(inspirational music) (music ends) >> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities, including data exploration business intelligence, natural language processing and machine learning directly within the fabric makes it faster and easier for organizations to gain new insights and power intelligence predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi, yeah, thank you so much for having me. And so for this demo, we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements, and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo, and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see, and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be, for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software, or adverse reaction warnings from a clinical risk grouping application, and so much more. So I'm really going to be simulating a patient logging in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here, and I'm going to be looking for information where the last name of this patient is Simmons, and their medical record number or their patient identifier in the system is 32345. And so as you can see, I have this single JSON payload that showed up here of, just, relevant clinical information for my patient whose last name is Simmons, all within a single response. So fantastic, right? Typically though, when we see responses that look like this there is an assumption that this service is interacting with a single backend system, and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture, we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here, we have our data fabric coordinator which is going to be in charge of this refinement and analysis, those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service, and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end, we do also support full life cycle API management within this platform. When you're dealing with APIs, I always like to make a little shout out on this, that you really want to make sure you have enough, like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what context. >> Can I just interrupt you for a second, Jess? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you could have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry, and API securities are like, really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So, there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So, the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product, and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So, that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of the security. >> And that's been designed in, it's not a bolt on as they like to say. >> Absolutely. >> Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly, each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like Fire. Interactions with a homegrown enterprise data warehouse for instance, may use SQL. For a cloud-based solutions managed by a vendor, they may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and applications. And I'm about to log out, so I'm going to (chuckles) keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources, and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is, it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST, or SOAP, or SQL, or FTP, regardless of that protocol, there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as in healthcare we have HL7, we have Fire, we have CCDs, across the industry, JSON is, you know, really hitting a market strong now, and XML payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel, I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example, communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR, I'm leveraging a standard healthcare messaging format known as Fire, which is a REST based protocol. And then when I'm working with my health record management system, I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly, and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN, and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out-of-the box or black box approach to be able to develop things that are specific to their data fabric, or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you not only get an opportunity to view how we're establishing these connections or how we're building out these processes, but you have the opportunity to inject your own kind of processing, your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out-of-the-box code that is provided in this data fabric platform from IRIS, combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out-of-the-box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. (laughs) >> It's a lot here. You know, actually- >> I can pause. >> If I could, if we just want to sort of play that back. So we went to the connect and the collect phase. >> Yes, we're going into refine. So it's a good place to stop. >> So before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know, the ability to bring in different dev tools. We heard about Fire, which of course big in healthcare. And that's the standard, and then SQL for traditional kind of structured data, and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely. And I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection, into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refinement. >> We're going into refinement. Exciting. (chuckles) So how do we actually do refinement? Where does refinement happen? And how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator, or stands for Smart Data Fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information, it's aggregating it, and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like. And as you can see, it follows a flow chart like structure. So there's a start, there is an end, and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce, or we make this data fabric a bit smarter, and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection, we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging, 'cause you need to be able to know, you know, if there was an issue, where did that issue happen in which connected process, and how did it affect the other processes that are related to it? In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric, to when data was sent back out from that smart data fabric. So I didn't record the time, but I bet if you recorded the time it was this time that we sent that request in and you can see my patient's name and their medical record number here, and you can see that that instigated four different calls to four different systems, and they're represented by these arrows going out. So we sent something to chart script, to our health record management system, to our clinical risk grouping application, into my EMR through their Fire server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems, and we bundle them together. And from my Fire lovers, here's our Fire bundle that we got back from our Fire server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping, or errors that were thrown, alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Sure, who did what when where, what did the machine do what went wrong, and where did that go wrong? Right at your fingertips. >> Right. And I'm a visual person so a bunch of log files to me is not the most helpful. While being able to see this happened at this time in this location, gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric, is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information it's transforming that data, in a format that your consumer's not going to understand. It's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? It all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well, we can keep going. I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this, but essentially if we go back to our coordinator here, we can see here's that original, that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here, which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric, but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at, and we're running it through a machine learning model that exists within the smart data fabric pipeline, and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world, is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL-like syntax to be able to construct and execute these predictions. So, it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge, right? Because it directly affects the cost for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment, or, you know, as an outpatient perhaps, to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day, which is what makes this so exciting. >> Awesome demo. >> Thank you! >> Jess, are you cool if people want to get in touch with you? Can they do that? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy, and we'd love to hear from you. I always love talking about this topic so we'd be happy to engage on that. >> Great stuff. Thank you Jessica, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment, we're going to dig into the use cases where data fabric is driving business value. Stay right there. (inspirational music) (music fades)

(upbeat music) >> Today more than ever before, organizations are striving to gain a competitive advantage, deliver more value to customers, reduce risk, and respond more quickly to the needs of businesses. Now, to achieve these goals, organizations need easy access to a single view of accurate, consistent and very importantly, trusted data. If it's not trusted, nobody's going to use it and all in near real time. However, the growing volumes and complexities of data make this difficult to achieve in practice. Not to mention the organizational challenges that have evolved as data becomes increasingly important to winning in the marketplace. Specifically as data grows, so does the prevalence of data silos, making, integrating and leveraging data from internal and external sources a real challenge. Now, in this final segment, we'll hear from Joe Lichtenberg who's the global head of product and industry marketing, and he's going to discuss how smart data fabrics can be applied to different industries. And by way of these use cases, we'll probe Joe's vast knowledge base and ask him to highlight how InterSystems, which touts a next gen approach to Customer 360, how the company leverages a smart data fabric to provide organizations of varying sizes and sectors in financial services, supply chain, logistics and healthcare with a better, faster and easier way to deliver value to the business. Joe welcome, great to have you here. >> Thank you, it's great to be here. That was some intro. I could not have said it better myself, so thank you for that. >> Thank you. Well, we're happy to have you on this show now. I understand- >> It's great to be here. >> You you've made a career helping large businesses with technology solutions, small businesses, and then scale those solutions to meet whatever needs they had. And of course, you're a vocal advocate as is your company of data fabrics. We talked to Scott earlier about data fabrics, how it relates to data mesh big discussions in the industry. So tell us more about your perspective. >> Sure, so first I would say that I have been in this industry for a very long time so I've been like you, I'm sure, for decades working with customers and with technology, really to solve these same kinds of challenges. So for decades, companies have been working with lots and lots of data and trying to get business value to solve all sorts of different challenges. And I will tell you that I've seen many different approaches and different technologies over the years. So, early on, point to point connections with custom coding, and I've worked with integration platforms 20 years ago with the advent of web services and service-oriented architectures and exposing endpoints with wisdom and getting access to disparate data from across the organization. And more recently, obviously with data warehouses and data lakes and now moving workloads to the cloud with cloud-based data marts and data warehouses. Lots of approaches that I've seen over the years but yet still challenges remain in terms of getting access to a single trusted real-time view of data. And so, recently, we ran a survey of more than 500 different business users across different industries and 86% told us that they still lack confidence in using their data to make decisions. That's a huge number, right? And if you think about all of the work and all of the technology and approaches over the years, that is a surprising number and drilling into why that is, there were three main reasons. One is latency. So the amount of time that it takes to access the data and process the data and make it fit for purpose by the time the business has access to the data and the information that they need, the opportunity has passed. >> Elapsed time, not speed a light, right? But that too maybe. >> But it takes a long time if you think about these processes and you have to take the data and copy it and run ETL processes and prepare it. So that's one, one is just the amount of data that's disparate in data silos. So still struggling with data that is dispersed across different systems in different formats. And the third, is data democratization. So the business really wants to have access to the data so that they can drill into the data and ask ad hoc questions and the next question and drill into the information and see where it leads them rather than having sort of pre-structured data and pre-structured queries and having to go back to IT and put the request back on the queue again and waiting. >> So it takes too long, the data's too hard to get to 'cause it's in silos and the data lacks context because it's technical people that are serving up the data to the business people. >> Exactly. >> And there's a mismatch. >> Exactly right. So they call that data democratization or giving the business access to the data and the tools that they need to get the answers that they need in the moment. >> So the skeptic in me, 'cause you're right I have seen this story before and the problems seem like they keep coming up, year after year, decade after decade. But I'm an optimist and so. >> As am I. >> And so I sometimes say, okay, same wine new bottle, but it feels like it's different this time around with data fabrics. You guys talk about smart data fabrics from your perspective, what's different? >> Yeah, it's very exciting and it's a fundamentally different approach. So if you think about all of these prior approaches, and by the way, all of these prior approaches have added value, right? It's not like they were bad, but there's still limitations and the business still isn't getting access to all the data that they need in the moment, right? So data warehouses are terrific if you know the questions that you want answered and you take the data and you structure the data in advance. And so now you're serving the business with sort of pre-planned answers to pre-planned queries, right? The data fabric, what we call a smart data fabric is fundamentally different. It's a fundamentally different approach in that rather than sort of in batch mode, taking the data and making it fit for purpose with all the complexity and delays associated with it, with a data fabric where accessing the data on demand as it's needed, as it's requested, either by the business or by applications or by the data scientists directly from the source systems. >> So you're not copying it necessarily to that to make that you're not FTPing it, for instance. I've got it, you take it, you're basically using the same source. >> You're pulling the data on demand as it's being requested by the consumers. And then all of the data management processes that need to be applied for integration and transformation to get the data into a consistent format and business rules and analytic queries. And with Jess showed with machine learning, predictive prescriptive analytics all sorts of powerful capabilities are built into the fabric so that as you're pulling the data on demand, right, all of these processes are being applied and the net result is you're addressing these limitations around latency and silos that we've seen in the past. >> Okay, so you've talked about you have a lot of customers, InterSystems does in different industries supply chain, financial services, manufacturing. We heard from just healthcare. What are you seeing in terms of applications of smart data fabrics in the real world? >> Yeah, so we see it in every industry. So InterSystems, as you know, has been around now for 43 years, and we have tens of thousands of customers in every industry. And this architectural pattern now is providing value for really critical use cases in every industry. So I'm happy to talk to you about some that we're seeing. I could actually spend like three hours here and there but I'm very passionate about working with customers and there's all sorts of exciting. >> What are some of your favorites? >> So, obviously supply chain right now is going through a very challenging time. So the combination of what's happening with the pandemic and disruptions and now I understand eggs are difficult to come by I just heard on NPR. >> Yeah and it's in part a data problem and a big part of data problem, is that fair? >> Yeah and so, in supply chain, first there's supply chain visibility. So organizations want a real time or near real time expansive view of what's happening across the entire supply chain from a supply all the way through distribution, right? So that's only part of the issue but that's a huge sort of real-time data silos problem. So if you think about your extended supply chain, it's complicated enough with all the systems and silos inside your firewall, before all of your suppliers even just thinking about your tier one suppliers let alone tier two and tier three. And then building on top of real-time visibility is what the industry calls a control tower, what we call the ultimate control tower. And so it's built in analytics to be able to sense disruptions and exceptions as they occur and predict the likelihood of these disruptions occurring. And then having data driven and analytics driven guidance in terms of the best way to deal with these disruptions. So for example, an order is missing line items or a cargo ship is stuck off port somewhere. What do you do about it? Do you reroute a different cargo ship, right? Do you take an order that's en route to a different client and reroute that? What's the cost associated? What's the impact associated with it? So that's a huge issue right now around control towers for supply chain. So that's one. >> Can I ask you a question about that? Because you and I have both seen a lot but we've never seen, at least I haven't the economy completely shut down like it was in March of 2020, and now we're seeing this sort of slingshot effect almost like you're driving on the highway sometimes you don't know why, but all of a sudden you slow down and then you speed up, you think it's okay then you slow down again. Do you feel like you guys can help get a handle on that product because it goes on both sides. Sometimes you can't get the product, sometimes there's too much of a product as well and that's not good for business. >> Yeah, absolutely. You want to smooth out the peaks and valleys. >> Yeah. >> And that's a big business goal, business challenge for supply chain executives, right? So you want to make sure that you can respond to demand but you don't want to overstock because there's cost associated with that as well. So how do you optimize the supply chains and it's very much a data silo and a real time challenge. So it's a perfect fit for this new architectural pattern. >> All right, what else? >> So if we look at financial services, we have many, many customers in financial services and that's another industry where they have many different sources of data that all have information that organizations can use to really move the needle if they could just get to that single source of truth in real time. So we sort of bucket many different implementations and use cases that we do around what we call Business 360 and Customer 360. So Business 360, there's all sorts of ways to add business value in terms of having a real-time operational view across all of the different GOs and parts of the business, especially in these very large global financial services institutions like capital markets and investment firms and so forth. So around Business 360, having a realtime view of risk, operational performance regulatory compliance, things like that. Customer 360, there's a whole set of use cases around Customer 360 around hyper-personalization of customers and in realtime next best action looking to see how you can sell more increase share of wallet, cross-sell, upsell to customers. We also do a lot in terms of predicting customer churn. So if you have all the historical data and what's the likelihood of customers churning to be able to proactively intercede, right? It's much more cost effective to keep assets under management and keep clients rather than going and getting new clients to come to the firm. A very interesting use case from one of our customers in Latin America, so Banco do Brasil largest bank in all of Latin America and they have a very innovative CTO who's always looking for new ways to move the needle for the bank. And so one of their ideas and we're working with them to do this is how can they generate net new revenue streams by bringing in new business to the bank? And so they identified a large percentage of the population in Latin America that does no banking. So they have no banking history not only with Banco do Brasil, but with any bank. So there's a fair amount of risk associated with offering services to this segment of the population that's not associated with any banks or financial institutions. >> There is no historical data on them, there's no. >> So it's a data challenge. And so, they're bringing in data from a variety of different sources, social media, open source data that they find online and so forth. And with us running risk models to identify which are the citizens that there's acceptable risk to offer their services. >> It's going to be huge market of unbanked people in vision Latin America. >> Wow, that's interesting. >> Yeah, yeah, totally vision. >> And if you can lower the risk and you could tap that market and be first >> And they are, yeah. >> Yeah. >> So very exciting. Manufacturing, we know industry 4.0 which is about taking the OT data, so the data from the MES systems and the streaming data, real-time streaming data from the machine controllers and integrating it with the IT data, so your data warehouses and your ERP systems and so forth to have not only a real-time view of manufacturing from supply and source all the way through demand but also predictive maintenance and things like that. So that's very big right now in manufacturing. >> Kind of cool to hear these use cases beyond your healthcare, which is obviously, your wheelhouse, Scott defined this term of smart data fabrics, different than data fabrics, I guess. So when we think about these use cases what's the value add of so-called smart data fabrics? >> Yeah, it's a great question. So we did not define the term data fabric or enterprise data fabric. The analysts now are all over it. They're all saying it's the future of data management. It's a fundamentally different approach this architectural approach to be able to access the data on demand. The canonical definition of a data fabric is to access the data where it lies and apply a set of data management processes, but it does not include analytics, interestingly. And so we firmly believe that most of these use cases gain value from having analytics built directly into the fabric. So whether that's business rules or predictive analytics to predict the likelihood of a customer churn or a machine on the shop floor failing or prescriptive analytics. So if there's a problem in the supply chain, what's the guidance for the supply chain managers to take the best action, right? Prescriptive analytics based on data. So rather than taking the data and the data fabric and moving it to another environment to run those analytics where you have complexity and latency, having tall of those analytics capabilities built directly into the fabric, which is why we call it a smart data fabric, brings a lot of value to our customers. >> So simplifies the whole data lifecycle, data pipelining, the hyper-specialized roles that you have to have, you can really just focus on one platform, is that? >> Exactly, basically, yeah. And it's a simplicity of architecture and faster speed to production. So a big differentiator for our technology, for InterSystems, Iris, is most if not all of the capabilities that are needed are built into one engine, right? So you don't need to stitch together 10 or 15 or 20 different data management services for relational database in a non-relational database and a caching layer and a data warehouse and security and so forth. And so you can do that. There's many ways to build this data fabric architecture, right? InterSystems is not the only way. >> Right? >> But if you can speed and simplify the implementation of the fabric by having most of what you need in one engine, one product that gets you to where you need to go much, much faster. >> Joe, how can people learn more about smart data Fabric some of the use cases that you've presented here? >> Yeah, come to our website, intersystems.com. If you go to intersystems.com/smartdatafabric that'll take you there. >> I know that you have like probably dozens more examples but it would be cool- >> I do. >> If people reach out to you, how can they get in touch? >> Oh, I would love that. So feel free to reach out to me on LinkedIn. It's Joe Lichtenberg I think it's linkedin.com/joeLichtenberg and I'd love to connect. >> Awesome. Joe, thanks so much for your time. Really appreciate it. >> It was great to be here. Thank you, Dave. >> All right, I hope you've enjoyed our program today. You know, we heard Scott now he helped us understand this notion of data fabrics and smart data fabrics and how they can address the data challenges faced by the vast majority of organizations today. Jess Jody's demo was awesome. It was really a highlight of the program where she showed the smart data fabrics inaction and Joe Lichtenberg, we just heard from him dug in to some of the prominent use cases and proof points. We hope this content was educational and inspires you to action. Now, don't forget all these videos are available on Demand to watch, rewatch and share. Go to theCUBE.net, check out siliconangle.com for all the news and analysis and we'll summarize the highlights of this program and go to intersystems.com because there are a ton of resources there. In particular, there's a knowledge hub where you'll find some excellent educational content and online learning courses. There's a resource library with analyst reports, technical documentation videos, some great freebies. So check it out. This is Dave Vellante. On behalf of theCUBE and our supporter, InterSystems, thanks for watching and we'll see you next time. (upbeat music)

>> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities including data exploration, business intelligence natural language processing, and machine learning directly within the fabric, makes it faster and easier for organizations to gain new insights and power intelligence, predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi. Yeah, thank you so much for having me. And so for this demo we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software or adverse reaction warnings from a clinical risk grouping application and so much more. So I'm really going to be assimilating a patient logging on in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here and I'm going to be looking for information where the last name of this patient is Simmons and their medical record number their patient identifier in the system is 32345. And so as you can see I have this single JSON payload that showed up here of just relevant clinical information for my patient whose last name is Simmons all within a single response. So fantastic, right? Typically though when we see responses that look like this there is an assumption that this service is interacting with a single backend system and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here we have our data fabric coordinator which is going to be in charge of this refinement and analysis those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end we do also support full lifecycle API management within this platform. When you're dealing with APIs I always like to make a little shout out on this that you really want to make sure you have enough like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what contact. >> Can I just interrupt you for a second? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you can have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry and API securities are really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of security. >> And that's been designed in, >> Absolutely, yes. it's not a bolt-on as they like to say. Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like FIRE, interactions with a homegrown enterprise data warehouse for instance may use SQL for a cloud-based solutions managed by a vendor. They may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and and applications. And I'm about to log out so I'm going to keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST or SOAP or SQL or FTP regardless of that protocol there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as, in healthcare we have H7, we have FIRE we have CCDs across the industry. JSON is, you know, really hitting a market strong now and XML, payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR I'm leveraging a standard healthcare messaging format known as FIRE, which is a rest based protocol. And then when I'm working with my health record management system I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So let's, why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out of the box or black box approach to be able to develop things that are specific to their data fabric or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you cannot, you not only get an opportunity to view how we're establishing these connections or how we're building out these processes but you have the opportunity to inject your own kind of processing your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out of the box code that is provided in this data fabric platform from IRIS combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out of the box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. >> It's a lot here. You know, actually, if I could >> I can pause. >> If I just want to sort of play that back. So we went through the connect and the collect phase. >> And the collect, yes, we're going into refine. So it's a good place to stop. >> Yeah, so before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know the ability to bring in different dev tools. We heard about FIRE, which of course big in healthcare. >> Absolutely. >> And that's the standard and then SQL for traditional kind of structured data and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely, and I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refine. >> We're going into refinement, exciting. So how do we actually do refinement? Where does refinement happen and how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator or stands for smart data fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information it's aggregating it and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like and as you can see it follows a flow chart like structure. So there's a start, there is an end and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL Logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce or we make this data fabric a bit smarter and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging 'cause you need to be able to know, you know, if there was an issue, where did that issue happen, in which connected process and how did it affect the other processes that are related to it. In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric to when data was sent back out from that smart data fabric. So I didn't record the time but I bet if you recorded the time it was this time that we sent that request in. And you can see my patient's name and their medical record number here and you can see that that instigated four different calls to four different systems and they're represented by these arrows going out. So we sent something to chart script to our health record management system, to our clinical risk grouping application into my EMR through their FIRE server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems and we bundle them together. And for my FIRE lovers, here's our FIRE bundle that we got back from our FIRE server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping or errors that were thrown alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Etcher, who did what, when, where what did the machine do? What went wrong and where did that go wrong? >> Exactly. >> Right in your fingertips. >> Right, and I'm a visual person so a bunch of log files to me is not the most helpful. Well, being able to see this happened at this time in this location gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information, it's transforming that data, in a format that your consumer's not going to understand it's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? This all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well we can keep going. 'Cause right now, I mean we can, oh, we're at 18 minutes. God help us. You can cut some of this. (laughs) I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this but essentially if we go back to our coordinator here we can see here's that original that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at and we're running it through a machine learning model that exists within the smart data fabric pipeline and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL like syntax to be able to construct and execute these predictions. So it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge. >> Yes. >> Right, because it directly affects the cost of for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment or you know, as an outpatient perhaps to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely, absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day which is what makes this so exciting. >> Awesome demo. >> Thank you. >> Fantastic people, are you cool? If people want to get in touch with you? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy and we'd love to hear from you. I always love talking about this topic, so would be happy to engage on that. >> Great stuff, thank you Jess, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment we're going to dig into the use cases where data fabric is driving business value. Stay right there.

[upbeat electronic music] >> Hi everyone, welcome back to theCUBE's coverage of AnsibleFest 2021. I'm John Furrier, your host of theCUBE. We're here with Eric Pennington, Director of Solutions Engineering, and Mike Todaro, Senior Epic Cache Consultant at Sapphire Health. Gentlemen, thank you for coming on theCUBE and chatting about the wave of Cloud, cloud-native, Sapphire Health and Ansible. Thanks for coming on. >> Thanks for having us. >> Thank you. >> So, let's get started. Can you guys just briefly describe Sapphire Health and what you guys are doing there. The consulting services, the trends that you're seeing. Just take a step, a minute to describe the environment at Sapphire Health and what you guys are doing. >> For sure, yeah. So, Sapphire Health was a consultancy that was founded by the CEO back in 2016, Austin Park, who also serves as a CTO for some healthcare organizations, because he was having difficulty finding an organization that really specialized in Epic infrastructure. So you might be familiar with some of the large players in Epic consultancies, but they are typically focused more on the application side, so configuring like the ambulatory clinical system or something like that. And there really wasn't a solution that he could find in the market for an organization that was focused on Epic infrastructure and some of the more technical components of managing an Epic technical ecosystem. So, Austin founded a team. Mike was one of the early folks to join. I joined a little bit later. But he put a team together to, again, really focus on the technical components of an Epic implementation. And since then, we've been providing managed services for Epic infrastructure for a number of organizations. We've been focusing on platform migrations from, for example, AIX to REL for Epic organizations, and we've been focusing on some growth areas as well in the Cloud. Epic systems is now able to be hosted on the public Cloud, that's a relatively recent occurrence. So, we're working with some organizations in that space as well. Mike, anything you'd add there? >> No, I think that pretty much covers it. We've spent a large fraction of our effort making sure that we're engineering solutions for these clients that move them in the directions towards Cloud readiness, towards containerization, automation, and those sorts of things. I think Eric's description's spot on. >> So, you guys must be busy. I mean, I can only imagine the action happening right now as people realized, with the pandemic specifically, two areas that we've reported aggressive growth on was public sector and healthcare. Both were under massive strains of pressure to get faster. (chuckles) Can you guys just weigh in real quickly on what you guys are seeing and how that's impacted your consulting services, but also the customer. What's going on in their minds? >> Absolutely, we had some customers very early on in the beginning of the pandemic where we were given the cadence of updates coming from Epic, the needs for growth for those customers where both in ICU surge capability as well as just general admittance. There was a flurry of hardware purchasing, provisioning, set up. An increased cadence around patching for various pieces of the Epic environment including Epic code directly. All of those things. The tempo of all of that increased once the pandemic began, and we spent a significant fraction of time trying to find better ways, faster ways to engineer what we were already doing for clients, simply so that we could continue to keep up with the surge in demand without requiring an additional surge in investment in people, where it wasn't necessary. Obviously, some growth was necessary, but we wanted to help our clients get the most out of what they already had so that they could spend that money where it was needed to help patients. >> Yeah, awesome, great stuff. So, we're here at AnsibleFest getting into the action. It's all about automation. So I have to ask you guys, what led you to start exploring automation solutions at Sapphire Health? >> Yeah, so there's quite a few reasons. I would say the most critical is that we've been providing managed services to organizations around infrastructure management for some time. And as you can imagine, infrastructure management has some repetitive tasks, and I'm quoting my colleague, Mike, here, but a good administrator is a lazy administrator. And what we mean when we say that is, if there's a repetitive task that's being performed over and over again, if there's an opportunity to automate it, that's going to save us time. But more importantly, that's going to... Paul, these lights here. Let me move around a little bit, should come back, there we go. But it's going to provide an opportunity for us to focus on more value-add services for the client. It's going to reduce costs for the client in terms of the services that we're providing. And I think most importantly, it's removing the possibility for human error or the possibility for error overall. So it's a natural evolution of us observing the time that we're spending with our client partners, and again, it really provides a lot of value to Sapphire as an organization and our customer partners as well. >> Mike, you want to weigh in on this automation trend. How do you see it evolving? I mean, obviously sounds good when you want to automate things that you do repetitive tasks, but is there more going on that you see in automation that goes beyond just, okay, if you do it three times-automated kind of vibe. >> Sure. Automating repetitive tasks is the kiddie end of the pool. That's how we get... That's how we sell the idea to people who just don't get the concept yet. But there are workflows that really aren't feasible outside of automation. We tend to think of automation, in some cases in this sort of limited way, but automation is really... What we really are targeting with automation is more about workflow. It's less about individual tasks, and it's more about an idea of workflow or a business requirement from its origin all the way through its implementation. So, I've got just the simplest case that jumps immediately to mind, is I have a new hire, I've got to provision them an account. I need to provision it across multiple systems. I've got to do it in our single sign on. They need home directories. They might need access. They need building accesses we need to generate. You got to generate badges for these people. And these are all workflows that are normally disparate. You know, you have to take your sheet to this guy, take your sheet to this guy, here's my new hire form. Really, what you really want is, we got a new hire, everything's checked out, put it in this basket here and let the automation move it through all of these systems all the way across. And that's the sort of thing, like I said, that's a very limited, very simple idea, but that's the kind of thing we really want. We want to get in the door with automation with simple things and then we want to teach... We want clients and ourselves to be challenged, to be creative, to find new ways to apply it that aren't immediately obvious. >> Yeah, I was smiling because I love the example of the kiddie end of the pool because automation is going mainstream, and it used to be kind of, you know, for the geeks who were doing the hardcore stuff who got the whole big picture. Now you're seeing with AI automation moving in and with Cloud, a lot more automation happening. So, I can almost see in my mind mental image of people wearing bubbles in the pool, kind of like going in the deep end, get back over here. Stay in your lane. Yeah, but this is the trend, and I want to get into this because you guys are involved in this Epic migration that's been talked about. So for the folks that aren't in, say the health care space, put a little context around Epic and then I want to get into this whole migration discussion. I think that kind of points to some real value propositions. So, what is Epic for the folks outside healthcare? >> Sure, so Epic is one of the leading EHRs or electronic health records software in the world. It is by far the most deployed in the United States. What's involved in building an Epic, or performing an Epic migration. Epic is hundreds of systems. When you think about Epic as an umbrella concept, it is servers and end-user workstations and all of these things. When we talk about platform migration, what we're usually talking about is the transactional database. They call it the ODB or whichever term I think you feel applies best. When we perform all those migrations, we're usually talking about... When we perform one of those migrations, we're usually talking about an AIX to Red Hat migration, although you can just do hardware to hardware. Involved in that is a number of things. You're building new VMs. You're setting up patch cycles, setting up the patching server. Installing the various administration scripts that Epic provides. Installing the software that runs the DB, which at the moment is either InterSystems Cache or Iris. There's the provisioning of the local security users. There's the configuration of the OS. If you're moving from AIX to Red Hat, you're talking generally about a bit endians conversions, so, big endian to little endian, there's a tool for that. There's a lot of these little stats. And the thing is, is that, they're all very, very well defined and very similar, and so, they look identical in many of these cases from one implementation of Epic to the next. And that's not true for the entire Epic stack necessarily, but at the ODB level, this stuff is all very similar, and this is a very right place to automate. This screams automate, and we do this because, I mean, who wants to make mistakes. If you write and build your script and debug it, the script runs, it doesn't make mistakes. I make mistakes, the script doesn't. So, we do that, and we end up spending less time on these repetitive, unnecessary tasks. We guarantee the correctness of them, or we do a better job of guaranteeing the correctness of them, and all of that ends up saving money in the long run. >> That's awesome, and thanks for the context. I was going to get there on the automation piece. It really sets the table for the automation. Real quick clarification. How much or what kind of software work is involved in a migration? >> Oh, so there's the installation of... You have from the installation of the OS and the configuration of the OS, the building in the patch server, the implementation, testing, and patch cycling. There's those data conversions I talked about. There's environment refreshes where we copy an existing environment on a regular basis to another environment for things like testing, for troubleshooting purposes or for other reasons. There's more than one database for Epic. There's one big production database. You have training databases, and you have playground databases for people to work in so they can learn to use the system better, and then there are, I mean, there's a galaxy. >> Oh man, so it's a huge system. Okay, so I got to ask the security question. >> Sure. >> Is security element as important when selecting automation or how has that factored in? I mean, right now that's super important, obviously, records are key, but honestly, where does that fit into the automation piece of security? >> Yeah, I think that's a very important question, and as you alluded to, security is incredibly important. It's very important in healthcare in particular. And in fact, with healthcare, there's a lot of regulatory requirements. There's a lot of requirements that individual healthcare institutions have that we as a partner to that institution need to follow. So, as we were evaluating automation vendors and automation solutions, a highly secure system was not a nice to have or like a value add, it was something that was absolutely critical and paramount to being able to successfully automate any of the things that we're doing. So I'll turn it over to Mike to talk about some of the specifics, but as we evaluated Ansible, we saw that it really supported robust security. So, Mike, can you comment a little bit more on that? >> Sure. There's a number of ways that we use Ansible to help improve the security posture for clients. One of the ways is Ansible playbooks are written to be runnable against the server and nothing will change unless something is set incorrectly. And this lets us assure that the configuration is where we expect it to be so we don't get drift on these servers. Now, remember I said an Epic environment is a lot of servers. If one or two of these... >> John: Mike, if you don't mind, I need to interrupt. What is, when you say drift, what are you referring to? >> So when I say drift, what I mean is, if there's a bunch of different servers and I as an administrator have to work on one or two of these servers just for little things during the day, I might make a change on one of these servers advertently or inadvertently, and then that server's configuration is now slightly out of phase with the other servers, which could be benign, but it could also be a security hole. Having Ansible able to run nightly and continue to adjust these servers back to the expected baseline, and in the case of things like tower, be able to report that these things were out of position. Let us know, hey, it lets us reduce the attack surface, first of all. It lets us multiply it, like a force multiply our attention across this farm of servers, and it gives us that sort of clarity that we know we're doing what we have to do to make sure these servers continue to be safe. >> That's an awesome service. That right there is, I mean, just going in manually trying to figure all this stuff out, it's just a nightmare. I mean, what a great relief that is. I mean, just the alternative is what, you know, more pain and suffering human wise, that's the labor, and then risk on attack because people go to bed. >> I'm a patient. The thing is, on a personal note, I'm a patient too, all of us are. We all have doctors. We have to go to the hospital for things occasionally. And if we fail when we perform these security audits, if we fail when we perform these security checks, patient data can get lost. It can get sent to people who shouldn't have it. And I'm a patient, I have no desire for my medical information to be available anywhere but in the hands of my doctor or myself. And that's the thought I try to stay with when I'm working on these systems. I'm a patient. It's not that I'm doing this because... I mean, the knock-on effects of reducing liability for the customers cannot be ignored or overstated, and they're critical, but, ultimately, my eyesight is on the patient. >> Yeah and having that stability is huge. Okay, this brings up the whole automation thing as it becomes more mainstream for you guys, specifically, is critical. The system's there, you have to watch farms, all the action happening, it's a huge system. Complex automation is key. How are you guys continuing to push the automation envelope into the Sapphire Health's consulting practice? >> Well, as you mentioned, John, yeah, we're really taking a look at the entire technical infrastructure when we're working with our clients. And we are offering fully outsourced managed services for organizations, not just around the Epic infrastructure but things like networking devices, security and other third party systems. So with that, we're seeing a lot of these things that are going on, and we're always evaluating opportunities for automation. There's actually two areas in particular that we're seeing gain a lot of momentum with our customers, and we're seeing a lot of opportunity for automation. The first is business continuity and disaster recovery, specifically within Epic. So, Epic has very stringent requirements for resiliency, as you can imagine. When the system goes down, a hospital can't really do what it needs to do from a billing standpoint, a clinical standpoint, so very robust disaster recovery and resiliency standards and solutions are very important. However, there's not a lot of automation that's available either from Epic or, as far as I know, other consultancies, so what we did is we built a script that provides failover automation. So some of the tasks that would be very manual in terms of failing over to your DR solution, we've automated that, and that again, removes a lot of the opportunity for human error, really speeds up the failover process. And so with the customers that we work with, that's something that we provide. Another big area that we're seeing is environment refreshes. So within Epic, there are different environments that are, basically, all their data is copied over on a recurring basis from the production environment, and the refreshes can have a lot of manual steps involved, so we found an opportunity and have implemented some automation around environment refreshes for some of our managed services clients. And as we continue to go throughout, you know, building our Cloud practice in some other areas, I'm very confident that we're going to see, you know, infrastructure is code more opportunities for automation around areas like that. >> I mean, you guys got to love the DevOps vibe going on now. Mike, I mean, you guys have seen the movie before in the old legacy going back to the mainframes, so you probably still run into a lot of older systems that still do a purpose. I mean, I have a lot of friends and clients that are working in the big banks, and they still have all the old school that does their job well, but containerization and Cloud kind of give life to these systems because now we're living in this system architecture called distributed computing again with the Cloud. It's the same game, different, different stuff though. >> Absolutely. Years ago, almost every Epic client was running on AIX, and maybe not mainframe but more mini computer. The migration path for almost all of the clients has been to move from those AIX mini computers down to VMs running Red Hat, or running Linux, and the natural evolution of that path is to move at least disaster recovery data centers into the Cloud, and then for some clients, the economics say the whole data center to the Cloud. So, absolutely that path is, it's well forged, it's there. I suspect that we'll see a lot more of clients, even larger hospitals, beginning to move down that road in the near future. >> And for the folks watching who may not have the scar tissue that we have, AIX was IBM's old Unix, a kind of mid-range mini computer. It was kind of client server, it was client server going now again being modernized. So obviously Red Hat is now part of IBM, but it speaks not just to IBM, this is about Ansible, right. So this is like, there is action happening here, so this is a case study of pretty much all migrations. It's not just the fact that it's AIX to Red Hat, it's system to the new thing that has benefits. >> Absolutely. >> What's your take, Mike, on that that kind of paradigm, because a lot of people going through similar situations just change AIX to something else. You have a lot of this migration re-platforming going on with the opportunity to kind of tweak it and add stuff to it. What's your advice and what's your reaction to this big trend? >> My advice for this trend, honestly, my advice is when you're planning these migrations, you know they're coming. Even if you're not in the cycle yet, you know it's coming. My advice is start brainstorming your implementation of the automation now. Get your automation into the system as you platform into your new platform, because it is far easier to build that entire platform with automation as a critical component than it is to bolt it on later, and you will get much more out of your investment and time and effort if you've integrated it from the very beginning. I would say anyone that was looking to perform a platform migration now and hadn't already begun serious consideration of running automation or had no plans for an automation, was setting themselves up for a very long and very difficult road to hell, and I would advise against it at this point. >> Great, great insight, Mike and Eric. Thanks for coming on, appreciate your insight here. You guys want to give a quick plug for the company? What you guys are looking to do, hiring, any update you want to share because great, great content you guys just shared here. Thanks for doing that. Take a minute to put a plug for the company. >> Yeah, I think a quick plug here. Yeah, if you're a talented cache admin, there's not too many Mikes out there, so we're definitely looking for more Mikes. But more broadly, we're really looking to expand into the Cloud space. We're rapidly expanding our managed services opportunities, and what we're seeing is a lot of organizations have like one ODB admin or one client systems ECSA admin. And what they run into is that person will leave, that person will retire, that person needs to get married and go on their honeymoon. It's kind of a problem, so we're working with a lot of organizations to not just fully outsource their environment but to provide a hybrid-managed service to provide overflow, to provide capabilities, to scale up with upgrades and projects like that. So, talk to us, we're pretty darn good at it, as you heard from Mike. We've got a couple of Mikes, again, we could use more, so if you are a Mike, please reach out. >> I think we virtualized him, we just virtualized Mike, you know, virtualization is a huge trend. >> If data writes Mike, we need to do that, yeah. >> Are you a body, are you the real Mike? >> (laughing) As far as I know, my wife would appreciate it if you guys would clone me a few times. >> You know, I've heard horror stories, Eric, around root passwords, like, who has the root password, oh, she left two years ago, kind of situations, this happens. I mean, this is not... it sounds like crazy but people leave. >> Yeah, I mean, nobody works anywhere forever, right? >> Don't be that company where you lose the root password, and never mind the ransomware action. Oh my God, must be brutal. Anyway, we can go another segment on that. Eric, thank you for coming on. Mike, thank you for your insight, really appreciate it, thanks for coming on. Appreciate it. >> Absolutely. >> Absolutely, it was our pleasure. >> Stay right here for continued coverage of AnsibleFest 2021. This is theCUBE, I'm John Furrier. Thanks for watching. (slow tempo electronic music)

>>thank you very much for this opportunity to talk about Stamp with a bio design program, which is entrepreneurship education for the medical devices. My name is Julia Key Can. Oh, I am Japanese. I have seen the United States since two doesn't want on the more than half of my life after graduating from medical school is in the United States. I hope I can contribute to make them be reached between Japan that you were saying right I did the research in the period of medical devices with a patient all over the world today is my batteries met their country finished medication stamp of the city. Yeah, North Korea academia, but also a wrong. We in the industry sectors sometimes tried to generate new product which can generate revenue from their own research outward, it is explained by three steps. The first one is the debut river, which is the harbor Wrong research output to the idea which can be product eventually. That they are hard, though, is the best body, which is a hot Arboria. From idea to commercial for the other one is that we see which is a harder to make a martial hold up to become a big are revenue generating products for the academia that passed the heart is a critical on the essential to make a research output to the idea. Yeah, they're two different kind of squash for the developing process in the health care innovation, Why's bio and by all the farmer under the other one is medical device regarding the disciplining method is maybe in mechanical engineering. Electrical engineering on the medical under surgical by Obama is mainly chemical engineering, computer science, biology and genetics. However, very important difference off these to be the innovation process. Medic is suitable on these digital innovation and by Obama, is suitable discovery process needs. Yeah, in general transformation of medical research between the aroma academia output to the commercial product in the medical field is called bench to bed. It means from basically such to critical applications. But it is your bio on the path. Yeah, translation. Medical research for medical devices is better. Bench on back to bed, which means quicker Amit needs to bench on back to Greek application. The difference off the process is the same as the difference off the commercialization. Yeah, our goal is to innovate the newer devices for patient over the war. Yeah, yeah, there are two process to do innovation. One is technology push type of innovation. The other one is news, full type of innovation. Ignore the push stop Innovation is coming from research laboratory. It is suitable for the farm on the bios. Happy type of innovation. New, useful or used driven type of type of innovation is suitable for medical devices. Either Take this topic of innovation or useful type of innovation. It is important to have Mini's. We should think about what? It's waas Yeah, in 2001 stop for the Cube, API has started to stop with Bio Design program, which is on entrepreneurship education for medical devices. Our mission is educated on empowering helps technology, no based innovators on the reading, the transition to a barrier to remain a big innovation ecosystem. Our vision is to be a global leader in advancing Hearst technology innovation to improve lives everywhere. There are three steps in our process. Off innovation, identify invent on England. Yeah, yeah. The most important step is the cluster, which is I didn't buy. I didn't buy a well characterized needs is the Vienna off a grating vision. Most of the value off medical device development is due to Iraq Obina unmet needs. So we focused in this gated by creates the most are the mosque to find on the Civic on appropriate. Yeah, our barrels on the student Hickory World in March, disparate 19 that ideally include individual, which are background in many thing engineering on business. Yeah, how to find our needs. Small team will go to the hospital or clinic or environment to offer them the healthcare providers with naive eyes. The team focused. You look to keep all the um, it needs not technology. This method is senior CTO. It's a rocket car approach which can be applied all that design, thinking the team will generate at least 200 needs from economic needs. Next stick to identify Pace is to select the best. Amit Knees were used for different aspect, which can about it the nominees. These background current existing solutions market size on the stakeholders. Once we pick up ur madness from 200 nominees, they can move to the invention pates. Finally, they can't be the solution many people tend to invent on at the beginning base without carefree evaluating its unmet knees to result in a better tend to pouring love. Their whole idea, even amid NIS, is not what this is. Why most of the medical device innovation fail due to the lack off unmet needs. To avoid this Peter Hall, our approach is identify good needs. First on invention is the sex to generate the idea wrong. Unmet knees. We will use seven Rules off race Tony B B zero before judgment encourage wild ideas built on the ideas off. Others. Go Conte. One conversation time. Stay focused on the topic. The brainstorming is like association game. Somebody's idea can stimulate the others ideas. After generating many ideas, the next step is sleeping of idea whether use five different Dustin to embody the ideas. Intellectual property regulatory. Remember National Business Model on technology How, after this election step, we can have the best solution with system it needs, and finally team will go to the implementation pace. This place is more business oriented mothers. The strategy off business implementations on the business planning. Yeah, yeah, students want more than 50 starting up are spinning off from by design program. Let me show one example This is a case of just reputations. If patient your chest pain, most of that patient go to family doctor and trust. The first are probably Dr before the patient to General Securities. General Card, obviously for the patient Director, Geologist, Director, API geologist will make a reservation. Horta uses it. Test patient will come to the clinic people for devices in machine on his chest. Well, what? Two days? Right? That patient will visit clinic to put all the whole decency After a few days off. Analysis patient Come back to Dr to hear the result Each step in his money to pay. This is a minute, Knees. This is a rough sketch off the solutions. The product name is die. A patch on it can save about $620. Part maybe outpatient right here. >>Yeah, yeah. Life is stressful. We all depend on our heart with life source of our incredible machine. The body, however, sometimes are hard Need to check up. Perhaps you felt dizzy heart racing or know someone who has had a serious heart problem The old fashioned monitors that used to get from most doctors or bulky And you can't wear them exercising or in the shower. If appropriate for you, sudden life will provide you the eye rhythm. Zero patch to buy five inch band aid like patch would. You can apply to your chest in the comfort of your own home or in the gym. It will monitor your heart rate for up to 14 days. You never have to come into a doctor's office as you mail back. Patched us shortly after you were receiving. Easy to understand report of your heart activity, along with recommendations from a heart specialists to understand the next steps in your heart. Health sudden life bringing heart monitoring to you. >>This is from the TV broadcasting become Ah, this is a core value we can stamping on his breast. He has a connotation of the decent died. Now the company names Iris is in the public market cap off. This company is more than six billion di parts is replacing grasp all or that you see the examination. However, our main product is huge. The product lifecycle Very divisive, recent being it's. But if we can educate the human decision oil because people can build with other people beyond space and yeah, young broader stop on by design education is now runs the media single on Japan. He doesn't 15 PBS probably star visited Stamp of the diversity and Bang. He announced that Japan, by design, will runs with vampires. That problem? Yeah, Japan Barzan program has started a University of Tokyo Osaka University and we've asked corroborating with Japanese government on Japanese medical device Industry s and change it to that. Yeah, this year that it's batch off Japan better than parachute on. So far more than five. Starting up as being that's all. Thank you very much for your application.

>>from around the globe. It's the Cube with digital coverage of Red Hat. Summit 2020 Brought to you by Red Hat. >>Hi. And welcome back to the Cube's coverage of Red Hat Summit 2020. I'm stew Minuteman. And while this year's event is being held virtually, which means we're talking to all of the guests where they're coming from, one of the things that we always love about the user conference is talking to the practitioners themselves And Red Hat Summit. Of course, we love talking to customers and really happy to welcome to the program. Uh, Alejandro Lopez Asano, who's the director of e health with the Argentine Ministry of Health, Coming to us from Buenos Iris, Argentina. Alessandro, thank you so much for joining us. Thank you for having me. All right, So Ah, you know, look, healthcare obviously is, You know, normally, you know, challenging in the midst of what is happening globally. There are strange and pressures on. What? What is happening? So really appreciate. You think with us? Um, tell us a little bit about you know, the organization, and you know your role in Nike's role in supporting the company's mission. >>I'm part of the minister of girls in Argentina, Argentina Federal country. That's a national military girls, according it's Felker Healthcare System. All around the country with different provinces work, we work with the with the Ministry of Culture, which problems with the governor of problems trying to maintain and coordination the healthcare system. And we create the national policies that tried everybody. Show them to apply on the assistance that we create national incentive. This is much more. It's similar to the US, with the national government. Create incentives the province since the states adopt new new new practices and the best quality >>Excellent. So, yeah, the anytime we talk about healthcare, you know, uh, you know, medical records, of course, critically important. It's usually a key piece of, I d you know, governance, compliance in general. So what are some of the challenges that the ministry basis when it comes to you know, this piece >>of overall health care? My role in the midst of cops is exactly that. Coordinate health information systems around the country and having and access to the single sorts of medical records around the country. It's a great thing that we're trying to achieve We don't want to have a central repository, but they're going to have some kind of have that allows you to access information for all around the country. So the fragmentation of the seat between different provinces and also having public providers and private providers. It's a challenge because the information for one patient is this. Turn a lot of different places. I need to have some kind off have or enterprise services. But you're allows you to gather this information at the point of care and to provide the best quality of care for the patient having the full road regardless of work. It was taking her before. >>Yeah, pretty Universal Challenger talking about their distributed architecture, obviously security of Paramount performance, but still has to have the scale and performance that customers need to bring us in a little bit. This this project, you know, how long has this national health information system? How long has it been to put that together, Bring us through a little bit as to you know, how you choose how to architect these pieces, >>except that we've been working on for the last three years and then be able to create an architecture that was not invasive, that anyone can collaborate and contribute to this information network, but still having the on the rights and other responsibility for Monday in their own data. And we didn't want to have a central that the rates that it's acceptable security issues or privacy issues. We wanted information to remain distributed. But to be able to collect that a 10 point so they're able to create a set off AP Eyes Bay seven Healthcare interoperability standards that allow developers off critical systems all around the country to adopt this new way of changing information to your and privately provided to the practitioners so they can access information. Another side, >>Excellent. And so three years. You know, that's a rather big project. You've got quite a lot of constituents, and obviously, you know, healthcare is, you know, completely essential and critical service. There, underneath the pieces obviously were part of Red Hat Summit covering this so help us understand a little bit, you know, Red Hat and any other partners. You know what technologies they're using to deliver this? >>That's the big challenge was to have this kind of distributed organization with a central how that needs to provide services around the country at any time today. And we really think people need to be confident that they can use this network, that we're treating patients. We don't want them to try to do it and fail from the lost confidence in that you're not going to have the greater adoption from system developers. We need to have a very strong and company in the world, and this can grow really exponentially cause data. I mean, any chess is constructing, like one billion right work on math or something like that. But we know we can grow exponentially, but we need to have some kind of infrastructure that was reliable, but it was easy to deploy the first time. But the house and growth road map that will allow us to incorporate all the extra capacity around Argentina, Mr Safeway Way, need to be confident that we can grow a dog's level. So basically we were working already. We're Kalina and all the basic things. We wanted to go to open shift. It was really important to be able to have the container station system that allows us to found according to the needs and the adoption, right? That was really unpredictable because we need to create incentives for election. But you never know how fast the adoption would be. We need to have some flexibility of attracted by open ship, but also, we need to use a P. I like the scale in order to provide this way to communicate ap eyes to give people secure form to access the FBI's to learn about them and to try. So we're using different parts off the off the stack we have in order to do that. >>Okay, great. Tell us the adoption of this solution. How was the how is the learning curve? But, you know, moving to containerized architectures. You talking about all the AP eyes in there? How much was there a retraining of your group? Were there any new people that came in? You know what was what was Red Hat's role in really the organizational pieces of getting everybody on this on this new skill set? >>Well, the role of record was central because we didn't have the capability to go on research all these open source tools and find the proper combination between the container administrated orchestrator, the continuous integration part it was really difficult for us to start from scratch. I mean, this is something that this violent wanting to have a huge team, a lot of time, special skills and when you, because there are teams were used to work in monolithic applications with a very long development cycles that every time you need to change, we need, like, three months another. See, the change lives in the application for the end user, but we need to make a radical change there. So we saw in Red Hat Opportunity. We have a robot on the container adoption program sandcastle the steps that we need to work true. So what's really good to have our 16 team to retrain and to go through the container adoption program to use the combination of tools that breath already provides, like a stock that's the really compatible with each other. Then you need to know that that is easy to update when there are changes in their security things that they need to take to get the notification. So this and you have the daily support also because we have to create a new brand developers and the Dev Ops team was negative and you have developers and very technical person that didn't know anything about the application. We helped to create the tools that this, these new roles that combined these activities on the day to day work record expert was really key to that because they give us the roadmap. But what we need to do with timeframe with thing, that sort of statement we need to do in order on give us the daily support, the retraining, and they were really excited to work. Yeah, attempting that also was really good news for them because they were using old versions of job on old versions, off deployment systems, that they were everything by heart and the common life. And now, when they learn to do that with sensible and with the continuous integration system, a lot of menial tasks that they were doing everything you know there are automated. But that's a really great impact on the quality of life for them. >>Well, it's interesting that you talk about that, you know. Automation, of course, has been something we've been talking about for decades, but critically important today, you know, 100. I'm curious with kind of the situation happening with the pandemic. You know, people are having to work from home. There needs to be social, distancing the automation. And you know some of this new tooling. You know, what impact has that had on being able to deal with today's work >>environment? That kind of very good impact also, because not only for the automation, because that was that. It's really people have a secure way to work from home to the place ever. You don't need to access directly. Each one of the servers with logging or things like that is much more secure, much safer, much easier to work from home and maintaining the city. But also the dynamic has put a strain on the system because we are maintaining in open shift the whole family objects and violence system for Argentina, and that has much more information going through all the decision making. Politicians are getting information from the violence system and make predictions the style policies and they did. That information is to be available all the time, and previously, when a new strain came like the officially system went down, what was old workings globally So but now, with open shift, we were able to dial up more resources. The system, I maintain the quality, the world, the perimeter Signet work until the decision making person that needs information just in there. >>All right, so So all 100. We've talked about kind of a transformation that you've had. There's the government impact. There's the practice, the other providers of services. If you talk about you know, the ultimate end patient, you know what is the impact on them or you know what? What you have implemented here, >>what they did, that the patients now would be able to move between different parts of this complex system we have before. It was very common that the patient arrived hospital with about full of studies in paper, like somebody from a previous hospital finishes reported lab reports. And they have to bring about Dr and don't have to go to all the way from the foundation or a basic both from a province to the capital to get terrible, especially when they go back. And the Dr in the province don't have any information about what happened on one side that said no. They will care if you but no information. I get it through the patient. But now I think the system will integrate the older caregiver around Argentina in a much more simpler where you will be able to collaborate with doctors, another throwing, sitting, other CPIs on the patient will be able to vote from private to public. We have different kind of procedures, and every information will follow him on. Everyone will be able to take care of him with the best information. >>I'll under that. That's really powerful pieces there. So I guess the last piece is a little bit about kind of where you are with the overall project. What future goals do you have for this initiative? >>You've been really happy with the way we're starting to have adoption. We have more than 37 knows not already working in this network. And so this is really good. We have a good adoption right on. The implementation of open shift is going really well. The developers are really happy. We see the impact. That there are no downtime is really good. We need to continue transforming old legacy applications, monolithic applications to transform that into micro services. This work to do in deconstructing these big applications into more scalable micro services, and we need to take more advantage off. Sorry. Scale, Because really excellent feature for Developer portal. So, like that, everything will be about the adoption of the FBI. That information much simpler when we give all those tools developed. >>That's that. Once again, Andre, thank you so much. This has been, ah, really important work that your team is doing. Congratulations on the progress that you've made and, you know, definitely hope in the future. We will get to see you at one of the Red hat summits in person. So thank you so much for joining us. Thank you very much. All right, Lots more coverage from the cube at Red Hat Summit 2020. I'm stew minimum. And thank you. As always for watching the Cube. >>Yeah, yeah, yeah, yeah.

>> from Burlingame, California It's the Cube covering Suma logic Illuminate 2019. Brought to You by Sumer Logic >> Hey, welcome back, everybody. Jeffrey here with the Cube were at the higher Regency San Francisco Airport at Suma Logic, Illuminate, 2019 were here last year for our first time. It's a 30 year the show. It's probably 809 100 people around. 1000 packed house just had the finish. The keynote. And we're really excited to have our first guest of the day. Who's been here since the very beginning is Bruno Critic, the founding VP of product and strategy for Suma Logic, you know, great to see you. Likewise. Thank you. So I did a little homework and you're actually on the cube aws reinvent, I think 2013. Wow. How far has the cloud journey progressed? Since efforts? I think it was our first year at reinvented as well. >> That's the second year agreement, >> right? So what? What an adventure. You guys made a good bet six years ago. Seems to be paying off pretty well. >> It really has been re kind of slipped out that the cloud is gonna be a real thing. Put all of our bats into it and have been executing ever since. And I think we were right. They think it is no longer a question. Is this cloud thing gonna be re alarm enterprise gonna adopt it? It's just how quickly and how much. >> Right? Right. But we've seen kind of this continual evolution, right? Was this jump into public cloud? Everybody jumped in with both feet, and now they're pulling back a little bit. But now really seen this growth of the hybrid cloud Big announcement here with Antos and Google Cloud Platform and in containers. And, you know, the rise of doctor and the rise of kubernetes. So I don't know, a CZ. You look a kind of the evolution. A lot of positive things kind of being added to the ecosystem that have helped you guys in your core mission. >> That's right. Look, you know, five years ago, which is such a short time, But yet instead of the speed of the technology adoption and change, you know it's in It's in millennia. What's happened over the last few years is technology stocks have changed dramatically. We've gone from okay, we can host some v ems in the cloud and put some databases in the cloud. So we're now building micro service's architecture, leveraging new technologies like Kubernetes like Serverless Technologies and all the stuff And, you know, some one of the fastest growing technologies that's being adopted by some village custom base, actually the fastest kubernetes and also the fastest customer segment growing customer segments. ImmuLogic is multi clog customers, basically that sort of desire by enterprise to build choice into their offerings. Being able to have leverage over the providers is really coming to fruition right now, >> right? But the multi cloud almost it makes a lot of sense, right, because we're over and over. You want to put your workload in the environment that supposed appropriate for the workload. It kind of. It kind of flipped the bid. It was no longer. Here's your infrastructure. What kind of APs can you build on it? Now here's my app. Where should it run that maybe on Prem it may be in a public cloud. It may be in a data center, so it's kind of logical that we've come into this this hybrid cloud world that said, Now you've got a whole another layer of complexity that that's been added on. And that's really been a big part of the rise of kubernetes. >> That's right. And so, as you're adopting service's that are not equal, right, you have to create a layer that insulate you from those. Service is if you look a tw r continues intelligence report that we just announced today. You will also see that how customers and enterprise are adopting cloud service is is they're essentially adopting the basic and core compute storage network, and database service is there's a long, long tail of service that are very infrequently adopted. And that is because enterprise they're looking for a way to not get to lock Tintin into anyone. Service provider kubernetes Give them Give them that layer of insulation with in thoughts and other technologies like that, you are now able to seamlessly manage all those workloads rather there on your on premise in AWS in G C. P. In azure or anywhere else, >> right? So there's so much we can unpack. You're one of the things I want to touch on which you talked about six years ago, but it's even more thing appropriate. Today is kind of this scale this exponential growth of data on this exponential scale of complexity. And we, as people, has been written about by a lot of smart people, and I, we have a real hard time. Is humans with exponential growth. Everything's linear. Tow us. So as you look at this exponential growth and now we're trying to get insights. Now we've got a I ot and this machine a machine data, which is a whole another multiple orders of magnitude. You can't work in that world with a single painted glass with somebody looking at a dashboard that's trying to find a yellow light that's earned it. I'm going to go read. You don't have analytics. Your hose. >> That's right. This is no longer world of Ding dong lights, right? You can just like to say, Okay, red, green, yellow. The as sort of companies go digital right? Which is driving this growth in data, you know? Ultimately, that data is governed by Moore's law. Moore's law says machines are gonna be able to do twice as much every 18 to 24 months. Well, that guess what? They're gonna tell you what they're doing twice as much. Every 18 to 24 months, and that is an exponential growth rate, right? The challenge that is, budgets don't grow at that rate, either, right? So budgets are not exponentially growing. So how do you cope with the onslaught of this data? And if you're running a digital service, right, if you're serving your customers digital generating revenue through digital means, which is just about every industry. At this point in time, you must get that data because if you don't get the data, you can't run your business. This data is useful not just in operations and security. It's useful for general business abuse, useful in marketing and product management in sales and their complexity. And the analytics required to actually make sense of that data and serve it to the right constituency in the business is really hard. And that has been whatever we have been trying to solve, including this economics of machine. Dad and me talked about it today. Keynote. We're trying t bend the cost curve >> Moore's law >> yet delivered analytics that the enterprise can leverage to really not just operate an application but run their business >> right. So let's talk about this concept of observe ability. You've written box about it. When you talk to people about observe ability, what should they be thinking about? How are you defining it? Why is it important? >> It's great question, So observe ability right now is being defined as a technique right. The simplest way to think about it is people think, observe a witty I need to have these three data sets and I have observed ability. And then you have to ask yourself a question. First of all, what is Observe ability and why does it matter? I think there's a a big misconception in the market how people adopt this is that they think, observe abilities the end. But it isn't observe. Ability is the means of achieving a goal. And what we like to talk about is what is the goal? Observe, observe ability right now. Observe abilities talked about strictly in the devil up space, right? Basically, how am I going to get obs Erv City into an application? And it's maybe runtime how it's running, whether it's up and performance. The challenge with that is that is a pigeon pigeon hole view off, observe ability, observe ability. If you think about it, we talk about objectives during observe ability. Operability tau sa two ns Sorry could be up time in performance. Well, guess what a different group like security observe. Ability is not getting breached. Understanding your compliance posture. Making sure that you are compliant with with regular to re rules and things like that observe ability to a business person to a product manager who's who owns a P N. L. On some product is how are my users using this product powers my application being adopted where users having trouble. What are they and where's the user experience? Poor right? So all of this data is multifaceted and multi useful as multi uses and observing Tow us. Is his objectives driven? If you don't know what your object it is, observe. Ability is just a tool. >> I love that, you know, because it falls under this thing We talked about off the two, which is, you know, there's data, right, and then there's information in the data and then, but it is a useful information because it has to be applied to something that's right in and of itself. It has no value, and what you're talking about really is getting the right data to the right person at the right time, which kind of stumbled into another area, which is how do you drive innovation in an organization? In one of the simple concepts is democratization. Get more people more than data more than tools to manipulate the data. Then piano manager is gonna make a different decision based on different visibility than Security Person or the Dev Ops person. So how is how is that evolving? Where do you see it going? Where was it in the past? And you know, I think he made it interesting or remain made. Interesting thing in the keynote where you guys let your software be available to everyone. And there was a lot of people talking about giving Maur. People Maur access to the tools and more of the data so that they can start to drive this innovation >> abuse of an example of one of the one of the sort of aspects of when we talk about continued continues intelligence. What do we mean? So this concept of agile development didn't evolve because people somehow thought, Hey, why don't we just try to push court production all the time? Break stuff all the time. What's the What's the reason why that came about? It did not come about because somehow somebody decided so better. Software development model It's because cos try to innovate faster, so they they wanted Toa accelerate. How they deliver digital product and service is to their customers. And what's facilitates that delivery cycle is the feedback loop. They get out of their data. They push code early. They observed the data. They understand what it's telling them about how their customers are using their products, and service is what products are working with or not. And they're quickly baking that feedback back into their development cycles into the business business cycles. To make better Prada effectively, it evolved as a as a tool to differentiate and out innovate the competition. And that's to a large degree one of the ways that you deliver the right inside to the right group to improve your business right. And so this is applicable across all use cases in order pot. All departments are on the company, but that's just one example of how you think of this continuous innovation, continuous data from to use analytics and don't >> spend two years doing an M r d and another two years doing a P R d and then another to your shift >> When you when you actually ship it. Half of the assumptions that you made two years ago already all the main along, right? So now you've gotta go. You've wasted half of your development time, and you've only released half of the value that you could have other, >> right? Right. And your assumptions are not gonna be correct, right? You just don't know until you get that >> you think over time, like two years of kubernetes with a single digits percentage adoption technology and soon was customer base. Now it's 1/3 right? Right? Which means no things have changed. If I had made an assumption as of two years ago on communities, I would have no way wouldn't have done this announcement, >> right? Right. >> But we did it in an interactive mode and re benefit from that continuous information continues intelligence that we do in our own >> right, right? We fed Joe and the boys on lots of times so that it's a pretty interesting how fast that came and how it really kind of over took. Doctor has informed they contain it. Even the doctor, according to reporters. Still getting a Tana Tana traction >> and it's >> working in conjunction with communities. Communities allows you to manage those containers right, And Dr Containers are always part of the ecosystem. And so it's, you know, you know, it's like the management layer and the actual container layer, >> right? So as you look forward to give you the last word, you know, as we're really kind of getting into the SIA Teague World and five G's coming just around around the corner, which is gonna have a giant impact on an industrial I ity and this machine a machine communications, what are some of your priorities? What are you looking, you know, kind of a little bit down the road and keeping an eye on >> interesting question. You know, we used to think about I ot as is the new domain. We should think about I or tea. And maybe we need to build a solution for right. It turns out our biggest customers, customers and the way that I have personally reframed my thinking about Iris is the following Computational capacity is ubiquitous. Now, what used to be a modern application 345 years ago was something that your access to your laptop or three or mobile app, and maybe you're a smart watch Now the computation that you interface with runs in your doorbell, you know, in a light switch in your light bulbs and how's it runs everywhere runs in your shoe because when you're around, it talks to your phone to tell you how many steps you've taken, all the stuff right? Essentially, enterprises building application to serve their customers are simply pushing computation farther and farther into our being, like everywhere. There's now I, P Networks, CP use memory and all of those distributed computers are now running the applications that are serving us in our lives, right? And to me, that's what I ot is. It's just an extension off what the digital service is our and we interface with does, and it so happens that when you push computation farther and farther into our lives, you get more and more computers participating. You get more data, and many of our largest customers are essentially ingesting their full stack of iron devices to serve their customers >> right crazy future and you know, it just kind of this continual Adam ization to of computer store and memory. Well, Bruno, hopefully it will not be six years before we see you again. Congrats on the conference. And thanks for taking a few minutes. Absolutely. All right. He's Bruno. I'm Jeff. You're watching the Cube where? It's suma logic illuminate at the Hyatt Regency seven square port. Thanks for watching. We'll see you next time.

>> from our studios in the heart of Silicon Valley, Palo ALTO, California It is a cute conversation. >> Hello and welcome to this Special Cube conversation here in Palo Alto, California Cubes Studios Jon for your host of the Q. We retreat embodies the founder and CEO of H 20 dot ay, ay, Cuba Lem hot. Start up right in the action of all the machine learning artificial intelligence with the democratization, the role of data in the future, it's all happening with the cloud 2.0, Dev Ops 2.0, great to see you, The test. But the company What's going on, you guys air smoking hot? Congratulations. You got the right formally here with a I explain what's going on. It started about seven >> years ago on Dottie. I was was just a new fad that arrived into Silicon Valley. Today we have thousands of companies in the eye and we're very excited to be partners in making more companies becoming I first. And our region here is to democratize the eye and we've made simple are open source made it easy for people to start adapting data signs and machine learning and different functions inside their large and said the large organizations and apply that for different use cases across financial service is insurance healthcare. >> We leapfrog in 2016 and build our first closer. It's chronic traveler >> C I. We made it on GPS using the latest hardware software innovations Open source. I has funded the rice off automatic machine learning, which >> further reduces the need for >> extraordinary talent to build machine learning. >> No one has time >> today and then we're trying to really bring that automatic mission learning a very significant crunch. Time free, I so people can consuming. I better. >> You know, this is one of the things I love about the current state of the market right now. Entrepreneur Mark, as well as start of some growing companies Go public is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something like provisioning like the old eyes. I get a phD and we're seeing this in data science. I mean, you don't have to be a python coder. This democratisation is not just a tagline. It's actually the reality is of a business opportunity of whoever can provide the infrastructure and the systems four people to do. It is an opportunity. You guys were doing that. This is a real dynamic. This isn't a new way, a new kind of dynamic in the industry. The three real character >> sticks on ability to adopt. Hey, Iris Oneness Data >> is a team, a team sport, which means that you gotta bring different dimensions within your organization to be able to take advantage of data and the I and, um, you've got to bring in your domain. Scientists work closely with your data. Scientists were closely with your data. Engineers produce applications that can be deployed and then get your design on top of it. That can convince users are our strategist to make those decisions. That delays is showing up, so that takes a multi dimensional workforce to work closely together. So the rial problem, an adoption of the AI today is not just technology, it's also culture. And so we're kind of bringing those aspects together and form of products. One of our products, for example, explainable. Aye, aye. It's helping the data. Scientists tell a story that businesses can understand. Why is the model deciding? I need to take discretion. This'll direction. Why's this moral? Giving this particular nurse a high credit score? Even though she is, she has a very she doesn't have a high school graduation. That kind of figuring out those Democratic democratization goes all the way down there. It's wise, a mortal deciding what's deciding and explaining and breaking that down into English, which which building trust is a huge aspect in a >> well. I want to get to the the talent in the time and the trust equation on the next talk track, but I want to get the hard news out there. You guys are have some news driverless a eyes, your one of your core things. What's the hard Explain the news. What's the big news? >> The big news has Bean, that is, the money ball from business and money Ball, as it has been played out, has been. The experts >> were left out of the >> field and all garden is taking over and there is no participation between experts, the domain scientists and the data scientists and what we're bringing with the new product in travel see eyes, an ability for companies to take away I and become a I companies themselves. The rial air races not between the Googles and the Amazons and Microsoft's and other guy companies, software companies. The relay race is in the word pickles. And how can a company, which is a bank or an insurance giant or a health care company take a I platforms and become, take the data, monetize the data and become a I companies themselves? >> You know, that's a really profound state. I would agree with 100% on that. I think we saw that early on in the big data world round Doop doop kind of died by the wayside. But day Volonte and we keep on team have observed and they actually predicted that the most value was gonna come from practitioners, not the vendors, because they're the ones who have the data. And you mentioned verticals. This is another interesting point. I want to get more explanation from you on Is that APS are driven by data data needs domain specific information. So you can't just say I have data. Therefore, magic happens. It's really at the edge of the domain speak or the domain feature of the application. This is where the data is this kind of supports your idea that the eyes with the company's not that are using it, not the suppliers of the technology. >> Our vision has always being hosted by maker customer service for right to be focused on the customer, and through that we actually made customer one of the product managers inside the company. And the way that the doors that opened from working where it closed with some of our leading customers was that we need to get them to participate and take a eyes, algorithms and platforms that can tune automatically. The algorithms and the right hyper parameter organizations, right features and amend the right data sets that they have. There's a whole data lake around there on their data architecture today, which data sets them and not using in my current problem solving. That's a reasonable problem in looking at that combination of these Berries. Pieces have been automated in travel a, C I. A. And the new version that we're not bringing to market is able to allow them to create their own recipes, bring your own transformers and make that automatic fit for their particular race. Do you think about this as a rebuilt all the components of a race car. They're gonna take it and apply for that particular race to win. >> So that's where driverless comes in its travels in the sense of you don't really need a full operator. It kind of operates on its own. >> In some sense, it's driver less, which is in some there taking the data scientists giving them a power tool that historically before automatic machine learning your valises in the umbrella automatic machine learning they would find tune learning the nuances off the data and the problem, the problem at hand, what they're optimizing for and the right tweaks in the algorithm. So they have to understand how deep the streets are gonna be home, any layers off, off deep learning they need what particular variation and deploying. They should put in a natural language processing what context they need to the long term, short term memory. All these pieces, they have to learn themselves. And they were only a few Grand masters are big data scientist in the world who could come up with the right answer for different problems. >> So you're spreading the love of a I around. So you simplifying that you get the big brains to work on it and democratization. People can then participate in. The machines also can learn both humans and machines between >> our open source and the very maker centric culture we've been able to attract on the world's top data scientists, physicists and compiler engineers to bring in a form factor that businesses can use. And today it one data scientist in a company like Franklin Templeton can operate at the level of 10 or hundreds of them and then bring the best in data science in a form factor that they can plug in and play. >> I was having a cautious We can't Libby, who works with being our platform team. We have all this data with the Cube, and we were just talking. Wait higher data science and a eye specialist and you go out and look around. You get Google and Amazon all these big players, spending between 3 to $4,000,000 per machine learning engineer, and that might be someone under the age of 30. And with no experience or so the talent war is huge. I mean the cost to just hire these guys. We can't hire these people. It's a >> global war. >> There's no there's a talent shortage in China. There's talent shortage in India. There stand shortage in Europe and we have officers in in Europe and in India. The talent shortage in Toronto and Ottawa writes it is. It's a global shortage off physicists and mathematicians and data scientists. So that's where our tools can help. And we see that you see travelers say I as a wave you can drive to New York or you can fly to me >> off. I started my son the other days taking computer science classes in school. I'm like, Well, you know, the machine learning at a eyes kind like dog training. You have dog training. You train that dog to do some tricks that some tricks. Well, if you're a coder, you want to train the machines. This is the machine training. This is data science is what a. I possibilities that machines have to be taught. Something is a base in foot. Machines just aren't self learning on their own. So as you look at the science of a I, this becomes the question on the talent gap. Can the talent get be closed by machines and you got the time you want speed low, latent, see and trust. All these things are hard to do. All three. Balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's where we brought a I to help the day >> I travel A. C. I's concept that bringing a I to simplify it's an export system to do a I better so you can actually give it to the hands of a new data scientists so you can perform it the power off a Dead ones data centers if you're not disempowering. The data sent that he is a scientist, the park's still foreign data scientist, because he cannot be stopped with the confusion matrix, false positives, false negatives. That's something a data scientists can understand. What you're talking about featured engineering. That's something a data scientists understand. And what travelers say is really doing is helping him may like do that rapidly and automated on the latest hardware. That's what the time is coming into GPS that PTSD pews different form off clouds at cheaper, faster, cheaper and easier. That's the democratization aspect, but it's really targeted. Data Scientist to Prevent Excrement Letter in Science data sciences is a search for truth, but it's a lot of extra minutes to get the truth and law. If you can make the cost of excrement really simple, cheaper on dhe prevent over fitting. That's a common problem in our science. Prevent by us accidental bites that you introduced because the data is last right, trying to kind of prevent the common pitfalls and doing data science leakage. Usually your signal leaks. And how do you prevent those common those pieces? That's kind of weird, revolutionize coming at it. But if you put that in the box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> Aye aye for creative people, for instance. They want infrastructure. They don't wanna have to be an expert. They wanted that value. That's the consumer ization, >> is really the co founder for someone who's highly imaginative and his courage right? And you don't have to look for founders to look for courage and imagination that a lot of intra preneurs in large companies were trying to bring change to that organization. >> You know, we always say that it's intellectual property game's changing from you know I got the protocol. This is locked and patented. Two. You could have a workflow innovation change. One little tweak of a process with data and powerful. Aye, aye, that's the new magic I P equation. It's in the workforce, in the applications, new opportunities. Do you agree with that? >> Absolutely. That the leapfrog from here is businesses will come up with new business processes that we looked at. Business process optimization and globalization can help there. But a I, as you rightfully said earlier, is training computers, not just programming them. Their schooling most of computers that can now with data, think almost at the same level as a go player. Right there was leading Go player. You can think at the same level off an expert in that space. And if that's happening now, I can transform. My business can run 24 by seven at the rate at which I can assembled machines and feed a data data creation becomes making new data becomes the real value that hey, I can >> h 20 today I announcing driverless Aye, aye. Part of their flagship problem product around recipes and democratization. Ay, ay, congratulations. Final point take a minute to explain for the folks just the product, how they buy it. What's it made of? What's the commitment? How did they engage with you >> guys? It's an annual license recruit. License this software license people condone load on our website, get a three week trial, try it on their own retrial. Pretrial recipes are open source, but 100 recipes built by then Masters have been made open source and they could be plugged and tried and taken. Customers, of course, don't have to make their software open source. They can take this, make it theirs. And our region here is to make every company in the eye company. And and that means that they have to embrace it. I learn it. Ticket. Participate some off. The leading conservation companies are giving it back so you can access in the open source. But the real vision here is to build that community off. A practitioners inside large formulations were here or teams air global. And we're here to support that transformation off some of the largest customers. >> So my problem of hiring an aye aye person You could help you solve that right today. Okay, So it was watching. Please get their stuff and come get a job opening here. That's the goal. But that's that's the dream. That is the dream. And we we want to be should one day. I have watched >> you over the last 10 years. You've been an entrepreneur. The fierce passion. We want the eye to be a partner so you can take your message to wider audience and build monetization or on the data you have created. Businesses are the largest after the big data warlords we have on data. Privacy is gonna come eventually. But I think I did. Businesses are the second largest owners of data. They just don't know how to monetize it. Unlock value from it. I will have >> Well, you know, we love day that we want to be data driven. We want to go faster. I love the driverless vision travel. Say I h 20 dot ay, ay here in the Cuban John for it. Breaking news here in Silicon Valley from that start of h 20 dot ay, ay, thanks for watching. Thank you.

>> Narrator: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in San Mateo, California right at the crossroads. The building's called The Crossroads but it's right at the crossroads of 92 and 101. It's a really interesting intersection over the years as you watch these buildings that are on the corner continue to change names. I always think of the Seibel, his first building came up on this corner and we're here to see a good friend of SnapLogic and their brand new building. Gaurav Dhillon, Chairman and CEO, great to see you. >> Pleasure to be here. >> So how long you been in this space? >> Gosh, it's been about a year. >> Okay. >> Although it feels longer. It's a high-growth company so these are dog years. (laughs) >> That's right. and usually, you outgrow it before you all have moved in. >> The years are short but the days are long. >> And it's right next Rakuten, I have to mention it. We all see it on the Warriors' jerseys So now we know who they are and where they are exactly. >> No they're a good outfit. We had an interesting time putting a sign up and then the people who made their sign told us all kinds of back stories. >> Oh, good, good Alright. So give us an update on SnapLogic. You guys are in a great space at a really, really good time. >> You know, things been on a roll. As you know, the mission we set out to... engage with was to bring together applications and data in the enterprise. We have some of the largest customers in high technology. Folks like Qualcomm, Workday. Some of the largest customers in pharmaceuticals. Folks like Astrazeneca, Bristol-Meyers Squibb. In retail, Denny's, Wendy's, etc. And these folks are basically bringing in new cloud applications and moving data into the cloud. And it's really fun to wire that all up for them. And there's more of it every day and now that we have this very strong install-base of customers, we're able to get more customers faster. >> Right. >> In good time. >> It's a great time and the data is moving into the cloud, and the public cloud guys are really making bigger plays into the enterprise, Microsoft and, Amazon and Google. And of course, there's IBM and lots of other clouds. But integration's always been such a pain and I finally figured out what the snap in SnapLogic means after interviewing you >> (laughs) a couple of times, right. But this whole idea of, non-developer development and you're taking that into integration which is a really interesting concept, enabled by cloud, where you can now think of snapping things together, versus coding, coding, coding. >> Yeah Cloud and A.I, right We feel that this problem has grown because of the change in the platform. The compute platform's gone to the cloud. Data's going to the cloud. There was bunch of news the other day about more and more companies moving the analytics into the cloud. And as that's happening, we feel that this approach and the question we ask ourselves when we started this company, we got into building the born in the cloud platform was, what would Apple do if they were to build an integration product? And the answer was, they would make it like the iPhone, which is easy to use, but very powerful at the same time. And if you can do that, you can bring in a massive population of users who wouldn't have been able to do things like video chat. My mom was not able to do video chat, and believe me, we tried this and every other thing possible 'till facetime came along. And now she can talk to my daughter and she can do it without help, any assistance from teenage grandchildren on that side, Right? >> Right, Right >> So what we've done with SnapLogic, is by bringing in a beautiful, powerful, sleek interface, with a lot of capability in how it connects, snaps together apps and data, we've brought in a whole genre of people who need data in the enterprise so they can serve themselves data. So if your title has analyst in it, you don't have to be programmer analyst. You could be any analyst. >> Right >> You could be a compensation analyst, a commissions analyst, a finance analyst, an HR analyst. All those people can self-serve information, knock down silos, and integrate things themselves. >> It's so interesting because we talk a lot about innovation and digital transformation, and in doing thousands of these interviews, I think the answer to innovation is actually pretty simple. You give more people access to the data. You give them more access to the tools to work with the data and then you give them the power to actually do something once they figure something out. And you guys are really right in the middle of that. So before, it was kind of >> (laughs) Yeah >> democratization of the data, democratization of the tools to work with the data, but in the API economy, you got to be able to stitch this stuff together because it's not just one application, it's not just one data source. >> Correct >> You're bringing from lots and lots of different things and that's really what you guys are taking advantage of this cloud infrastructure which has everything available, so it's there to connect, >> (laughs) Versus, silo in company one and silo in company two. So are you seeing it though, in terms of, of people enabling, kind of citizen integrators if you will, versus citizen developers. >> Yeah. Heck Yeah. So I'll give you an example. One of our large customers... Adobe Systems, right here in San Jose has been amazingly successful flagship account for us. About 800 people at Adobe come to www.snaplogic.com, every week to self-serve data. We replaced legacy products like TIBCO, informatica web methods about four years ago. They first became a customer in 2014 and usage of those products was limited to Java programmers and Sequel programmers, and that was less than 50 people. And imagine that you have about 800 people doing self-service getting information do their jobs. Now, Adobe is unique in that, it's moved the cloud in a fantastic way, or it was unique in 2014. Now everybody is emulating them and the great success that they've had. With the cloud economic model, with the cloud ID model. This is working in spades. We have customers who've come on board in Q4. We're just rounding out Q1 and in less than 60, 90 days, every time I look, 50, 100, 200 people, from each large company, whether it's a cosmetics company, pharmaceuticals company, retailer, food merchandise, are coming in and using data. >> Right >> And it's proliferating, because the more successful they are, the better they are able to do in their jobs, tell their friends about it sort-of-thing, or next cubicle over, somebody wants to use that too. It's so interesting. Adobe is such a great example, cause they did transform their business. Used to be a really expensive license. You would try to find your one friend that worked there around Christmas >> (laughs) Cause you think they got two licenses a year they can buy for a grand. Like, I need an extra one I can get from you. But they moved to a subscription model. They made a big bet. >> Yes. Yes >> And they bet on the cloud, so now if you're a subscriber, which I am, I can work on my home machine, my work machine, go to machine, machine. So, it's a really great transformation story. The other piece of it though, is just this cloud application space. There's so many cloud applications that we all work with every day whether it's Basecamp, Salesforce, Hootsuite. There's a proliferation of these things and so they're there. They've got data. So the integration opportunity is unlike anything that was ever there before. Cause there isn't just one cloud. There isn't just one cloud app. There's a lot of them. >> Yes. >> How do I bring those together to be more productive? >> So here's a stat. The average enterprise has most cloud services or SAS applications, in marketing. On the average, they have 91 marketing applications or SAS applications. >> 91. That's the average. >> 96% of them are not connected together. >> Right. >> Okay. That's just one example. Now you go to HR, stock administration. You go into sales, CRM, and all the ancillary systems around CRM. And there is this sort of massive, to us, opportunity of knocking down these silos and making things work together. You mention the API economy and whilst that's true that all these SAS applications of APIs. The problem is, most companies don't have programmers to hook up those API's. >> Right. To connect them. >> Yes, in Silicon Valley we do and maybe in Manhattan they do, but in everywhere else in the world, the self-service model, the model of being able to do it to something that is simple, yet powerful. Enterprise great >> Right. Right >> and simple, beautiful is absolutely the winning formula in our perspective. So the answer is to let these 100 applications bloom, but to keep them well behaved and orchestrated, in kind of a federated model, where security, having one view of the world, etc., is managed by SnapLogic and then various people and departments can bring in a blessed, SAS applications and then snap them in and the input and the way they connect, is done through snaps. And we've found that to be a real winning model for our customers. >> So you don't have to have like 18 screens open all with different browsers and different apps. >> Swivel chair integration is gone. Swivel chair integration is gone. >> Step above sneakernet but still not-- >> Step above but still not. And again, it may make sense in very, very specific super high-speed, like Wall Street, high frequency trading and hedge funds, but it's a minuscule minority of the overall problems that there needs to be solved. >> Right. So, it's just a huge opportunity, you just are cleaning up behind the momentum in the SAS applications, the momentum of the cloud. >> Cloud data. Cloud apps. Cloud data. And in general, if a customer's not going to the cloud, they're probably not the best for us. >> Right. >> Right. Our customers' almost always going towards the cloud, have lots of data and applications on premise. And in that hybrid spot, we have the capability to straddle that kind of architecture in a way that nobody else does. Because we have a born in the cloud platform that was designed to work in the real world, which is hybrid. >> So another interesting thing, a lot of talk about big data over the years. Now it's just kind of there. But AI and machine learning. Artificial intelligence which should be automated intelligence and machine learning. There's kind of the generic, find an old, dead guy and give it a name. But we're really seeing the values that's starting to bubble up in applications. It's not, AI generically, >> Correct. >> It's how are you enabling a more efficient application, a more efficient workflow, a more efficient, get your job done, using AI. And you guys are starting to incorporate that in your integration framework. >> Yes. Yes. So we took the approach, 'doctor heal thyself.' And we're going to help our customers do better job of having AI be a game changer for them. How do we apply that to ourselves? We heard one our CIOs, CI of AstraZeneca, Dave Smoley, was handing out the Amazon Alexa Echo boxes one Christmas. About three years ago and I'm like, my gosh that's right. That was what Walt Mossberg said in his farewell column. IT is going to be everywhere and invisible at the same time. Right. >> Right. >> It'll be in the walls, so to speak. So we applied AI, starting about two years ago, actually now three, because we shipped Iris a year ago. The artificial intelligence capability inside SnapLogic has been shipping for over 12 months. Fantastic usage. But we applied to ourselves the challenge about three years ago, to use AI based on our born in the cloud platform. On the metadata that we have about people are doing. And in the sense, apply Google Autocomplete into enterprise connectivity problems. And it's been amazing. The AI as you start to snap things together, as you put one or two snaps, and you start to look for the third, it starts to get 98.7% accurate, in predicting how to connect SAS applications together. >> Right. Right. >> It's not quite autonomous integration yet but you can see where we're going with it. So it's starting to do so much value add that most of our customers, leave it on. Even the seasoned professionals who are proficient and running a center of excellence using SnapLogic, even those people choose to have sort-of this AI, on all the time helping them. And that engagement comes from the value that they're getting, as they do these things, they make less mistakes. All the choices are readily at hand and that's happening. So that's one piece of it >> Right. >> Sorry. Let me... >> It's Okay. Keep going. >> Illustrate one other thing. Napoleon famously said, "An army marches on its stomach" AI marches on data. So, what we found is the more data we've had and more customers that we've had, we move about a trillion documents for our customers worldwide, in the past 30 days. That is up from 10 million documents in 30 days, two years ago. >> Right. Right >> That more customers and more usage. In other words, they're succeeding. What we've found as we've enriched our AI with data, it's gotten better and better. And now, we're getting involved with customers' projects where they need to support data scientists, data engineering work for machine learning and that self-service intricate model is letting someone who was trying to solve a problem of, When is my Uber going to show up? So to speak. In industry X >> Right. Right. >> These kinds of hard AI problems that are predictive. That are forward changing in a sense. Those kind of problems are being solved by richer data and many of them, the projects that we're now involved in, are moving data into the cloud for data lake to then support AI machine learning efforts for our customers. >> So you jumped a little bit, I want to talk on your first point. >> Okay. Sorry >> That's okay. Which is that you're in the very fortunate position because you have all that data flow. You have the trillion documents that are changing hands every month. >> Born in the cloud platform. >> So you've got it, right? >> Got it. >> You've got the data. >> It's a virtual cycle. It's a virtual cycle. Some people call it data capitalism. I quibble with that. We're not sort-of, mining and selling people's personal data to anybody. >> Right. Right. >> But this is where, our enterprise customers' are so pleased to work with us because if we can increase productivity. If we can take the time to solution, the time to integration, forward by 10 times, we can improve the speed that by SAS application and it gets into production 10 times faster. That is such a good trade for them and for everyone else. >> Right. Right. >> And it feeds on itself. It's a virtual cycle. >> You know in the Marketo to the Salesforce integration, it's nothing. You need from company A to company B. >> I bet you somebody in this building is doing it on a different floor right now. >> Exactly. >> (laughs) >> So I think that's such an interesting thing. In the other piece that I like is how again, I like your kind of Apple analogy, is the snap packs, right. Because we live in a world, with even though there 91 on-averages, there's a number of really dominant SAS application that most people use, you can really build a group of snaps. Is snap the right noun? >> That's the right word. >> Of snaps. In a snap pack around the specific applications, then to have your AI powered by these trillion transactions that you have going through the machines, really puts you in a unique position right now. >> It does, you know. And we're very fortunate to have the kind of customer support we've had and, sort of... Customer advisory board. Big usages of our products. In which we've added so much value to our customers, that they've started collaborating with us in a sense. And are passing to us wonderful ideas about how to apply this including AI. >> Right. >> And we're not done yet. We have a vision in the future towards an autonomous integration. You should be able to say "SnapLogic, Iris, "connect my company." And it should. >> Right. Right. >> It knows what the SAS apps are by looking at your firewall, and if you're people are doing things, building pipelines, connecting your on-premise legacy applications kind of knows what they are. That day when you should be able to, in a sense, have a bot of some type powered by all this technology in a thoughtful manner. It's not that far. It's closer at hand than people might realize. >> Which is crazy science fiction compared to-- I mean, integration was always the nightmare right back in the day. >> It is. >> Integration, integration. >> But on the other hand, it is starting to have contours that are well defined. To your point, there are certain snaps that are used more. There are certain problems that are solved quite often, the quote-to-cash problem is as old as enterprise software. You do a quote in the CRM system. Your cash is in a financial system. How does that work together? These sort of problems, in a sense, are what McKinsey and others are starting to call robotic process automations. >> Right. >> In the industrial age, people... Stopped, with the industrial age, any handcrafted widget. Nuts, and bolts, and fasteners started being made on machines. You could stamp them out. You could have power driven beams, etc., etc. To make things in industrial manner. And our feeling is, some of the knowledge tasks that feel like widget manufactures. You're doing them over and over again. Or robotic, so to speak, should be automated. And integration I think, is ripe as one of those things and using the value of integration, our customers can automate a bunch of other repeatable tasks like quote-to-cash. >> Right. Right. It's interesting just when you say autonomous, I can't help but think of autonomous vehicles right, which are all the rage and also in the news. And people will say "well I like to drive "or of course we all like to drive "on Sunday down at the beach" >> Sure. Yeah. >> But we don't like to sit in traffic on the way to work. That's not driving, that's sitting in traffic on the way to work. Getting down the 101 to your exit and off again is really not that complicated, in terms of what you're trying to accomplish. >> Indeed. Indeed. >> Sets itself up. >> And there are times you don't want to. I mean one of the most pleasant headlines, most of the news is just full of bad stuff right. So and so and such and such. But one of the very pleasing headlines I saw the other day in a newspaper was, You know what's down a lot? Not bay area housing prices. >> (laughs) >> But you know what's down a lot? DUI arrests, have plummeted. Because of the benefits of Lyft and Uber. More and more people are saying, "You know, I don't have to call a black cab. "I don't need to spend a couple hundred bucks to get home. "I'm just getting a Lyft or an Uber." So the benefits of some of these are starting to appear as in plummeting DUIs. >> Right. Right >> Plummeting fatalities. From people driving while inebriated. Plunging into another car or sidewalk. >> Right. Right. >> So Yes. >> Amara's Law. He never gets enough credit. >> (laughs) >> I say it in every interview right. We overestimate in the short term and we underestimate in the long term the effects of these technologies cause we get involved-- The Gartner store. It's the hype cycle. >> Yeah, Yeah >> But I really I think Amara nailed it and over time, really significant changes start to take place. >> Indeed and we're seeing them now. >> Alright well Gaurav, great to get an update from you and a beautiful facility here. Thanks for having us on. >> Thank you, thank you. A pleasure to be here. Great to see you as well. >> Alright He's Gaurav, I'm Jeff. And you're watching theCUBE from SnapLogic's headquarters Thanks for watching. (techno music)

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time CUBE alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time Cube alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Narrator: From San Mateo, California, it's theCUBE covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in San Mateo, California right at the crossroads. The building's called The Crossroads but it's right at the crossroads of 92 and 101. It's a really interesting intersection over the years as you watch these buildings that are on the corner continue to change names. I always think of the Seville, his first building came up on this corner and we're here to see a good friend of SnapLogic and their brand new building. Gaurav Dhillon, Chairman and CEO, great to see you. >> Pleasure to be here. >> So how long you been in this space? >> Gosh, it's been about a year. >> Okay. >> Although it feels longer. It's a high-growth company so these are dog years. (laughs) >> That's right. and usually, you outgrow it before you all have moved in. >> The years are short but the days are long. >> And it's right next Rakuten, I have to mention it. We all see it on the Warriors' jerseys So now we know who they are and where they are exactly. >> No they're a good outfit. We had an interesting time putting a sign up and then the people who made their sign told us all kinds of back stories. >> Oh, good, good Alright. So give us an update on SnapLogic. You guys are in a great space at a really, really good time. >> You know, things been on a roll. As you know, the mission we set out to... engage with was to bring together applications and data in the enterprise. We have some of the largest customers in high technology. Folks like Qualcomm, Workday. Some of the largest customers in pharmaceuticals. Folks like Astrazeneca, Bristol-Meyers Squibb. In retail, Denny's, Wendy's, etc. And these folks are basically bringing in new cloud applications and moving data into the cloud. And it's really fun to wire that all up for them. And there's more of it every day and now that we have this very strong install-base of customers, we're able to get more customers faster. >> Right. >> In good time. >> It's a great time and the data is moving into the cloud, and the public cloud guys are really making bigger plays into the enterprise, Microsoft and, Amazon and Google. And of course, there's IBM and lots of other clouds. But integration's always been such a pain and I finally figured out what the snap in SnapLogic means after interviewing you >> (laughs) a couple of times, right. But this whole idea of, non-developer development and you're taking that into integration which is a really interesting concept, enabled by cloud, where you can now think of snapping things together, versus coding, coding, coding. >> Yeah Cloud and A.I, right We feel that this problem has grown because of the change in the platform. The compute platform's gone to the cloud. Data's going to the cloud. There was bunch of news the other day about more and more companies moving the analytics into the cloud. And as that's happening, we feel that this approach and the question we ask ourselves when we started this company, we got into building the born in the cloud platform was, what would Apple do if they were to build an integration product? And the answer was, they would make it like the iPhone, which is easy to use, but very powerful at the same time. And if you can do that, you can bring in a massive population of users who wouldn't have been able to do things like video chat. My mom was not able to do video chat, and believe me, we tried this and every other thing possible 'till facetime came along. And now she can talk to my daughter and she can do it without help, any assistance from teenage grandchildren on that side, Right? >> Right, Right >> So what we've done with SnapLogic, is by bringing in a beautiful, powerful, sleek interface, with a lot of capability in how it connects, snaps together apps and data, we've brought in a whole genre of people who need data in the enterprise so they can serve themselves data. So if your title has analyst in it, you don't have to be programmer analyst. You could be any analyst. >> Right >> You could be a compensation analyst, a commissions analyst, a finance analyst, an HR analyst. All those people can self-serve information, knock down silos, and integrate things themselves. >> It's so interesting because we talk a lot about innovation and digital transformation, and in doing thousands of these interviews, I think the answer to innovation is actually pretty simple. You give more people access to the data. You give them more access to the tools to work with the data and then you give them the power to actually do something once they figure something out. And you guys are really right in the middle of that. So before, it was kind of >> (laughs) Yeah >> democratization of the data, democratization of the tools to work with the data, but in the API economy, you got to be able to stitch this stuff together because it's not just one application, it's not just one data source. >> Correct >> You're bringing from lots and lots of different things and that's really what you guys are taking advantage of this cloud infrastructure which has everything available, so it's there to connect, >> (laughs) Versus, silo in company one and silo in company two. So are you seeing it though, in terms of, of people enabling, kind of citizen integrators if you will, versus citizen developers. >> Yeah. Heck Yeah. So I'll give you an example. One of our large customers... Adobe Systems, right here in San Jose has been amazingly successful flagship account for us. About 800 people at Adobe come to www.snaplogic.com, every week to self-serve data. We replaced legacy products like DIBCO, informatica web methods about four years ago. They first became a customer in 2014 and usage of those products was limited to Java programmers and Sequel programmers, and that was less than 50 people. And imagine that you have about 800 people doing self-service getting information do their jobs. Now, Adobe is unique in that, it's moved the cloud in a fantastic way, or it was unique in 2014. Now everybody is emulating them and the great success that they've had. With the cloud economic model, with the cloud ID model. This is working in spades. We have customers who've come on board in Q4. We're just rounding out Q1 and in less than 60, 90 days, every time I look, 50, 100, 200 people, from each large company, whether it's a cosmetics company, pharmaceuticals company, retailer, food merchandise, are coming in and using data. >> Right >> And it's proliferating, because the more successful they are, the better they are able to do in their jobs, tell their friends about it sort-of-thing, or next cubicle over, somebody wants to use that too. It's so interesting. Adobe is such a great example, cause they did transform their business. Used to be a really expensive license. You would try to find your one friend that worked there around Christmas >> (laughs) Cause you think they got two licenses a year they can buy for a grand. Like, I need an extra one I can get from you. But they moved to a subscription model. They made a big bet. >> Yes. Yes >> And they bet on the cloud, so now if you're a subscriber, which I am, I can work on my home machine, my work machine, go to machine, machine. So, it's a really great transformation story. The other piece of it though, is just this cloud application space. There's so many cloud applications that we all work with every day whether it's Basecamp, Salesforce, Hootsuite. There's a proliferation of these things and so they're there. They've got data. So the integration opportunity is unlike anything that was ever there before. Cause there isn't just one cloud. There isn't just one cloud app. There's a lot of them. >> Yes. >> How do I bring those together to be more productive? >> So here's a stat. The average enterprise has most cloud services or SAS applications, in marketing. On the average, they have 91 marketing applications or SAS applications. >> 91. That's the average. >> 96% of them are not connected together. >> Right. >> Okay. That's just one example. Now you go to HR, stock administration. You go into sales, CRM, and all the ancillary systems around CRM. And there is this sort of massive, to us, opportunity of knocking down these silos and making things work together. You mention the API economy and whilst that's true that all these SAS applications of APIs. The problem is, most companies don't have programmers to hook up those API's. >> Right. To connect them. >> Yes, in Silicon Valley we do and maybe in Manhattan they do, but in everywhere else in the world, the self-service model, the model of being able to do it to something that is simple, yet powerful. Enterprise great >> Right. Right >> and simple, beautiful is absolutely the winning formula in our perspective. So the answer is to let these 100 applications bloom, but to keep them well behaved and orchestrated, in kind of a federated model, where security, having one view of the world, etc., is managed by SnapLogic and then various people and departments can bring in a blessed, SAS applications and then snap them in and the input and the way they connect, is done through snaps. And we've found that to be a real winning model for our customers. >> So you don't have to have like 18 screens open all with different browsers and different apps. >> Swivel chair integration is gone. Swivel chair integration is gone. >> Step above sneakernet but still not-- >> Step above but still not. And again, it may make sense in very, very specific super high-speed, like Wall Street, high frequency trading and hedge funds, but it's a minuscule minority of the overall problems that there needs to be solved. >> Right. So, it's just a huge opportunity, you just are cleaning up behind the momentum in the SAS applications, the momentum of the cloud. >> Cloud data. Cloud apps. Cloud data. And in general, if a customer's not going to the cloud, they're probably not the best for us. >> Right. >> Right. Our customers' almost always going towards the cloud, have lots of data and applications on premise. And in that hybrid spot, we have the capability to straddle that kind of architecture in a way that nobody else does. Because we have a born in the cloud platform that was designed to work in the real world, which is hybrid. So another interesting thing, a lot of talk about big data over the years. Now it's just kind of there. But AI and machine learning. Artificial intelligence which should be automated intelligence and machine learning. There's kind of the generic, find an old, dead guy and give it a name. But we're really seeing the values that's starting to bubble up in applications. It's not, AI generically, >> Correct. >> It's how are you enabling a more efficient application, a more efficient workflow, a more efficient, get your job done, using AI. And you guys are starting to incorporate that in your integration framework. >> Yes. Yes. So we took the approach, 'doctor heal thyself.' And we're going to help our customers do better job of having AI be a game changer for them. How do we apply that to ourselves? We heard one our CIOs, CI of AstraZeneca, Dave Smoley, was handing out the Amazon Alexa Echo boxes one Christmas. About three years ago and I'm like, my gosh that's right. That was what Walt Mossberg said in his farewell column. IT is going to be everywhere and invisible at the same time. Right. >> Right. >> It'll be in the walls, so to speak. So we applied AI, starting about two years ago, actually now three, because we shipped iris a year ago. The artificial intelligence capability inside SnapLogic has been shipping for over 12 months. Fantastic usage. But we applied to ourselves the challenge about three years ago, to use AI based on our born in the cloud platform. On the metadata that we have about people are doing. And in the sense, apply Google Autocomplete into enterprise connectivity problems. And it's been amazing. The AI as you start to snap things together, as you put one or two snaps, and you start to look for the third, it starts to get 98.7% accurate, in predicting how to connect SAS applications together. >> Right. Right. >> It's not quite autonomous integration yet but you can see where we're going with it. So it's starting to do so much value add that most of our customers, leave it on. Even the seasoned professionals who are proficient and running a center of excellence using SnapLogic, even those people choose to have sort-of this AI, on all the time helping them. And that engagement comes from the value that they're getting, as they do these things, they make less mistakes. All the choices are readily at hand and that's happening. So that's one piece of it >> Right. >> Sorry. Let me... >> It's Okay. Keep going. >> Illustrate one other thing. Napoleon famously said, "An army marches on it's stomach" AI marches on data. So, what we found is the more data we've had and more customers that we've had, we move about a trillion documents for our customers worldwide, in the past 30 days. That is up from 10 million documents in 30 days, two years ago. >> Right. Right >> That more customers and more usage. In other words, they're succeeding. What we've found as we've enriched our AI with data, it's gotten better and better. And now, we're getting involved with customers' projects where they need to support data scientists, data engineering work for machine learning and that self-service intricate model is letting someone who was trying to solve a problem of, When is my Uber going to show up? So to speak. In industry X >> Right. Right. >> These kinds of hard AI problems that are predictive. That are forward changing in a sense. Those kind of problems are being solved by richer data and many of them, the projects that we're now involved in, are moving data into the cloud for data lake to then support AI machine learning efforts for our customers. >> So you jumped a little bit, I want to talk on your first point. >> Okay. Sorry >> That's okay. Which is that you're in the very fortunate position because you have all that data flow. You have the trillion documents that are changing hands every month. >> Born in the cloud platform. >> So you've got it, right? >> Got it. >> You've got the data. >> It's a virtual cycle. It's a virtual cycle. Some people call it data capitalism. I quibble with that. We're not sort-of, mining and selling people's personal data to anybody. >> Right. Right. >> But this is where, our enterprise customers' are so pleased to work with us because if we can increase productivity. If we can take the time to solution, the time to integration, forward by 10 times, we can improve the speed that by SAS application and it gets into production 10 times faster. That is such a good trade for them and for everyone else. >> Right. Right. >> And it feeds on itself. It's a virtual cycle. >> You know in the Marketo to the Salesforce integration, it's nothing. You need from company A to company B. >> I bet you somebody in this building is doing it on a different floor right now. >> Exactly. >> (laughs) >> So I think that's such an interesting thing. In the other piece that I like is how again, I like your kind of Apple analogy, is the snap packs, right. Because we live in a world, with even though there 91 on-averages, there's a number of really dominant SAS application that most people use, you can really build a group of snaps. Is snap the right noun? >> That's the right word. >> Of snaps. In a snap pack around the specific applications, then to have your AI powered by these trillion transactions that you have going through the machines, really puts you in a unique position right now. >> It does, you know. And we're very fortunate to have the kind of customer support we've had and, sort of... Customer advisory board. Big usages of our products. In which we've added so much value to our customers, that they've started collaborating with us in a sense. And are passing to us wonderful ideas about how to apply this including AI. >> Right. >> And we're not done yet. We have a vision in the future towards an autonomous integration. You should be able to say "SnapLogic, Iris, "connect my company." And it should. >> Right. Right. >> It knows what the SAS apps are by looking at your firewall, and if you're people are doing things, building pipelines, connecting your on-premise legacy applications kind of knows what they are. That day when you should be able to, in a sense, have a bot of some type powered by all this technology in a thoughtful manner. It's not that far. It's closer at hand than people might realize. >> Which is crazy science fiction compared to-- I mean, integration was always the nightmare right back in the day. >> It is. >> Integration, integration. >> But on the other hand, it is starting to have contours that are well defined. To your point, there are certain snaps that are used more. There are certain problems that are solved quite often, the quote-to-cash problem is as old as enterprise software. You do a quote in the CRM system. Your cash is in a financial system. How does that work together? These sort of problems, in a sense, are what McKinsey and others are starting to call robotic process automations. >> Right. >> In the industrial age, people... Stopped, with the industrial age, any handcrafted widget. Nuts, and bolts, and fasteners started being made on machines. You could stamp them out. You could have power driven beams, etc., etc. To make things in industrial manner. And our feeling is, some of the knowledge tasks that feel like widget manufactures. You're doing them over and over again. Or robotic, so to speak, should be automated. And integration I think, is ripe as one of those things and using the value of integration, our customers can automate a bunch of other repeatable tasks like quote-to-cash. >> Right. Right. It's interesting just when you say autonomous, I can't help but think of autonomous vehicles right, which are all the rage and also in the news. And people will say "well I like to drive "or of course we all like to drive "on Sunday down at the beach" >> Sure. Yeah. >> But we don't like to sit in traffic on the way to work. That's not driving, that's sitting in traffic on the way to work. Getting down the 101 to your exit and off again is really not that complicated, in terms of what you're trying to accomplish. >> Indeed. Indeed. >> Sets itself up. >> And there are times you don't want to. I mean one of the most pleasant headlines, most of the news is just full of bad stuff right. So and so and such and such. But one of the very pleasing headlines I saw the other day in a newspaper was, You know what's down a lot? Not bay area housing prices. >> (laughs) >> But you know what's down a lot? DUI arrests, have plummeted. Because of the benefits of Lyft and Uber. More and more people are saying, "You know, I don't have to call a black cab. "I don't need to spend a couple hundred bucks to get home. "I'm just getting a Lyft or an Uber." So the benefits of some of these are starting to appear as in plummeting DUIs. >> Right. Right >> Plummeting fatalities. From people driving while inebriated. Plunging into another car or sidewalk. >> Right. Right. >> So Yes. >> Amara's Law. He never gets enough credit. >> (laughs) >> I say it in every interview right. We overestimate in the short term and we underestimate in the long term the effects of these technologies cause we get involved-- The Gartner store. It's the hype cycle. >> Yeah, Yeah >> But I really I think Amara nailed it and over time, really significant changes start to take place. >> Indeed and we're seeing them now. >> Alright well Gaurav, great to get an update from you and a beautiful facility here. Thanks for having us on. >> Thank you, thank you. A pleasure to be here. Great to see you as well. >> Alright He's Gaurav, I'm Jeff. And you're watching theCUBE from SnapLogic's headquarters Thanks for watching. (techno music)

>> Announcer: From Milpitas, California, at the edge of Silicon Valley, it's The Cube. Covering autonomous vehicles. Brought to you by Western Digital. >> Hey, welcome back everybody, Jeff Frick here with the Cube. We're at Western Digital in Milpitas, California at the Auto Tech Council, Autonomous vehicle meetup, get-together, I'm exactly sure. There's 300 people, they get together every year around a lot of topics. Today is all about autonomous vehicles, and really, this whole ecosystem of startups and large companies trying to solve, as I was just corrected, not the thousands of problems but the millions and billions of problems that are going to have to be solved to really get autonomous vehicles to their ultimate destination, which is, what we're all hoping for, is just going to save a lot of lives, and that's really serious business. We're excited to have the guy that's kind of running the whole thing, Derek Curtain. He's the chairman of the Auto Tech Council. Derek, saw you last year, great to be back, thanks for having us. >> Well, thanks for having me back here to chat. >> So, what's really changed in the last year, kind of contextually, since we were here before? I think last year it was just about, like, mapping for autonomous vehicles. >> Yes. >> Which is an amazing little subset. >> There's been a tremendous amount of change in one year. One thing I can say right off the top that's critically important is, we've had fatalities. And that really shifts the conversation and refocuses everybody on the issue of safety. So, there's real vehicles out there driving real miles and we've had some problems crop up that the industry now has to re-double down in their efforts and really focus on stopping those, and reducing those. What's been really amazing about those fatalities is, everybody in the industry anticipated, 'oh' when somebody dies from these cars, there's going to be the governments, the people, there's going to be a backlash with pitchforks, and they'll throw the breaks on the whole effort. And so we're kind of hoping nobody goes out there and trips up to mess it up for the whole industry because we believe, as a whole, this'll actually bring safety to the market. But a few missteps can create a backlash. What's surprising is, we've had those fatalities, there's absolutely some issues revealed there that are critically important to address. But the backlash hasn't happened, so that's been a very interesting social aspect for the industry to try and digest and say, 'wow, we're pretty lucky.' and 'Why did that happen?' and 'Great!' to a certain extent. >> And, obviously, horrible for the poor people that passed away, but a little bit of a silver lining is that these are giant data collection machines. And so the ability to go back after the fact, to do a postmortem, you know, we've all seen the video of the poor gal going across the street in the dark and they got the data off the one, 101 87. So luckily, you know, we can learn from it, we can see what happened and try to move forward. >> Yeah, it is, obviously, a learning moment, which is absolutely not worth the price we pay. So, essentially, these learning moments have to happen without the human fatalities and the human cost. They have to happen in software and simulations in a variety of ways that don't put people in the public at risk. People outside the vehicle, who haven't even chosen to adopt those risks. So it's a terrible cost and one too high to pay. And that's the sad reality of the whole situation. On the other hand, if you want to say silver lining, well, there is no fatalities in a silver lining but the upside about a fatality in the self-driving world is that in the human world we're used to, when somebody crashes a car they learn a valuable lesson, and maybe the people around them learned a valuable lesson. 'I'm going to be more careful, I'm not going to have that drink.' When an autonomous car gets involved in any kind of an accident, a tremendous number of cars learn the lesson. So it's a fleet learning and that lesson is not just shared among one car, it might be all Teslas or all Ubers. But something this serious and this magnitude, those lessons are shared throughout the industry. And so this extremely terrible event is something that actually will drive an improvement in performance throughout the industry. >> That's a really good, that's a super good point. Because it is not a good thing. But again, it's nice that we can at least see the video, we could call kind of make our judgment, we could see what the real conditions were, and it was a tough situation. What's striking to me, and it came up in one of the other keynotes is, on one hand is this whole trust issue of autonomous vehicles and Uber's a great example. Would you trust an autonomous vehicle? Or will you trust some guy you don't know to drive your daughter to the prom? I mean, it's a really interesting question. But now we're seeing, at least in the Tesla cases that have been highlighted, people are all in. They got a 100% trust. >> A little too much trust. >> They think level five, we're not even close to level five and they're reading or, you know, doing all sorts of interesting things in the car rather than using it as a driver assist technology. >> What you see there is that there's a wide range of customers, a wide range users and some of them are cautious, some of them will avoid the technology completely and some of them will abuse it and be over confident in the technology. In the case of Tesla, they've been able to point out in almost every one of their accidents where their autopilot is involved, they've been able to go through the logs and they've been able to exonerate themselves and say, 'listen, this was customer misbehavior. Not our problem. This was customer misbehavior.' And I'm a big fan, so I go, 'great!' They're right. But the problem is after a certain point, it doesn't matter who's fault it is if your tool can be used in a bad way that causes fatalities to the person in the car and, once again, to people outside the car who are innocent bystanders in this, if your car is a tool in that, you have reconsider the design of that tool and you have to reconsider how you can make this idiot proof or fail safe. And whether you can exonerate yourself by saying, 'the driver was doing something bad, the pedestrian was doing something bad,' is largely irrelevant. People should be able to make mistakes and the systems need to correct those mistakes. >> But, not to make excuses, but it's just ridiculous that people think they're driving a level five car. It's like, oh my goodness! Really. >> Yeah when growing up there was that story or the joke of somebody that had cruise control in the R.V. so they went in the back to fry up some bacon. And it was a running joke when I was a kid but you see now that people with level two autonomous cars are kind of taking that joke a little too far and making it real and we're not ready for that. >> They're not ready. One thing that did strike that is here today that Patty talked about, Patty Rob from Intel, is just with the lane detection and the forward-looking, what's the technical term? >> There's forward-looking radar for braking. >> For braking, the forward-looking radar. And the crazy high positive impact on fatalities just those two technologies are having today. >> Yeah and you see the Insurance Institute for Highway Safety and the entire insurance industry, is willing to lower your rates if you have some of these technologies built into your car because these forward-looking radars and lidars that are able to apply brakes in emergency situations, not only can they completely avoid an accident and save the insurer a lot of money and the driver's life and limb, but even if they don't prevent the accident, if they apply a brake where a human driver might not have or they put the break on one second before you, it could have a tremendous affect on the velocity of the impact and since the energy that's imparted in a collision is a function of the square of the velocity, if you have a small reduction of velocity, you could have a measurable impact on the energy that's delivered in that collision. And so just making it a little slower can really deliver a lot of safety improvements. >> Right, so want to give you a chance to give a little plug in terms of, kind of, what the Auto Tech Council does. 'Cause I think what's great with the automotive industry right, is clearly, you know, is born in the U.S. and in Detroit and obviously Japan and Europe those are big automotive presences. But there's so much innovation here and we're seeing them all set up these kind of innovation centers here in the Bay area, where there's Volkswagen or Ford and the list goes on and on. How is the, kind of, your mission of bringing those two worlds together? Working, what are some of the big hurdles you still have to go over? Any surprises, either positive or negative as this race towards autonomous vehicles seems to be just rolling down the track? >> Yeah, I think, you know, Silicone Valley historically a source of great innovation for technologies. And what's happened is that the technologies that Silicone Valley is famous for inventing, cloud-based technology and network technology, processing, artificial intelligence, which is machine learning, this all Silicone Valley stuff. Not to say that it isn't done anywhere else in the world, but we're really strong in it. And, historically, those may not have been important to a car maker in Detroit. And say, 'well that's great, but we had to worry about our transmission, and make these ratios better. And it's a softer transmission shift is what we're working on right now.' Well that era is still with us but they've layered on this extremely important software-based and technology-based innovation that now is extremely important. The car makers are looking at self-driving technologies, you know, the evolution of aid as technologies as extremely disruptive to their world. They're going to need to adopt like other competitors will. It'll shift the way people buy cars, the number of cars they buy and the way those cars are used. So they don't want to be laggards. No car maker in the world wants to come late to that party. So they want to either be extremely fast followers or be the leaders in this space. So to that they feel like well, 'we need to get a shoulder to shoulder with a lot of these innovation companies. Some of them are pre-existing, so you mentioned Patti Smith from Intel. Okay we want to get side by side with Intel who's based here in Silicone Valley. The ones that are just startups, you know? Outside I see a car right now from a company called Iris, they make driver monitoring software that monitors the state of the driver. This stuff's pretty important if your car is trading off control between the automated system and the driver, you need to know what the driver's state is. So that's startup is here in Silicone Valley, they want to be side by side and interacting with startups like that all the time. So as a result, the car companies, as you said, set up here in Silicone Valley. And we've basically formed a club around them and said, 'listen, that's great! We're going to be a club where the innovators can come and show their stuff and the car makers can come and kind of shop those wares. >> It's such crazy times because the innovation is on so many axis for this thing. Somebody used in the keynote care, or Case. So they're connected, they're autonomous, so the operation of them is changing, the ownership now, they're all shared, that's all changing. And then the propulsion in the motors are all going to electric and hybrid, that's all changing. So all of those factors are kind of flipping at the same time. >> Yeah, we just had a panel today and the subject was the changes in supply chain that Case is essentially going to bring. We said autonomy but electrification is a big part of that as well. And we have these historic supply chains that have been very, you know, everyone's going as far GM now, so GM will have these premier suppliers that give them their parts. Brake stores, motors that drive up and down the windows and stuff, and engine parts and such. And they stick year after year with the same suppliers 'cause they have good relationships and reliability and they meet their standards, their factories are co-located in the right places. But because of this Case notion and these new kinds of cars, new range of suppliers are coming into play. So that's great, we have suppliers for our piston rods, for example. Hey, they built a factory outside Detroit and in Lancing real near where we are. But we don't want piston rods anymore we want electric motors. We need rare earth magnets to put in our electric motors and that's a whole new range of suppliers. That supply either motors or the rare earth magnets or different kind of, you know, a switch that can transmit right amperage from your battery to your motor. So new suppliers but one of the things that panel turned up that was really interesting is, specifically, was, it's not just suppliers in these kind of brick and mortar, or mechanical spaces that car makers usually had. It's increasing the partners and suppliers in the technology space. So cloud, we need a cloud vendor or we got to build the cloud data center ourselves. We need a processing partner to sell us powerful processors. We can't use these small dedicated chips anymore, we need to have a central computer. So you see companies like Invidia and Intel going, 'oh, that's an opportunity for us we're keen to provide.' >> Right, exciting times. It looks like you're in the right place at the right time. >> It is exciting. >> Alright Derek, we got to leave it there. Congratulations, again, on another event and inserting yourself in a very disruptive and opportunistic filled industry. >> Yup, thanks a lot. >> He's Derek, I'm Jeff, you're watching The Cube from Western Digital Auto Tech Council event in Milpitas, California. Thanks for watching and see you next time. (electronic music)

>> Voiceover: Live from San Francisco it's theCUBE. Covering AWS Summit 2017. Brought to you by Amazon Web Services. (upbeat music) >> Hey welcome back to theCUBE live at the Moscone Center at the Amazon Web Services Summit San Francisco. Very excited to be here, my co-host Jeff Rick. We're now talking to the Chief Scientist and professor at University of San Francisco, Greg Benson of SnapLogic. Greg, welcome to theCUBE, this is your first time here we're excited to have you. >> Thanks for having me. >> Lisa: So talk to us about what SnapLogic is, what do you do, and what did announce recently, today, with Amazon Web Services? >> Greg: Sure, so SnapLogic is a data integration company. We deliver a cloud-native product that allows companies to easily connect their different data sources and cloud applications to enrich their business processes and really make some of their business processes a lot easier. We have a very easy-to-use what we call self-service interface. So previously a lot of the things that people would have to do is hire programmers and do lots of manual programming to achieve some of the same things that they can do with our product. And we have a nice drag-and-drop. We call it digital programming interface to achieve this. And along those lines, I've been working for the last two years on ways to make that experience even easier than it already is. And because we're Cloud-based, because we have access to all of the types of problems that our customers run into, and the solutions that they solve with our product, we can now leverage that, and use it to harness machine-learning. We call this technology Iris, is what we're calling it. And so we've built out this entire meta-data framework that allows us to do data science on all of our meta-data in a very iterative and rapid fashion. And then we look for patterns, we look for historical data that we can learn from. And then what we do is we use that to train machinery and algorithms, in order to improve the customer experience in some way. When they're trying to achieve a task, specifically the first product feature that is based on the Iris technology is called the Integration Assistant. And the Integration Assistant is a very practical tool that is involved in the process of actually building out these pipelines. We call, when you build a pipeline it consists of these things called snaps, right? Snaps encapsulate functionality and then you can connect these snaps together. Now, it's often challenging when you have a problem to figure, OK, it's like a puzzle what snaps do I put together, and when do I put them together? Well, now that we've been doing this for a little while and we have quite a few customers with quite a few pipelines, we have a lot of knowledge about how people have solved those puzzles in the past. So, what we've done with Iris, is we've learned from all of those past solutions and now we give you automatic suggestions on where you might want to head next. And, we're getting pretty good accuracy for what we're predicting. So, we're basically, and this integration system is, a recommendation engine for connecting snaps into your pipelines as they're developing. So it's a real-time assistant. >> Jeff: So if I'm getting this right, it's really the intelligence of the crowd and the fact that you have so many customers that are executing many of the similar, same processes that you use as the basis to start to build the machine-learning to learn the best practices to make suggestions as people are going through this on their own. >> Greg: That's absolutely right. And furthermore, not only can we generalize from all of our customers to help new customers take advantage of this past knowledge, but what we can also do is tailor the suggestions for specific companies. So as you, as a company, as you start to build out more solutions that are specific to your problems, your different integration problems... >> Jeff: Right. >> The algorithms can now be, can learn from those specific things. So we both generalize and then we also make the work that you're doing easier within your company. >> And what's the specific impact? Are there any samples, stories you can share of what is the result of this type of activity? >> Greg: We're just, we're releasing it in May. >> Jeff: Oh OK. >> So it's going to be generally available to customers. >> Couple weeks still. >> Greg: Yeah. So... So... And... So... So we've done internal tests, so we've dove both through sort of the data science, so the experimentation to see, to feed it and get the feedback around how accurately it works. But we've also done user studies and what the user studies, not only did the science show but the user studies show that it can improve the time to completion of these pipelines, as you're building them. >> Lisa: So talk to us a little bit about who your target audience is. We're AWS, as we said. They really started 10 years ago in the start of space and have grown tremendous at getting to enterprise. Who is the target audience for SnapLogic that you're going after to help them really significantly improve their infrastructure get to the cloud, and beyond? >> Greg: So, so, so basically, we work with, largely with IT organizations within enterprises, who are, you know, larger companies are tasked with having sort of a common fabric for connecting, you know, which in an organization is lots of different databases for different purposes, ERP systems, you know, now, increasingly, lots of cloud applications and that's where part of our target is, we work with a lot of companies that still have policies where of course their data must be behind their firewall and maybe even on their premise, so our technology, while we're... we're hosted and run in the cloud, and we get the advantage of the SAS, a SAS platform, we also have the ability to run behind a firewall, and execute these data pipelines in the security domains of the customers themselves. So, they get the advantage of SAS, they get the advantage of things like Iris, and the Integration Assistant, right, because we can leverage all of the knowledge, but they get to adhere to any, you know, any regulatory or security policies that they have. And we don't have to see their data or touch their data. >> Lisa: So helping a customer that was, you know, using a service-oriented architecture or an ETL, modernize their infrastructure? >> Greg: Oh it's completely about modernization. Yeah, I mean, we, you know, our CEO, Gaurav Dhillon has been in the space for a while. He was formerly the CEO of Informatica. And so he has a lot of experience. And when he set out to start SnapLogic he wanted to look, you know, embrace the technologies of the time, right? So we're web-focused, right? We're HTTP and REST and JSON data. And we've centered the core technologies around these modern principles. So that makes us work very well with all the modern applications that you see today. >> Jeff: Look Greg, I want to shift gears a little bit. >> Greg: Yeah. >> You're also a professor. >> Greg: Correct. >> At University of San Francisco and UC Davis. I'd just love to get your perspective from the academic side of the house on what's happening at schools, around this new opportunity with big data, machine-learning, and AI and how that world is kind of changing? And then you are sitting in this great position where you kind of cross-over both... How does that really benefit, you know, to have some of that fresh, young blood, and learning, and then really take that back over, back into the other side of the house? >> Greg: Yeah, so a couple of things. Yeah, professor at University of San Francisco for 19 years. I did my PhD at UC Davis in computer science. And... My background is research in operating systems, parallel and distributed computing, in recent years, big data frameworks, big data processing. And University of San Francisco, itself, we have a, what we call the Senior and Masters Project Programs. Where, we've been doing this for, ever since I've been at USF, where what we do is we partner groups of students with outside sponsors, who are looking for opportunities to explore a research area. Maybe one that they can't allocate, you know, they can't justify allocating funds for, because it's a little bit outside of the main product, right? And so... It's a great win, 'cause our students get experience with a San Francisco, Silicon Valley company, right? So it helps their resume. It enhances their university experience, right? And because, you know, a lot of research happens in academia and computer science but a lot of research is also happening in industry, which is a really fascinating thing, if you look at what has come out of some of the bigger companies around here. And we feel like we're doing the same thing at SnapLogic and at the University of San Francisco. So just to kind of close that loop, students are great because they're not constrained by, maybe, some of us who have been in the industry for a while, about maybe what is possible and what's no so possible. And it's great to have somebody come and look at a problem and say, "You know, I think we could approach this differently." And, in fact, really, the impetus for the Integration Assistant came out of one of these projects where I pitched to our students, and I said "OK, we're going to explore SnapLogic meta-data and we're going to look at ways we can leverage machine-learning in the product on this data." But I left it kind of vague, kind of open. This fantastic student of mine from Thailand, his name is Jump, he kind of, he spent some time looking at the data and he actually said, "You know I'm seeing some patterns here. I'm seeing that, you know, we've got this great repository of these," like I described, "of these solved puzzles. And I think we could use that to train some algorithms." And so we spent, in the project phase, as part of his coursework, he worked on this technology. Then we demoed it at the company. The company said, "Wow, this is great technology. Let's put this into production." And then, there was kind of this transition from sort of this more academic, sort of experimental project into, going with engineers and making it a real feature. >> Lisa: What a great opportunity though, not just for the student to get more real-world applicability, like you're saying, taking it from that very experimental, investigational, academic approach and seeing all of the components within a business, that student probably gets so much more out of just an experiment. But your other point is very valid of having that younger talent that maybe doesn't have a lot of the biases and the pre-conceived notions that those of us that have been in the industry for a while. That's a great pipeline, no pun intended... >> Greg: Sure. >> For SnapLogic, is that something that you helped bring into the company by nature of being a professor? Just sort of a nice by-product? >> Well, so a couple of things there. One is that, like I said, University of San Francisco we were running this project class for a while, and... I got involved, you know, I had been at USF for a long time before I got involved with SnapLogic. I was introduced to Gaurav and there was this opportunity. And initially, right, initially, I was looking to apply some of my research to the technology, their product and their technology. But then it became clear that hey, you know we have this infrastructure in place at the university, they go through the academic training, our students are, it's a very rigorous program, back to your point about what they are exposed to, we have, you know, we're very modern, around big data, machine-learning, and then all of the core computer science that you would expect from a program. And so, yeah, it's been... It's been a great mutually beneficial relationship with SnapLogic and the students. But many other companies also come and pitch projects and those students also do similar types of projects at other companies. I would like to say that I started it at USF but I didn't. It was in existence. But I helped carry it forward. >> Jeff: That's great. >> Lisa: That is fantastic. >> And even before we got started, I mean you said your kind of attitude was to be the iPhone in this space. >> Greg: Of integration, yeah. >> Jeff: So again, taking a very different approach a really modern approach, to the expected behavior of things is very different. And you know, the consumerization of IT in terms of the expected behavior of how we interact with stuff has been such a powerful driver in the development of all these different applications. It's pretty amazing. >> Greg: And I think, you know, just like maybe, now you couldn't imagine most sort-of consumer-facing products not having a mobile application of some sort, increasingly what you're seeing is applications will require machine-learning, right, will require some amount of augmented intelligence. And I would go as far to say that the technology that we're doing at SnapLogic with self-service integration is also going to be a requirement. That, you just can't think of self-service integration without having it powered by a machine-learning framework helping you, right? It almost, like, in a few years we won't imagine it any other way. >> Lisa: And I like the analogy that Jeff, you just brought up, Greg, the being the iPhone of data integration. The simplicity message, something that was very prevalent today at the keynote, about making things simpler, faster, enabling more. And it sounds like that's what you're leveraging computer science to do. So, Greg Benson, Chief Scientist at SnapLogic. Thank you so much for being on theCUBE, you're now CUBE alumni, so that's fantastic. >> Alright. >> Lisa: We appreciate you being here and we appreciate you watching. For my co-host Jeff Rick, I'm Lisa Martin, again we are live from the AWS Summit in San Francisco. Stick around, we'll be right back. (upbeat music)