>>Que presents HP Discover 2022. Brought to You by HP >>Good morning, everyone. It's the Cube live in Las Vegas. Day two of our coverage of HP Discover 2022 from the Venetian Expo Centre. Lisa Martin and David want a what a day we had yesterday and today. Unbelievable >>for today. Big Big day today, >>Big day Today we've got a lot. We got some big heavy hitters on talking with HP customers. Partners, leadership. We've a couple of guests up with us next. Going to be talking more about the ecosystem. He's welcome. Corey Dire, the chief revenue officer, Digital Realty and Cliff Evans, senior director. H P E Green like partner ecosystem Guys. Great to have you on the >>programme. Thank you. Great to be here. >>Thank you for having us excited to be here >>with. So that's so that's harness that excitement. Cory, talk to us about the partnership. The announcement? What's going on there with Digital Realty and Green like? >>Yeah, we're crazy excited about it. You know, we've got customers dealing with data, gravity and the opportunity around that and how they could make use of it. And then they're thinking through digital transformation. How how you doing? Multi cloud and they need a partnership. To do that in this partnership with Green Leg and digital is perfect solution for them. So I'm crazy excited to be here with Cliff absolute with all of you to talk about it and hopefully build out a great partnership in relationship with HP. >>Talk to us. Sure, you're crazy Excitement >>club? Absolutely no. I think it is absolutely fantastic Partnership. I think the term is coming together as organisations. Bringing the two platforms together isn't it is an amazing thing that we have for customers, customers we know they want. They want a cloud experience. But really, they want to do that without really the DC footprint that had previously. So how did they do that in a way that really works for them in a secure client secure, sustainable way. But with the cloud experience. Really, the combination of the two pieces coming together really makes that happen, and that is what that's exciting. So we >>dig in to the two things that you mentioned Cory digital transformation and multiply. When I go back to the early days of cloud, it was that girl, you know, nobody's going to do anything you know ever again in the data centre. You know Charles Phillips, the the CEO of in four, famously said, Friends don't let friends, Bill Data centres, right? Everything's going in the cloud. So a lot of people predicted, You know, guys like you were going to be in trouble. The exact opposite happened. The market took off. So you mentioned digital transformation of multi cloud. Can we peel the onion on that? What? What is it about those two items? Are there other trends? They're driving your business, >>you know, You tied right on to to where it started. All enterprises started going to the club and then they got to the cloud and there was more that they needed to make that rial. I talk about multi cloud. You're going to use different cloud providers for different opportunities and different applications. And so you have to start thinking about how does this work in a world where you're gonna go to multiple clouds, multiple locations and what it really drove? It is the need for Cole location to make this because you've got a distributed architecture in order to enable all of this and then having to have us help you out with it. And partners like HP. That's part of where it comes from. But if you think through going to the cloud, can you stay there? Is that the full solution? You need to secure sustainable solution for that. One of the opportunities for us around that is that if you're building data centres for yourself on Prem, you don't have all the cloud access we do. We've got more cloud access points than anybody. So that helps in this digital transformation. >>How How much home? I'm sorry, Didn't mean to you how much homogeneity is there are our clients or customers saying, Hey, I kind of want the same experience in the same infrastructure. Same same. Or they saying, Hey, I want to do stuff in Digital Realty that I can't get from, you know, a cloud provider, Oracle Rack. You know, something like that, >>I would tell you that they come to us from all the partners. So we are partner community. We are not going up the stack anywhere on that. We do are we do our part. We're really good at doing the data centres really good at building data. They descended sustainable. Our position in the market is sustainability around it. We were the first to sign up on the science based initiatives for zero kind of carbon neutrality and in the future in 2030. And so yeah, so I think there's the partner aspect that they need help with on it to drive that Yeah. >>And I think from that from the HP Green Lake perspective, I think customers they very much want that that cloud experience. But I want to do on their own terms. The partnership allows that to happen on Gapen simply the cloud experiencing with the green light cloud platform to really go and deliver that genuine cloud experience and then building cloud services. On top of that, they get all the benefits that they would have from a public cloud experience, but done in the way that they would prefer to do it. So it's bringing those pieces together on >>I think the other side of you asked if it was it was the same across the board and ubiquitous. It's very bespoke. Solutions weaken D'oh! Every customer we have has a different footprint. Most from the multinationals. So we think through where their data is, where it needs to be accessed where their customers are, where their employees are, what makes the most sense. And then the partnership we have with HP into a whole lot for making very bespoke solution for that customer and help them be successful. Journey >>s O on. That s o. So what we've done with destroy lt is we have a specific offer around how we go to market with this really going how customers So we call it Green Light with co location. It's all about really positioning on offer to customers that says, Look, we can go and do this with you and do it simply and really make it happen very quickly and efficiently. So the customer ends up with a single contract in a single invoice for Green Lake Cloud Services on the co location piece, all in one single contracts. That just makes it a lot easier in terms of organising on a really big part of that as well is that our involvement is also spans right from the design to the implementation to support. So we do the whole thing to really help organisations golf and do this. So that's the big for me. The big differentiator. So rather than just having Green Lake in Cloud Services, were saying, Look, we can now do the Coehlo piece and they can really take the whole thing to a whole new level in terms of that public cloud experience >>in the sari and that that that invoice comes from HPD or Digital Realty is bundled into that >>correct? Yes, directly through the channel. We can sell that in a number of different ways. Customers get that that single invoice on a big part of that as well, just going a little bit deeper on that. So what we do is we We use a part of the company called Data Centre Technology Services, which are a great kind of consulting organisation with tremendous experience and something like 3000 projects across 40 countries from the very smallest of the very largest of data centre implementation. So all of that really makes the whole thing a lot easier from a customer's perspective in terms of designing, implementing and then supporting. So you pull all of that together. It's fantastic >>and I think it's really changed to add on to that partner in prison. So customers, now we're thinking about it differently and data centres differently, and they see us as a strategic partner along with HP. To go after this used to be space, power and calling. Now it's How much connectivity do you have? What your sustainability profile? What's your security profile? How do you secure this data? Date is the lifeblood of all these companies and you have to have a really secure, sustainable solution for them, >>right? That's absolutely critical for every industry. Talk about the specific value prop at a bespoke co location solution delivers to customers. Maybe you got a favourite customer example that you think really articulates the value of this partnership. >>So I think a combination. So so I think we touched on a lot of it, actually. So there's obviously the data centre aspect itself in terms of with the footprint that realty have across the world, you can pick and choose the data centre in the class of data centre that you want in terms of your Leighton see and connectivity that you want. Then really, it's the green make peace in terms of the flexibility that you get with that really is that value. And as I touched on the Green Lake with Cole Oh, I think for me is from our perspective, I think the biggest piece of value that we provide there to really go make it happen. Yeah, >>there's about 70 applications right now that are part of Green Lake Polo that you can bespoke for what you need to. You can think around your specific solutions that you need, and we've got it all right there with HP Green like and follow for us. And because we have a 290 data centre footprint across 50 markets, it gives us the opportunity really be the data centre provider in the Partner for H P, pretty much anywhere but with connective ity everywhere. >>When you say 70 applications, these the 70 services are you talking about talking >>about? Okay, Category 70 services. There's a lot of stuff. >>Cory, when you talked about sustainability a couple of times, is a really important ingredient of the customer decision. Why is it because they're indirectly paying the power bill or is because that's the right thing to do? And they care. There's increased. People care about it more because you go back a while ago. People way always talked about green it, but it was all lip service. Is that changing or is that there? Is there an economics >>changing in a really big way? Almost every conversation I have with customers is how are you doing Sustainability. So if they're doing an on Prem, that's not their core capabilities. They don't know how to do that. On our end, I mentioned our SP R science based initiatives that we signed up for. But how do we enable that? Enable it for how do we build in designer data centres? How do we actually work them and operate them? And then how do we go after all the green sources of sustainable energy including, I think since 2015, we've issued six billion in green bonds around that same support of it. So yeah, >>and your customer can then I presume, report that on their sustainability report a >>good way to think about it. You no longer have your data centre at its sometimes less efficient way than way are we're really good at building sustainable data centres, and then you can actually get some credits back and forth, >>just from agreement. Perspective. So Green Lake. So there's a specific Forrester Impact report that looks a green lake on how it how it performs from sustainability. Perspective on Greenlee really is giving you their 30% reduction in your energy consumption. So there's a big kind of win there as well, I think. Which is then, >>why? Where does that come from? >>So it Zim part that kind of the avoidance of over provisioning such that you going right size things, Then you have you have you have a certain amount of reserve capacity that you're using them just using the extra consumption piece when you need it. So rather than having everything running at full speed, it really is kind of struggling as to how that work. So you get a combination of effects >>with consulting and the thoughtfulness around this bespoke solution that you have. You end up needing fewer servers, pure technology that drives less power consumption and therefore you get a lot of this same really base it down. You >>talked about the savings you talked about the simplification delivery perspective. Talk about the implementation. What's the time to value that Organisations can glean from this partnership >>superfast So So yeah this This does accelerate the whole process from from initial kind of opportunity if you like and customer inquiry through to actual implementation So previously this would take considerable amount of time in terms of to ing and froing between multiple organisations on Now what we do is coordinate that do it efficiently and effectively So D. C. T s Data Sentinel services team very closely. Just have those connections often do those things incredibly quickly and it does accelerate the whole time >>and they're tied in with our team is well around. Where's the leighton? See where the solutions Because we're really thinking about what is your stack looked like from an HP perspective, but then where you need to deploy it so that you have access to the clouds You have the right proper Leighton see across your environment and you really haven't distributed architecture that works the best for you and your company. >>So this is probably answer those questions Probably both, but I'm asking anyway, I've always been a repatriation sceptic, but I'm happy to be proven wrong. You guys have other data. And maybe this is part of what one of my blind spots question is, is what's driving your business in terms of the EU's case? Is it organisations saying Hey, we want to get out of the data centre business way Don't want to put everything into the cloud but we're going to go on a digital realty and being green leg and we're gonna move into that cola Or is it? People say, You know, while we over rotated into the cloud, you were going to come back. So it's >>both. It's both, >>Yeah, in the empire. The credit. >>I think there are a lot of customers with good intentions on going to the cloud, and then there's some cost with it that maybe they didn't fully factor in it at that time. And now you've got the ability around these bespoke solutions to really right size every bit of this. And when they originally did it, they didn't think through a distributor architecture. They thought my own prim, and then I'm just gonna burst everything that a cloud that's no longer the case, and it's not really the most efficient way to your point about repatriation. They start pulling their storage back in. Well, where do you want your data? Where do you want your storage? You wanted as close as you can to the clouds for that capability and in a solution that's wrapped around it makes it very simple for you. >>I think the repatriation is very real and is increasing, eh? So we're seeing a lot of it in terms of activity and customers really trying to understand the cost that they're incurring now from a public cloud perspective. And how can they do that differently? In fact, with combined offer that we have it, it makes it a lot easier to compare. So, yeah, that really is accelerating because you don't >>see it in the macro numbers. I mean, just to be honest, you see the cloud guys combined growing 35%. And is that because your business is in transition from traditional on prime model, too, and as a service model, and so you've got that imbalance and it gets hidden in >>all that, and I think it's I think it's a new wave of things that are happening. Yeah. I mean, there's a there's a lot of things, obviously, that makes complete sense to me in Public Cloud, but I do think there's been an over rotation towards it, so I think now that realisation and it's going to take time to kind of pick that. But it's absolutely happening. There are a lot of opportunities that we've gotten some very big ones I'd love to talk about. Can't quite talk about them just get but really, where there's big, big savings in terms of what they're paying from a public cloud perspective, Really, what they want is that full management cloud service to go make it happen. So the combination of the data centre piece to Green Lake piece and then some management services, whether they're from ourselves or from party community, from manage service providers that we also work with, that gives them the complete package. >>So I have another premise. A lot of it, of course, is traditionally been focused on internal, and I feel like there's a new era coming. It's talks of the ecosystem. Are you seeing customers not only running there it in digital realty and connecting to the cloud in a hybrid fashion, but also actually building new value and building businesses that are customer facing on that that air monetize herbal. Are you seeing that? Is that happening and having examples, even generic? >>Well, basic from our perspective, our partner community, that's what they do. We have a tonne of enterprise customers, but I'll need to connect and integrate the data that you have doesn't do anything for you, Fitz on its own. And it's not interacting with other data points. And it's not around interacting with other customers, other solutions in one night. So it does help build out a partner community, a solution community for our customers in our data centres and across the >>are their industry patterns emerging. In other words, is that data ecosystems emerging by industry or is a sort of or horizontal? >>There's a mix. So I think there's a lot of lot of financial sector stuff. Yes, certainly. And then certainly manufacturing s O. I think it's interesting that you're getting a bit of a combination, but not a lot of financial sector. >>Of course, the big bags early on that they could build their own cloud. Yeah, now they're probably rethinking that. Yeah, well, maybe >>they're also service providers. When you're that large a za bank on their end. They're doing a lot of work. E. I would also say the other part that a lot of people see as an opportunity is around all the HPC and AI applications as well, in addition to manufacturing distribution. So there's a lot of use cases, a lot of reasons, like us from sort of doing this >>wrap us up with value, perhaps that you're talking Torto Financial Services Organisation or a manufacturing company. What is that 32nd elevator pitch value problem? Why they should go HP Making Digital Realty together. >>So I would say green, like Rico location gives you a single contract. Singling voice, easy to go and design, implement support and go make happen. Sorry, that's very simple way say, very just make it easy >>on. And I would just say thank you on that. It's been great to speak with you guys. And yeah, when you think through that part of it also is a bespoke opportunity to put your data where it needs to be closer to your customers. Closer to the action you were thinking through the rape reiteration of it. A lot of it's being built out there on phones and whatnot. So you've got to think through where your data is and how you managed to >>write and enable every every company in every industry to be a data company. Because that's what, of course, the demanding consumers demanding that demand isn't it is not going to turn down right now. Absolutely. Just thanks so much for David. Very much. Thank you. Together in the ecosystem, there are guests. And Dave l want a I'm Lisa Martin. You're watching the key of live from the Venetian Expo Centre in Vegas, Baby. David, I will be back there next guest in a minute.

>> live from Las Vegas. It's the queue covering del technologies. World twenty nineteen, brought to you by Del Technologies and its ecosystem partners. >> Welcome back, everyone to the cubes. Live coverage of dental technologies. World to K nineteen here in Las Vegas. I'm your host. Her back tonight along with my co host stew Minimum wear, joined by Cliff Mad Drew. He is the VP cloud solution, architecture and engineering >> at Iron Mountain. Thank you so much for coming on the Q. >> Thank you so much for having me. I truly appreciate the opportunity. >> So Iron Mountain, we know the trucks, but But there's more to the story now. So I want you to tell us a little bit about the company and about how you're expanding into new terrain. >> Absolutely. So I mean, you said it right. Most people know us for the trucks. They know us for physical asset management records management. Um and you know how we help customers protect their physical information? Um, you know, we've been through an evolution. We've been through a transformation as a company, evolving with our customers to help them as they digitally transform. And what's interesting for our customers in particular is that they live, you know, in this world of physical and a digital realm, and how do they move from one to the other? Um, and that's where we focused a lot around. Building our portfolio of services is helping our customers through that transformation along with everything that we've done, you know, in in history and through history and our legacy around protecting physical information. We've carried through into our services with a focus on what we call Iron Cloud, which is built around that same chain of custody, that same security for our customers. And we're leveraging a lot of Delhi emcee technology within Iron Cloud to make that happen for our customers. >> So as as your transforming, you are helping other companies transformed to >> wear customer focus, and we're moving right along with our customers to help enable them. >> Cliff. It's been fascinating to watch, you know, the traditional storage industry is now focused on the data more than ever. And, you know, we hear so many stats about you know how much data is available searchable. You know, I think backto iron mountains like OOO for governance, require requirement or for a legal issue or things like that I had to retain. But tell us how the changing world of data, you know, you were in a teacher. That's a data deserves better. S O. I think data's probably central to what you're talking about. An absolutely, in the cloud. How that's changing how your customers look. ATT data >> data is at the core of everything that we talked about with our customers. Um, And I work, you know, within specifically our data management group, Uh, and to your point, you know, focus on customers data. And how are they able, Teo? Either leverage the historical data that they're currently storing with us leverage the physical data that needs to be transformed into something that's digital digital, something that searchable. Um, you know, we've just recently launched Tool called Insight, which gives full analytics capabilities on some of those data sets for our customers. And then how do you maintain the protection of that data in its digital format? And, you know, even if you go to our tape based business, which is all about data protection and getting that data protected off site well, in the world where people are, you know, looking to the cloud for hybrid strategies, looking for as a service type offerings. They're trying to move away from that physicality and having to manage that information physically. And so you know, for those customers in particular, were able to take a look at their data requirements, and we're able to help them evolve that strategy to make sure that they're go forward in the cloud is meeting the same needs, whether its compliance you mentioned, you know, regulation right regulatory needs around building out a strategy, our information, governance tools around policy management. And how do you ensure the appropriate retention of that data? Well, mitigating your risk and not keeping things for too long. All of those play into the hybrid world and in particular into a multi cloud world. Right, which we hear. A lot of these shows is talking about howto leverage, you know, best in breed SAS applications and other applications that are either posted in the cloud are here. Migrating were close to the cloud, the same challenges that all of our customers have really seen with the physical assets that they've managed in the past. Those challenges still exist, but in a digital realm, right? And so it is. So you know, when you think about that, you're now creating these silos of information. Well, if eighty to ninety percent of that data is infrequently access archival, our needs to be retained. You know, Teo, to meet a compliance need. How are you? How are you still managing that? And how are you able to do that? You know, in that multi cloud world. And and that's where we're helping our customers understand the information they're managing. Understand how Teo apply policy to that data. How did you know really garner insight from that data? Because again, it's all about the data. Like you said so. >> But cybersecurity is another very important priority. Uh, let's back up a little bit and just sort of laid the foundation for our viewers about breeches and about attacks. I >> I see a statistic here. Verizon Data Breach index. Twenty eight percent of cyber attacks >> were committed by inside actors. We keep thinking about these nefarious actors being from foreign nations in these other hostile but inside. So So what is it? Talk a little bit about that? >> Absolutely. When you start to develop a you know, We like to talk a lot about cyber resiliency. So cyber security, you know, incorporates a lot of things. Some of those things are around, you know, the prevention of bad actors from gaining access to your data. But we think about a lot around. How do you ensure you can recover when you have an attack? And, you know, how do you protect the data so that you can recover the data when you have an attack? And we're trying to help our customers understand? To help them develop is a strategy around recovery, because you know that there's no such thing as complete prevention and even leveraging some of the tools and some things that have been announced at the show. You know that SecureWorks is working on and, you know, some day I base tools, although you know you can drastically reduce your risk of an attack. The reality from my perspective, is you cannot prevent an attack, and so you need to ensure the data's protected. And when you think about an insider threat, so twenty eight percent you know of attacks are from an insider perspective. And actually roughly sixty eight percent of attacks come from unnoticed for months, and so that means someone's on your network. That means they're monitoring you from the inside, and they're trying to understand you know, the patterns and how you protect things. And how can they infiltrate that process? And, you know, when when we work with customers we're looking at first. How do you identify the critical data that you could not recover your business? You know, if you were to lose it or if it were to be destroyed, and we help them build strategies with what we call critical protection of recovery are CPR service that takes a copy of that information. It's managed by Iron Mountain, which I think is one of the most critical critical aspects of the service because an insider threat, it's something that's very hard to prevent when someone understands the inner workings of your you know of your environment. So by having that that solution managed by us having that put in one of the most secure data centers in the world. So you know, we spent over two billion dollars last year on data centers, and we have some of the most secure facilities in the world. It really helps customers prevent that insider threat >> is Clifton with one word? I didn't hear that. I was expecting here in that discussion. Was Ransomware okay? Sure. How does that fit in >> church? So, I mean, ransom were just one of the multitude of different, uh, challenges that our customers are faced with when it comes to, you know, cyber protection, you know? So from a ransomware perspective in particular, uh, I think it's roughly twenty percent cos they're So you know, we're not able to recover their data from ransom where I think the number is probably even higher than that. And again, back up and disaster recovery are not cyber resiliency solutions. They can give you a level of protection, and in some cases, you can recover from ransomware by restoring a backup data set. But depending on how you're figured, if your data is online, you know, with the with the amount in particular, we know an awful lot about the tape business. One of the values of tape is being able take date offline. But again, you know, one of the things that customers are moving away from its having Teo manually, you know, manage that process. And so, with something like Iron Cloud and with CPR, we could take that data and we can create an air gap so that you have the protection from the network. So if you have a ransomware type event or something that crawls your network, you have an air gap. Now, from the network perspective, your data is isolated because of that air gap, and then the third component is really an administrative air gaff. And this is the one around any type of insider attack or ensuring that, you know one of your employees because, you know, seventeen percent of attacks or social attacks, right? So again, all the software in the world can't change. You know, uh, you know, psychological attack on one of your employees who does have access to a system. And so you know so again, having that administrative air gap is what we like to call it, where you have an independent third party that is now protecting that data in an air gapped format. And again, we offer the ability to take it down to tape so you can still have many versions to recover from, because if you have, you know, an attack that's been months on your system, and you need to get a clean version of a file. Now we have the ability to bring that into what we call a clean room. Have that friend you can run your forensics on that in a very secure environment that it gets completely isolated from, You know, where your date has been attacked and then, you know, bring that data back to recover successfully from ransom. Where any. You know any other >> you give us some >> examples of customers that air using iron cloud CPR and been in the business impact that they're seeing? >> Sure. Yeah. So you know what? One of our more recent customers is an insurance provider in the Boston area, And they, you know, they wanted to ensure that the policy data for their customers was protected against any type of attack, right, And that they could always recover that information. Um, in their case in particular, they're data domain user. They want to leverage the technology they've already invested in as a, you know, as a way to get Iron Mountain, the data and, you know, with Iron Cloud, we support, uh, CPR for data domain. So we have the ability to take that data and replicate that data to our iron cloud and then, you know, offer for the air gapping and offer the cyber resiliency solution to those customers. So, um, that customer in particular again, you know that that major data base in a couple of databases that had their customer information is what they wanted to protect. And in many cases, you know, our customers don't always know what they want to protect. So we're helping a lot of customers right now understand their data and, you know, leverage some of our advisory services. To understand what, that you know what those crown jewels are. What? You know what it is that we really need to ensure is protected from a cyber perspective. And, you know, we're also dealing with a lot of right now financial institutions. So, you know, when you get Teo, you no account information transaction data ensuring that that information is protected again. That's a strong point for cyber resiliency solution for my remount. >> So, Cliff, the expo holes right behind us over the shoulder here for the people that didn't make it to give me a little flavor as toe. You know, What's the energy been any cool things you saw And you know, any meaningful conversations or talking delivered from customers? >> Yeah. I mean, the energy is infectious in a good way, you know, It's it's it's I always love these shows, but the amount of customers and Iron Mountain particularly. We have two hundred thirty five thousand customers. A lot of our customers attend, attend these shows and to be able to engage with them and have them understand our revolution were very well known, you know, for our records business, far shredding business. And not everyone understands. It brought the services that we can offer when it comes to digital information and helping them through their transformation. So some of just the speaking engagements that I've had here, you know, the crowds of people gathering and understanding and following up at the booth. Teo, really? I understand more about how we can help and scheduling follow up sessions so that we can help them through that transformation, whether they're coming off of tape, where they have critical assets that need protection, critical data that, you know they're interested in CPR, for I've had so many engaging conversation. So it's always great. >> Look, Cliff, thank you so much for coming on the cute way. Appreciate. It was a great conversation. >> Thank you so much. >> I'm Rebecca Knight for Stew Minutemen. You've been watching the cubes live coverage of Del Technologies World. We will see you next time.

>>Hi everyone. And welcome to data citizens. Thank you for making the time to join me and the over 5,000 data citizens like you that are looking to become United by data. My name is Jim Cushman. I serve as the chief product officer at Collibra. I have the benefit of sharing with you, the product, vision, and strategy of Culebra. There's several sections to this presentation, and I can't wait to share them with you. The first is a story of how we're taking a business user and making it possible for him or her data, use data and gain. And if it and insight from that data, without relying on anyone in the organization to write code or do the work for them next I'll share with you how Collibra will make it possible to manage metadata at scales, into the billions of assets. And again, load this into our software without writing any code third, I will demonstrate to you the integration we have already achieved with our newest product release it's data quality that's powered by machine learning. >>Right? Finally, you're going to hear about how Colibra has become the most universally available solution in the market. Now, we all know that data is a critical asset that can make or break an organization. Yet organizations struggle to capture the power of their data and many remain afraid of how their data could be misused and or abused. We also observe that the understanding of and access to data remains in the hands of just a small few, three out of every four companies continue to struggle to use data, to drive meaningful insights, all forward looking companies, looking for an advantage, a differentiator that will set them apart from their peers and competitors. What if you could improve your organization's productivity by just 5%, even a modest 5% productivity improvement compounded over a five-year period will make your organization 28% more productive. This will leave you with an overwhelming advantage over your competition and uniting your data. >>Litter employees with data is the key to your success. And dare I say, sorry to unlock this potential for increased productivity, huge competitive advantage organizations need to enable self-service access to data for everyday to literate knowledge worker. Our ultimate goal at Cleaver has always been to enable this self-service for our customers to empower every knowledge worker to access the data they need when they need it. But with the peace of mind that your data is governed insecure. Just to imagine if you had a single integrated solution that could deliver a seamless governed, no code user experience of delivering the right data to the right person at the right time, just as simply as ordering a pair of shoes online would be quite a magic trick and one that would place you and your organization on the fast track for success. Let me introduce you to our character here. >>Cliff cliff is that business analyst. He doesn't write code. He doesn't know Julian or R or sequel, but is data literate. When cliff has presented with data of high quality and can actually help find that data of high-quality cliff knows what to do with it. Well, we're going to expose cliff to our software and see how he can find the best data to solve his problem of the day, which is customer churn. Cliff is going to go out and find this information is going to bring it back to him. And he's going to analyze it in his favorite BI reporting tool. Tableau, of course, that could be Looker, could be power BI or any other of your favorites, but let's go ahead and get started and see how cliff can do this without any help from anyone in the organization. So cliff is going to log into Cleaver and being a business user. >>The first thing he's going to do is look for a business term. He looks for customer churn rate. Now, when he brings back a churn rate, it shows him the definition of churn rate and various other things that have been attributed to it such as data domains like product and customer in order. Now, cliff says, okay, customer is really important. So let me click on that and see what makes up customer definition. Cliff will scroll through a customer and find out the various data concepts attributes that make up the definition of customer and cliff knows that customer identifier is a really important aspect to this. It helps link all the data together. And so cliff is going to want to make sure that whatever source he brings actually has customer identifier in it. And that it's of high quality cliff is also interested in things such as email address and credit activity and credit card. >>But he's now going to say, okay, what data sets actually have customer as a data domain in, and by the way, why I'm doing it, what else has product and order information? That's again, relevant to the concept of customer churn. Now, as he goes on, he can actually filter down because there's a lot of different results that could potentially come back. And again, customer identifier was very important to cliff. So cliff, further filters on customer identifier any further does it on customer churn rate as well. This results in two different datasets that are available to cliff for selection, which one to use? Well, he's first presented with some data quality information you can see for customer analytics. It has a data quality score of 76. You can see for sales data enrichment dataset. It has a data quality score of 68. Something that he can see right at the front of the box of things that he's looking for, but let's dig in deeper because the contents really matter. >>So we see again the score of 76, but we actually have the chance to find out that this is something that's actually certified. And this is something that has a check mark. And so he knows someone he trusts is actually certified. This is a dataset. You'll see that there's 91 columns that make up this data set. And rather than sifting through all of that information, cliff is going to go ahead and say, well, okay, customer identifier is very important to me. Let me search through and see if I can find what it's data quality scores very quickly. He finds that using a fuzzy search and brings back and sees, wow, that's a really high data quality score of 98. Well, what's the alternative? Well, the data set is only has 68, but how about, uh, the customer identifier and quickly, he discovers that the data quality for that is only 70. >>So all things being equal, customer analytics is the better data set for what cliff needs to achieve. But now he wants to look and say, other people have used this, what have they had to say about it? And you can see there are various reviews for different reviews from peers of his, in the organization that have given it five stars. So this is encourages cliffs, a confidence that this is great data set to use. Now cliff wants to look a little bit more detailed before he finally commits to using this dataset. Cliff has the opportunity to look at it in the broader set. What are the things can I learn about customer analytics, such as what else is it related to? Who else uses it? Where did it come from? Where does it go and what actually happens to it? And so within our graph of information, we're able to show you a diagram. >>You can see the customer analytics actually comes from the CRM cloud system. And from there you can inherit some wonderful information. We know exactly what CRM cloud is about as an overall system. It's related to other logical models. And here you're actually seeing that it's related to a policy policy about PII or personally identifiable information. This gets cliff almost the immediate knowledge that there's going to be some customer information in this PII information that he's not going to be able to see given his user role in the organization. But cliff says, Hey, that's okay. I actually don't need to see somebody's name and social security number to do my work. I can actually work with other information in the data file. That'll actually help me understand why our customers churning in, what can I actually do about it. If we dig in deeper, we can see what is personally identifiable information that actually could cause issues. >>And as we scroll down and take a little bit of a focus on what we call or what you'll see here is customer phone, because we'll show that to you a little bit later, but these show the various information that once cliff actually has it fulfilled and delivered to him, he will see that it's actually massed and or redacted from his use. Now cliff might drive in deeper and see more information. And he says, you know what? Another piece that's important to me in my analysis is something called is churned. This is basically suggesting that has a customer actually churned. It's an important flag, of course, because that's the analysis that he's performing cliff sees that the score is a mere 65. That's not exactly a great data quality score, but cliff has, is kind of in a hurry. His bosses is, has come back and said, we need to have this information so we can take action. >>So he's not going to wait around to see if they can go through some long day to quality project before he pursues, but he is going to come up and use it. The speed of thinking. He's going to create a suggestion, an issue. He's going to submit this as a work queue item that actually informs others that are responsible for the quality of data. That there's an opportunity for improvement to this dataset that is highly reviewed, but it may be, it has room for improvement as cliff is actually typing in his explanation that he'll pass along. We can also see that the data quality is made up of multiple components, such as integrity, duplication, accuracy, consistency, and conformity. Um, we see that we can submit this, uh, issue and pass it through. And this will go to somebody else who can actually work on this. >>And we'll show that to you a little bit later, but back to cliff, cliff says, okay, I'd like to, I'd like to work with this dataset. So he adds it to his data basket. And just like if he's shopping online, cliff wants that kind of ability to just say, I want to just click once and be done with it. Now it is data and there's some sensitivity about it. And again, there's an owner of this data who you need to get permission from. So cliff is going to provide information to the owner to say, here's why I need this data. And how long do I need this data for starting on a certain date and ending on a certain date and ultimately, what purpose am I going to have with this data? Now, there are other things that cliff can choose to run. This one is how do you want this day to deliver to you? >>Now, you'll see down below, there are three options. One is borrow the other's lease and others by what does that mean? Well, borrow is this idea of, I don't want to have the data that's currently in this CRM, uh, cloud database moved somewhere. I don't want it to be persistent anywhere else. I just want to borrow it very short term to use in my Tablo report and then poof be gone. Cause I don't want to create any problems in my organization. Now you also see lease. Lease is a situation where you actually do need to take possession of the data, but only for a time box period of time, you don't need it for an indefinite amount of time. And ultimately buy is your ability to take possession of the data and have it in perpetuity. So we're going to go forward with our bar use case and cliff is going to submit this and all the fun starts there. >>So cliff has actually submitted the order and the owner, Joanna is actually going to receive the request for the order. Joanna, uh, opens up her task, UCS there's work to perform. It says, oh, okay, here's this there's work for me to perform. Now, Joanna has the ability to automate this using incorporated workflow that we have in Colibra. But for this situation, she's going to manually review that. Cliff wants to borrow a specific data set for a certain period of time. And he actually wants to be using in a Tablo context. So she reviews. It makes an approval and submits it this in turn, flips it back to cliff who says, okay, what obligations did I just take on in order to work for this data? And he reviews each of these data sharing agreements that you, as an organization would set up and say, what am I, uh, what are my restrictions for using this data site? >>As cliff accepts his notices, he now has triggered the process of what we would call fulfillment or a service broker. And in this situation we're doing a virtualization, uh, access, uh, for the borrow use case. Cliff suggests Tablo is his preferred BI and reporting tool. And you can see the various options that are available from power BI Looker size on ThoughtSpot. There are others that can be added over time. And from there, cliff now will be alerted the minute this data is available to them. So now we're running out and doing a distributed query to get the information and you see it returns back for raw view. Now what's really interesting is you'll see, the customer phone has a bunch of X's in it. If you remember that's PII. So it's actually being massed. So cliff can't actually see the raw data. Now cliff also wants to look at it in a Tablo report and can see the visualization layer, but you also see an incorporation of something we call Collibra on the go. >>Not only do we bring the data to the report, but then we tell you the reader, how to interpret the report. It could be that there's someone else who wants to use the very same report that cliff helped create, but they don't understand exactly all the things that cliff went through. So now they have the ability to get a full interpretation of what was this data that was used, where did it come from? And how do I actually interpret some of the fields that I see on this report? Really a clever combination of bringing the data to you and showing you how to use it. Cliff can also see this as a registered asset within a Colibra. So the next shopper comes through might actually, instead of shopping for the dataset might actually shop for the report itself. And the report is connected with the data set he used. >>So now they have a full bill of materials to run a customer Shern report and schedule it anytime they want. So now we've turned cliff actually into a creator of data assets, and this is where intelligent, it gets more intelligence and that's really what we call data intelligence. So let's go back through that magic trick that we just did with cliff. So cliff went into the software, not knowing if the source of data that he was looking for for customer product sales was even available to him. He went in very quickly and searched and found his dataset, use facts and facets to filter down to exactly what was available. Compare to contrast the options that were there actually made an observation that there actually wasn't enough data quality around a certain thing was important to him, created an idea, or basically a suggestion for somebody to follow up on was able to put that into his shopping basket checkout and have it delivered to his front door. >>I mean, that's a bit of a magic trick, right? So, uh, cliff was successful in finding data that he wanted and having it, deliver it to him. And then in his preferred model, he was able to look at it into Tableau. All right. So let's talk about how we're going to make this vision a reality. So our first section here is about performance and scale, but it's also about codeless database registration. How did we get all that stuff into the data catalog and available for, uh, cliff to find? So allow us to introduce you to what we call the asset life cycle and some of the largest organizations in the world. They might have upwards of a billion data assets. These are columns and tables, reports, API, APIs, algorithms, et cetera. These are very high volume and quite technical and far more information than a business user like cliff might want to be engaged with those very same really large organizations may have upwards of say, 20 to 25 million that are critical data sources and data assets, things that they do need to highly curate and make available. >>But through that as a bit of a distillation, a lifecycle of different things you might want to do along that. And so we're going to share with you how you can actually automatically register these sources, deal with these very large volumes at speed and at scale, and actually make it available with just a level of information you need to govern and protect, but also make it available for opportunistic use cases, such as the one we presented with cliff. So as you recall, when cliff was actually trying to look for his dataset, he identified that the is churned, uh, data at your was of low quality. So he passed this over to Eliza, who's a data steward and she actually receives this work queue in a collaborative fashion. And she has to review, what is the request? If you recall, this was the request to improve the data quality for his churn. >>Now she needs to familiarize herself with what cliff was observing when he was doing his shopping experience. So she digs in and wants to look at the quality that he was observing and sure enough, as she goes down and it looks at his churn, she sees that it was a low 65% and now understands exactly what cliff was referring to. She says, aha, okay. I need to get help. I need to decide whether I have a data quality project to fix the data, or should I see if there's another data set in the organization that has better, uh, data for this. And so she creates a queue that can go over to one of her colleagues who really focuses on data quality. She submits this request and it goes over to, uh, her colleague, John who's really familiar with data quality. So John actually receives the request from Eliza and you'll see a task showing up in his queue. >>He opens up the request and finds out that Eliza's asking if there's another source out there that actually has good is churned, uh, data available. Now he actually knows quite a bit about the quality of information sturdiness. So he goes into the data quality console and does a quick look for a dataset that he's familiar with called customer product sales. He quickly scrolls down and finds out the one that's actually been published. That's the one he was looking for and he opens it up to find out more information. What data sets are, what columns are actually in there. And he goes down to find his churned is in fact, one of the attributes in there. It actually does have active rules that are associated with it to manage the quality. And so he says, well, let's look in more detail and find out what is the quality of this dataset? >>Oh, it's 86. This is a dramatic improvement over what we've seen before. So we can see again, it's trended quite nicely over time each day, it hasn't actually degraded in performance. So we actually responds back to realize and say, this data set, uh, is actually the data set that you want to bring in. It really will improve. And you'll see that he refers to the refined database within the CRM cloud solution. Once he actually submits this, it goes back to Eliza and she's able to continue her work. Now when Eliza actually brings this back open, she's able to very quickly go into the database registration process for her. She very quickly goes into the CRM cloud, selects the community, to which she wants to register this, uh, data set into the schemas community. And the CRM cloud is the system that she wants to load it in. >>And the refined is the database that John told her that she should bring in. After a quick description, she's able to click register. And this triggers that automatic codeless process of going out to the dataset and bringing back its metadata. Now metadata is great, but it's not the end all be all. There's a lot of other values that she really cares about as she's actually registering this dataset and synchronizing the metadata she's also then asked, would you like to bring in quality information? And so she'll go out and say, yes, of course, I want to enable the quality information from CRM refined. I also want to bring back lineage information to associate with this metadata. And I also want to select profiling and classification information. Now when she actually selects it, she can also say, how often do you want to synchronize this? This is a daily, weekly, monthly kind of update. >>That's part of the change data capture process. Again, all automated without the require of actually writing code. So she's actually run this process. Now, after this loads in, she can then open up this new registered, uh, dataset and actually look and see if it actually has achieved the problem that cliff set her out on, which was improved data quality. So looking into the data quality for the is churn capability shows her that she has fantastic quality. It's at a hundred, it's exactly what she was looking for. So she can with confidence actually, uh, suggest that it's done, but she did notice something and something that she wants to tell John, which is there's a couple of data quality checks that seem to be missing from this dataset. So again, in a collaborative fashion, she can pass that information, uh, for validity and completeness to say, you know what, check for NOLs and MPS and send that back. >>So she submits this onto John to work on. And John now has a work queue in his task force, but remember she's been working in this task forklift and because she actually has actually added a much better source for his churn information, she's going to update that test that was sent to her to notify cliff that the work has actually been done and that she actually has a really good data set in there. In fact, if you recall, it was 100% in terms of its data quality. So this will really make life a lot easier for cliff. Once he receives that data and processes, the churn report analysis next time. So let's talk about these audacious performance goals that we have in mind. Now today, we actually have really strong performance and amazing usability. Our customers continue to tell us how great our usability is, but they keep asking for more well, we've decided to present to you. >>Something you can start to bank on. This is the performance you can expect from us on the highly curated assets that are available for the business users, as well as the technical and lineage assets that are more available for the developer uses and for things that are more warehoused based, you'll see in Q1, uh, our Q2 of this year, we're making available 5 million curated assets. Now you might be out there saying, Hey, I'm already using the software and I've got over 20 million already. That's fair. We do. We have customers that are actually well over 20 million in terms of assets they're managing, but we wanted to present this to you with zero conditions, no limitations we wouldn't talk about, well, it depends, et cetera. This is without any conditions. That's what we can offer you without fail. And yes, it can go higher and higher. We're also talking about the speed with which you can ingest the data right now, we're ingesting somewhere around 50,000 to a hundred thousand records per and of course, yes, you've probably seen it go quite a bit faster, but we are assuring you that that's the case, but what's really impressive is right now, we can also, uh, help you manage 250 million technical assets and we can load it at a speed of 25 million for our, and you can see how over the next 18 months about every two quarters, we show you dramatic improvements, more than doubling of these. >>For most of them leading up to the end of 2022, we're actually handling over a billion technical lineage assets and we're loading at a hundred million per hour. That sets the mark for the industry. Earlier this year, we announced a recent acquisition Al DQ. LDQ brought to us machine learning based data quality. We're now able to introduce to you Collibra data quality, the first integrated approach to Al DQ and Culebra. We've got a demo to follow. I'm really excited to share it with you. Let's get started. So Eliza submitted a task for John to work on, remember to add checks for no and for empty. So John picks up this task very quickly and looks and sees what's what's the request. And from there says, ah, yes, we do have a quality check issue when we look at these churns. So he jumps over to the data quality console and says, I need to create a new data quality test. >>So cliff is able to go in, uh, to the solution and, uh, set up quick rules, automated rules. Uh, he could inherit rules from other things, but it starts with first identifying what is the data source that he needs to connect to, to perform this. And so he chooses the CRM refined data set that was most recently, uh, registered by Lysa. You'll see the same score of 86 was the quality score for the dataset. And you'll also see, there are four rules that are associated underneath this. Now there are various checks that, uh, that John can establish on this, but remember, this is a fairly easy request that he receives from Eliza. So he's going to go in and choose the actual field, uh, is churned. Uh, and from there identify quick rules of, uh, an empty check and that quickly sets up the rules for him. >>And also the null check equally fast. This one's established and analyzes all the data in there. And this sets up the baseline of data quality, uh, for this. Now this data, once it's captured then is periodically brought back to the catalog. So it's available to not only Eliza, but also to cliff next time he, uh, where to shop in the environment. As we look through the rules that were created through that very simple user experience, you can see the one for is empty and is no that we're set up. Now, these are various, uh, styles that can be set up either manually, or you can set them up through machine learning again, or you can inherit them. But the key is to track these, uh, rule creation in the metrics that are generated from these rules so that it can be brought back to the catalog and then used in meaningful context, by someone who's shopping and the confidence that this has neither empty nor no fields, at least most of them don't well now give a confidence as you go forward. >>And as you can see, those checks have now been entered in and you can see that it's a hundred percent quality score for the Knoll check. So with confidence now, John can actually respond back to Eliza and say, I've actually inserted them they're up and running. And, uh, you're in good status. So that was pretty amazing integration, right? And four months after our acquisition, we've already brought that level of integration between, uh, Colibra, uh, data intelligence, cloud, and data quality. Now it doesn't stop there. We have really impressive and high site set early next year. We're getting introduced a fully immersive experience where customers can work within Culebra and actually bring the data quality information all the way in as well as start to manipulate the rules and generate the machine learning rules. On top of it, all of that will be a deeply immersive experience. >>We also have something really clever coming, which we call continuous data profiling, where we bring the power of data quality all the way into the database. So it's continuously running and always making that data available for you. Now, I'd also like to share with you one of the reasons why we are the most universally available software solutions in data intelligence. We've already announced that we're available on AWS and Google cloud prior, but today we can announce to you in Q3, we're going to be, um, available on Microsoft Azure as well. Now it's not just these three cloud providers that were available on we've also become available on each of their marketplaces. So if you are buying our software, you can actually go out and achieve that same purchase from their marketplace and achieve your financial objectives as well. We're very excited about this. These are very important partners for, uh, for our, for us. >>Now, I'd also like to introduce you our system integrators, without them. There's no way we could actually achieve our objectives of growing so rapidly and dealing with the demand that you customers have had Accenture, Deloitte emphasis, and even others have been instrumental in making sure that we can serve your needs when you need them. Uh, and so it's been a big part of our growth and will be a continued part of our growth as well. And finally, I'd like to actually introduce you to our product showcases where we can go into absolute detail on many of the topics I talked about today, such as data governance with Arco or data privacy with Sergio or data quality with Brian and finally catalog with Peter. Again, I'd like to thank you all for joining us. Uh, and we really look forward to hearing your feedback. Thank you..

(electronic music) >> Announcer: Live from Austin, Texas, it's theCUBE, covering DockercCon 2017. Brought to you by Docker and support from Asseco System Partners. >> Welcome back to theCUBE, I'm Stu Mittleman, with my co-host, Jim Kobielus. Happy to have on the program, I'm shocked to say a first time guest. Someone that I've known in the community here for many years, but Michael Ducy, who is Director of Product Marketing at Chef Software. Not a chef. Maybe you might-- >> Not a chef, although I do cook at home (laughing). >> Maybe in Chef. Not a puppeteer. >> Not a puppeteer. >> But you work for Chef Software. So thank you so much for joining us. >> Yes, thanks for having me. >> Alright, so Michael, for the audience that doesn't know you... I think a lot of people here in the community would know you. I've known you through Twitter for many years. What's your role at Chef? What do you work on? What's your passion? >> Sure, so right now I do product marketing for our open source projects. So Chef Software actually has a commercial product, and then we also have three open source projects that we maintain. The first was the original one that we're named after, which is Chef, which is open source automation or configuration management. The second one being Inspect, which is all about how do you basically write compliance rules as code. And then third one, as you can see from my shirt, is called Habitat. So Habitat is a new way of thinking about how do you package up automation for your application. And then how can you easily export that application and the automation into something like a container. I've had various roles at Chef though over the four years that I've worked for them. My passion's always kind of been open source communities, an involvement in open source communities and helping grow those communities. >> Yeah, and people send you lots of stuff about goats. >> People send me lots of stuff about goats (laughing). There was a joke that was made at a conference about waking up next to a goat. This was a conference in Amsterdam, which is I'm sure I wouldn't be the first one that woke up next to a goat in Amsterdam (laughing). But since then, the whole goat thing kind of took off after that. >> Yeah, so, Chef, you understand many things about Docker. So one of the things, we come in and we talk about there's Docker, the company, there's Docker, the community. A lot of what was talked about in the keynote today was about open source. >> Umm-hmm. >> So how's Docker doing? What interested you in the keynote? How do you as an individual in Chef see what's going on in the Docker ecosystem? And what do you think? >> Yeah. >> Yeah. >> So we've been put in a little bit of an interesting position as Chef, the company. And not only has Chef, the company, been put in this position, but all of our competitors have as well. So there's been a movement as Docker and containers got more popular that the idea that configuration management is no longer needed. And from a inside the container perspective, configuration management really isn't needed. But what you do end up realizing is that there's this whole idea of what you need to actually run a container in production effectively, that still needs to go into that container. And we kind of call it The Learning Cliff of Containers. And I tweeted out an image about... that why co-worker draw on a whiteboard. That shows in development you just have Docker and it's really easy, but then when you move it to production there's this whole other stack of concerns. And Docker or your container runtime is just one of them. And so, we've been focusing more on kind of shifting into those ideas of how do you actually run containers effectively in production. What we saw in the keynote today is more of an emphasis on things like security, right. That's definitely been an area that we're interested in, especially from a compliance perspective, and doing work around having our open source projects, being able to scan containers for compliance. >> Yeah, it's funny before the keynote they have this fun little thing. They have this 8-bit video game playing. >> Right. >> And it was like they were collecting coins and they were leveling up, but they kept hitting lots of bombs (laughing) and things were exploding all the time. And everybody was joking online. It was like, Oh, it's like putting Docker in production. I will level up (laughing) and I will get past everything, but, Boy, I'm going to have lots of bombs going off and things-- >> Sure. >> And things that I'll have to deal with, and there were lots of fun little comments that they threw out there. It's like, Checking documentation. Oh, documentation says you don't have documentation. (laughing) So just fun stuff like that. But it's challenging. Solomon says, We want this put in deployment, but as we know it's not quite there yet. There's lots of things, that's where you guys fit in. >> Umm-hmm. >> A lot of the ecosystem helps to solidify that about you here. >> Michael, what are those concerns that you allude to? There's security, and what other concerns are there for containers in production that need to be represented in the configuration management portfolio or profile you're describing? >> Sure, so there's the security aspects of it is focused on what vulnerabilities are in your container. >> Yeah. >> And there's been some interesting studies recently that showed 24% of the official images are shipping with some sort of a vulnerability. Some of that you have to accept, and then also realize can you do risk mitigation around that vulnerability. There's concerns about how the application is actually configured when you ship it as well. So am I doing things like storing secrets in config files. Am I disabling versions of ISOCELL that's no longer a best practice anymore because it's actually broken. And then there's other aspects around how do you things like service discovery, how do you do credentials or secrets. And how do you get them into the container securely. There's networking aspects. There's last malconfiguration of the application, so-- >> Right. >> If you take a container from one environment to another environment and kind of work it through a lifecycle. There are things at runtime that you have to change in its configuration to make it run in that particular environment. >> Right. >> So it's all of those little knobs that you still have to turn. And that's why-- >> The entire DevOps lifecycle essentially there's all those little knobs and... >> There's all these little knobs and this has always been a little bit of a frustration for me, in that PaaS sounds great, platform as a service sounds great. And this idea that you can just take this blob and go run it. But What people don't realize is there still are tons of knobs that you have to turn, and there are tons of concerns that you have to worry about as an operations person or as a DevOps person or as a developer when you actually are taking that code into production. >> Right. >> Michael, we've seen the cloud providers and some of the other open source providers kind of chipping away. Red Hat bought Ansible, every time I go to Amazon re:Invent or Google, it seems like they're trying to build more things up the stack and into their platforms. >> Umm-hmm. >> So what is Chef's position here? How do you guys play across all these environments and kind of maintain and grow what you're doing? >> Yeah, so we've started to take a little bit more of a different focus and... Well, not a different focus... A different focus for us. Traditionally, we focus on infrastructure and operations people and then as we moved up the stack and DevOps became more popular. We definitely focused on that because that's kind of our bread and butter. But what we started to do with Habitat is focus more on building a developer experience. So how can a developer take their code-- >> Yeah. >> Easily wrap automation around it, and then ship it out into production. And this is the new world for us, as coming from the operations side of things. And really starting to think about what does the developer tooling look like and the developer experience look like. We're taking source code, building that source code, and then deploying that source code to production. >> Yeah, and it's interesting, it sounds... We talk about Docker. They very much started out in the developer world, and then they're kind of moving to kind of the Op side more. >> Umm-hmm. >> And to the enterprise side more. You're almost going-- >> Michael: And we're kind of-- >> A little bit in reverse, huh. >> Yeah, going a little bit in reverse, yeah. >> Yeah, it's interesting because usually it's like, Okay, I start with developers, get them excited and then figure out to monetize. So, yeah, what are you seeing in your customer base? >> Sure. >> Who do you sell to in that aspect? Yeah, I'm just curiosity at some of the buyers. >> Well, so, traditionally, a tool like Chef or, even some of our competitors would be bought by what's called the Shared Services Team, right. And that Shared Services Team is going to take that and try and work economies of scale, right. And try and deploy that across all of the different BMs or machines that they have to manage, right. And we've seen this shift as we moved more up the stack and as the industry's shifted more up the stack. Of what the Shared Services Team actually needs to transform themselves into is more of a developer services team. So how can I offer the services that a developer can get via an API, to quickly deploy the application services that they need. And when I say application services, I'm thinking about all of the things that you need to actually go and persist the data. The business logic side of things are very easy to do in containers or PaaS. But when you're actually having to go and persist data in something like Red-S are Mongo or MySQL, that's a whole other area of concern that you have to worry about. So what we've actually had started to do is the core team that actually works on Habitat has a very, very big background in distributive systems. So what we've started to do is bake a lot of that foundational ideas about how you effectively run large-scale distributive systems into Habitat, which makes it very easy to then go and take that developer, take their source code, and deploy it using Habitat, using this knowledge that we have from distributive systems. So we actually see it as a benefit that we come from this infrastructure background because we have experience of actually running things in production, right. >> Umm-hmm, what do you see as some of the challenges that we still need to face in this kind of container ecosystem? I know one of the questions I have coming in is you talked about stateful applications. We know storage still needs some time to mature. Networking seems to be a little bit further along in what they're doing. >> Umm-hmm. >> What's your take as to what's doing well? What still needs some more work? >> Yeah, storage is one of those areas that... And persisting data is one of those areas that we're not able to get around, right. And if you look at some people's recommendations, so Pivotal, for example, recommends running persistent services on BMs, right. If you look at the Google approach or the Cuber-netee's approach, they actually recommend that you use a cloud provider services to go and run those data services for you, until you think you're good enough to actually go and run it like Google. (laughing) And they're also hedging on the fact that you'll probably never be good enough to run it like Google. >> Yeah, yeah. >> So, kind of building that expertise of running those distributive systems in an effective way is kind of the area in running those persistent data services in a highly scalable way is kind of the big challenge that operations still hasn't figured out. And developers also need work to... Need help to help figure that out as well. >> Yeah, the big theme this morning was really about scalability. When you talked to customers, what does scale mean to them? What are the limitations they're having? I loved when you talked about what you're doing with Habitat. Helping customers, so that they don't have to have the expertise to build distributive systems because that's the software challenge of our time-- >> Yeah. >> Is moving to that. What we talk at Wicky-bon, it's moving from the old enterprise where it was like kind of baked in the hardware to a distributive, where the software model, anything had failed, there's no single point of failure, I can scale. >> Yeah. >> What do you think? >> Well, to kind of paraphrase our CTO, Adam Jacob, he always likes to say ignore scaling problems because you don't have a scaling problem. (laughing) And you don't have a scaling problem until you have a scaling problem, right. So if you kind of look at where your time's most effectively spent, your time is more effectively spent at actually building an application that people want to use, and worry about the scaling problem when the scaling problem comes up, right. And the other thing is that you might never hit that scaling problem, so everyone wants to be the next Uber, everyone wants to be the next Netflix, and so forth. And so, if you go in as a startup or, even a startup inside of a large enterprise trying to do a new application. If you start by trying to solve the scaling problem out the door, then what you end up losing is a lot of development cycles that you could actually be spending on building something that people actually want to use. And then worrying about the scaling problem when you hit the scaling problem. >> So, Mike, last question I have for you. A month from now, you're going to be back in Austin. >> A month from now, I'm going to be back in Austin. >> So tell us about ChefConf. >> Yes. >> What can people expect? Give us a compare and contrast to kind of the communities, the type of people that attend. I expect we'll see more shorts because it's going to be a little bit warmer and more humid here in Austin (laughing). >> Yes, so we're back at Austin for the second ChefConf in Austin. We were here also last year. We were in Austin in July last year. >> Ooooh. >> Which was not a fun experience (laughing). The air conditioning was very nice. The pool was also very nice. (laughing) But what you can expect is more practical advice to how to actually run these things in production. We have a lot of talks about Habitat. I think we're going to have a lot... Nine talks on Habitat. We have a lot of talks from the Chef community about running actual systems in production in a lot of real world experience, which is something that we always try and hover into our conferences. We also have a day that's going to be focused on our open source community as well, so where our open source and contributors can get together to talk about problems that they're trying to solve in our open source communities as well. And then on the last day, of course, as every conference does we're going to have a hack day, where you can contribute to open source, our open source, or we can help you get started solving a problem that you have, but there'll be a lot of people there that can answer questions for you about the problems that you're trying to solve in running distributive systems. >> Alright, well, Michael Ducy, happy to welcoming you into the ranks of theCUBE alumni, finally. >> Yes, finally, thank you very much. >> And thank you for sharing all the updates with us. And thank you for watching theCUBE. (electronic music) >> I remember...