. You know? These are filters which you can select. And on the top, you see what we call as a top bar, which helps you pick the date, time, range, and also helps you navigate to, the different perspectives, which are the views that we we talked about. Okay? These two remain consistent. So the data that you get, is going to remain same, but you get to see the same data in multiple different perspectives and to to understand it better, to gain better insights. Alright. So the first one out here is the list view. So the list view, as you can see, is gonna give you the list of anomalies on the screen, and, you can drill down. If you click on one particular anomaly, you'll see more details about it as a pop up, and then you can see more information about, you know, how much the deviation is and all of the details. Okay? And, yes, on the same screen, on the top right, the same navigation on the top bar, you also get to see about the severity. Okay? Red being critical and yellow being a warning. So these are all generated from the HealthAI, and these will be displayed. If you wanna look into just the criticals, you look into criticals, or you can switch between warning, or you can look at all of them. So the list view gives you a list of, anomalies for you to look and act upon. Alright? And so this is a quick summary of what it is. You can, sort and customize the list view, and you can see that normally based on time range. The time picker is on the top. You have even the auto update, so the list can get auto populated as and when the time, flows through. Okay? Moving on. We have the heat map view. So as you can see, the dimension filter panel remained consistent. The top bar remained consistent. The data is now presented to you on the basis of time, which you can see on the top, but as a heat map. Okay? So reds are the warnings for which are the application views and l I mean, reds are the critical, yellow's are the warnings. You can see clearly, and then you can act upon them based on the hover slot or, you know, any of the hover slot that you can see on the top. Okay? So you get to see, as I said, you know, anomalies at the timeline chart, red being critical, yellow being warning. Moving on, this is what we call the bubble chart or analysis view. Okay? The same pattern holds the dimension filter on the top bar remain consistent. So you are looking at the same data in a different perspective. So it's presenting you some of the app views as you can see. Enterprise. Okay. How many anomalies do you see for that particular time range? And the bubble size, demonstrates the number of count of, anomalies, and, the y axis is sort of the growth percentage. So you can see the growth percentage as well displayed as part of the bubble chart. Moving on, a quick summary on it reflects the anomaly count. X axis is the anomaly count. Y axis is the growth percentage for the bubble chart. And the final view is the KPI or the key performance indicator. Okay? So that'll be diff display the same anomalies as different types of charts. Bar chart anomalies by KPI, you can set those KPI. Okay? The first chart, you can see anomalies are stacked, one over other by time. Okay? And below, you have multiple charts that you can go through individual KPIs. And then upon clicking a particular time range, you can still drill down further and then look into a particular anomaly. As somebody has for the KPI views, you know, you get to see it in different charts and anomaly rates, you know, for KPI by for selected dimensions. Okay? So at overall, this is what a scope, application. There are other connections and application view setups and CRUD operations that you can do, that an administrator can do that are not covered. This is high level, into the views that you can get. All the data is available at Rocket Software, site. You can drill down further on that. And moving on, I'm gonna talk about PMM PA. PA is a separate, product, but a quick shout out on the upcoming, changes on PA. So, we are giving IBM z 17 support. We have enhanced, the FTP as a secure FTP for certificate in a secure FTP certificate based authentication. And p one p a is also enhanced, for KPI generation, for health care and scope UI integration and security improvements for the PA application. These are all part of the upcoming enhancements that are gonna come out as part of PA. And with that, I'm done, and I'll hand it over to Sandhu. Thank you. Thanks, Nathan. Am I audible? Yes. You are. Okay. Great. Thanks. Hi, everyone. Good morning and good evening. I am Sentil, senior principal engineer from the tMon team. We are introducing an exciting new capability in our portfolio, what we call it as tMon stream. This is going to be the data bridge to connect, mainframe performance data to your modern analytical platform. So why does it matter now? We all have, all of our mainframe performance data kind of, buried in our individual team on subsystems and in some cases in some sort of, LFS archives. So, yes, there are tools like, ReportWriter that, we have been using all these years to create some handy reports, but the systems are getting big. Hundreds of LPA's, tons of, fixed and IMS transactions. And on the other side, there's a growing complexity of your application stack, things like mobile LPO devices and whatnot. So the end to end application stack is getting complex daily, and customers are looking beyond the mainframe to get an end to end application analysis view. Sometimes, like, to use popular tools like, Grafana, Splunk, or Elastic, to do their analytical work. So the goal of t mon string is to make all the t mon performance data seamlessly available to non z systems to get a kind of a real business value out of this data. So to talk about, the some of the core capabilities of the solution that's going to be, so the data is going to be made available as, real time data feed. So it can think of it as a kind of a live hot monitor for, your mainframe data. So you get to see your performance, as it happens, not hours later in a kind of a report. So it's all going to be in a standard JSON format. So your modern tools like a Splunk or Elastic can pick it up, directly. And when it comes to integration, so the streaming solution, what we offer can plug in right into your existing analytical tools or dashboards. So to call out, for example, if you have tools like Prometheus already in place, Prometheus has some specific data model to expose the metrics, which team on stream supports out of the box. So to call out some, key technical features, eventually, we are going to cover all the t mon data sources, but, we are starting with the KIX, DB2, and zOS. Data security is going to be something that's building the solution. So everything that moves out of mainframe to network, it's going to be via a secured HTTPS or a TLS channel. And, it's going to be very scalable, whether you are handling a small subset of your performance data or a large volume of transactional metrics. So the system is going to let you scale, flexibly through our broker configuration. The broker or or they call it as a data broker, that's our underlying engine that moves the data. So it's very flexible to configure, and it's going to be letting you configure your resource allocations for a kind of a right balance, between your performance expectations and cost efficiency. So and, also to call out that the architecture is also going to let you, to move some of the streaming workloads of the mainframe, so that it can free up your MIPS and keep the cost under control. And the last but not least, so, we as I mentioned, we are going to support a variety of endpoints. So you can choose where the data goes, whether it is Kafka, Prometheus, or any of your favorite analytical dashboards, like Grafana or Kibana. So what does this mean for your business? So, it's going to mean quicker or faster response. It's going to help boost your ops efficiency with the faster root cause analysis. And, also, it's going to help you seamlessly integrate, your mainframe performance data across your enterprise, meaning that mainframe is not going to be left alone anymore. So, how does it work? Architecturally, this is how it's going to span out. We have our t one monitors collecting performance data, And, optionally, today is logging some of these data to our l LFS datasets and some sites. So what we have, done is to add a kind of a hook in, so to our collectors. So that's going to be a PDF that's going to be made available for this. So once you have this team on stream enablement PDF, you can choose to send the performance metrics, to our data broker service. So and the data broker, can further relay the data to another Java application called as a Data Connect, which as I mentioned, you can choose to run either on z or off platform. And the Connect is is going to provide you, the configurations necessary to configure different endpoints like Elastic Prometheus or Kafka. So you can choose to route to, one of these destinations or more than one of these destinations, as the requirement. Also to call out as a part of the solution, we are planning to distribute, what we call it as a starter dashboard bundles, that will be made available in our RCC portal. These are some super handy, dashboards that you can just install them and start using the dashboards right away. Or, you can use them as a kind of a template to build something, on top of it. So that's going to be dashboards like this that's going to be made available for all the subsystems that we're going to support, like ZOS, DB two, and CACS. So So we are going to see, some starter dashboards distributed for all these subsystems. And, also, you will get a copy of these dashboards, one version of it in Grafana and one version in Kibana. So you can choose, so what whichever is your favorite. So that's pretty much about, this new solution offering that you can anticipate, in the next quarter. And with that, I'm going to pass it on to Rod to talk about the data sharing initiative. Great. Thank you, Cynthil. Appreciate that. Alright. So, we would like to be able to run a demo for our health AI for your enterprise, any of the data that you all have. So what we're asking, if you would like to join us on this journey, is to identify, the Sysplex that's having some, perhaps, some issues that you're aware of. It doesn't have to be, but if you have any of the data that, you can provide, you can send that to us. It can be LFS data from t mon or even SMF data. Preferably three months of data would be great for us to provide something that's really useful for you. We are gonna protect your data. It stays, in a very secure location on our system, and we we delete it as soon as we're done with it. We're not gonna maintain any of your data for very long. We will run through our reports on that. We will do our analysis, and then we will return the results to you. We have done this with other customers in the past, and we have, you know, identified some surprises that they weren't aware of. So it's a very useful thing that you can see the value of of the the solution for you and and your business, you know, as well as, you know, give you some, basically free, information about what's going on in your system that you may not have been aware of. So, you know, you can FTP the, any of the data that you can gather to us. We'll work with you. You can contact Reid. You can contact me as well. My, my email address is gonna be on a on a slide a little bit later on, but you can feel free to reach out to us. This this is very useful for us to be able to make sure that the data, algorithms that we're using, are working, that they are producing the right kind of results that are beneficial and valuable to you and and in your environments, and it helps you all, understand what's going on so that, you can you can resolve any problems that may be coming up. So the other thing we want to talk about briefly is the Timon modernization journey. Timon is undertaking some steps that you may have already seen earlier talking about the health AI and talking about the streaming solution in order to modernize the product that we have today. So things that we're looking at is improving the user experience of the product. We wanna be able to bring data from all of the different tech stacks, whether it's DB two, Kicks, VOS, IMS, NQ, TCPIP. Bring them all together into a set of dashboards that are providing much more information for you. This is going to make it much easier to find the information that you need in order to solve any performance problems that you're having or any other resource issue. And it allows you to, you know, drill down and and solve these problems faster, by using that. And and another thing we're trying to do is simplify our installation, considerably. We've already taken some steps that that you will have noticed, in some of the latest releases of our of our products, and we are gonna continue to, make this product similar to use. Our goal is is ideally to be able to have tMon installed in the morning and being productive in the afternoon, in your environments. So the other thing that we're doing, as you've already noticed, is we're trying to integrate AI more into the product. We're looking for more advanced AI algorithms that we can use, to better detect performance issues and be more predictive. We'd really like to be able to tell you that sometime within the next hour or so, you are you're gonna potentially have a performance problem in one of your areas. We are very aggressively looking at the different algorithms that are available in the machine learning space, to be able to help out with that. Tying all of this back to the data sharing, you know, we as as a development shop have development data. We don't have real world data. So the data that you all have collected in your SMF and then, if you have tmon and LFS for for and your tmon, is is very useful for us to validate all of the different algorithms and programming that we do. That's one of the reasons why we would appreciate, working with you, to get that data. And as I said earlier, you you get free advice and free information from us in return. We also recognize that mainframes exist in a hybrid cloud environment these days. It's not a a system that's standing off to the side. So we are doing work that we can, to look at data that's coming from multiple, multiple environments tracing the data across all of the different directions that it's moving. I'm sure that for many, if not most of you, observability is a, is a is a key factor or key tool in what you're using today in order to manage your systems. And we wanna be able to continue to provide more data into that, more information. So we're looking at different ways to do that. Now as a as a result of that, you know, what I'm putting here on this slide is a series of the things that we are considering doing. And what we would like is to get your input into the value of any one of these, particular, efforts that we're gonna take for features and functionality. If you have anything that you need to see in the product, we'd love to hear all about your pain points. We'd love to know, you know, what is it that the product doesn't do well for you that or any other issues that you're having in your environment. So, you know, we would like to continue reaching out to you all and having those kind of conversations. We're gonna do more work on our streaming solution, that that Simpil has already described to you. There's more data that we wanna bring into that, and I think there's also more, observability type information that we we want to collect as well that we don't necessarily make available to any of the observability platforms today. We again, we want to look more at, you know, tracing workloads as it moves across your different, tech stacks in your in your systems, and then make that data available, looking at logs, messages, things like that that that can be useful in diagnosing problems. The other thing that we're looking at is replacing the current OData API, REST API that we have in the product with a new REST API. The the current API relies on what in TMOT we call aggregate records, which is, they're it's a robust set of records, but it doesn't give you access to all of the TMON data. So we want to build a REST API that's gonna give you access to, any and all data that TMON can collect, whether that's real time or historical data. The the current HODATA API looks at real time data. It doesn't look at historical data. So, you know, if that's of interest to you, we would like to know. Also, we're looking at building an ISPF interface. So, apart from the the the standard TMP thirty two seventy interface that we have today, we'd like to be able to pull this into ISPF. And in addition, we'd also like to move much more into the Web UX, whether it's Kibana, or any of the other, web based visualization platforms that are out there today. You know, through the streaming, we're already gonna be able to bring data into Splunk and Elastic and, you know, OpenTelemetry and Datadog, things like that. We're also looking at bringing much more of our product functionality, our product panels and menus and whatnot into the web space and doing something there that, is very valuable and meaningful to you in in in that sense. And, not moving completely away from thirty two seventy, but but for many more of the type of reports that might be of interest to you, bringing that off the platform and into the web space. As I mentioned, we're also looking at doing more predictive analytics, the the kind of of AI that tells you, you know, that within a very short period of time, you're trending towards, a problem. We wanna give you advanced notice so that you're not getting this, after the fact. This is a little different from what we do with the Health AI today. You can think of it as an extension to what the Health AI does, which is, again, adding more of a trend analysis into the KPI analysis that we're already doing. And I've already mentioned what we wanna do with Simple Flying installation and maintenance. And and some of the ideas that we have, would be able to reduce the installation process to really installing one product, across all of your all of your systems. So you're not installing, you know, tmon KIX and tmon DB two and tmon ZOS. Instead, you would be installing tmon, and everything would flow from that. So this is here. The the we're putting this out, and we are asking for you to help us shape the future of TMon. Right? We have these, the email address for Reid and me at the bottom of this slide. We would love to hear from you. We want to know the problems that you're having in your shops, the pain points that our products can help you with. What do you not like about our products, so that we can, we can know what to improve and we can spend more time improving on that. What are the difficulties you're facing, day to day? We we know that your time is very valuable, and it's overloaded today. So we're trying to find ways of using the products here at Rocket generally, not just for the, you know, team on, in in order to help, you know, relieve the load on you. So please, if you're interested at all in working with us, talking with us directly, asking us questions, feel free to reach out to Reid or myself, and then we'll be, you know, super happy to respond with you and work with you. We would really like to build very tight relationships with our customers, much tighter than what we've had, you know, certainly in the recent past. We'd really like to know how we can better, help you with the work that, you need to do. K? And just a couple of last points. We've been very active this year in rebuilding, the websites that for all of our products. Reed and Ken have been, very, very busy going through and replacing a lot of, if not all of the web pages that, I, you know, talk about t mon products on the on the website. In addition, we've also been working on a lot of videos. So whether you you go to our website and find our videos or you can go to YouTube and you can look at our videos there, we are releasing videos that, provide insights into the products. We are doing what's new videos. We will be doing, additional technical videos. We don't have any of those available yet, but those are in the works to be able to tell you how to do things with the product. We'd love to know if you're interested in videos as a learning experience for using t month. Let us know what you particularly want to know about, and we can give those kind of topics priorities in the video generation that we're doing. We plan to do webinars much more frequently. Webinars such as this, we'll do, hopefully quarterly, at least semiannually. But we're trying to target webinars to overlap with our, maintenance and and feature prod, packages that we distribute once a quarter. So you can expect more of those to come. Any topics that you would like us to address, please let us know, and and we will, do that. Rocket has a customer forum site, and we're gonna our d b two one will be coming from our d b two. I'm sorry. Our team on will be, available sometime soon. And that will be a a general, site for communication between customers with us, customers with each other. It's a way of having updates where you can ask questions. We can chat. You can give advice, you can get advice. We'd like to make that a very active part of the team on, experience and and and bring everyone together so that we can all share what we, like best and know best about this product. At the end of this webinar is going to be a survey. We ask that you please take some time, be honest with your feedback. You know, if you didn't like part of this, let us know. That that way we won't do it again. So, if you did like something, that's good to know as well. But we'd really like to know what you think about this webinar and, you know, what we can do better in the future. K? So with that, Reid, back to you to wrap it all up and start the q and a. Well, thanks all. Let's just jump on into the q and a section. Rudra, thank you so much for your questions so far throughout the, the webinar. Do we have, some more questions from, people out there? Rudra, the floor is yours if you have more questions. Thank you, Ken. Alright. Oh, well, I I think Verdra had the, had the floor for questions. So, last call to put any questions that you may have into the q and a box on the right hand side. Well, with that, we thank you so much for your time today and allowing us to speak with you regarding, our direction in AI and data streaming. Again, as Rod noted, please contact me, rgerson@rocketsoftware.com or rdyson@rocketsoftware.com, with any further questions that you may have. We'd love to work with you. We've been working with, over 30 to 40 customers this year alone, to understand their needs, and we'd love to work with you. So with that, bye, everyone. Thank you for your time today. Thank you, Thank you, everyone. Bye bye.