(upbeat music) >> Welcome back to Vegas. It's theCUBE live at AWS re:Invent 2022. We're hearing up to 50,000 people here. It feels like if the energy at this show is palpable. I love that. Lisa Martin here with Dave Vellante. Dave, we had the keynote this morning that Adam Selipsky delivered lots of momentum in his first year. One of the things that you said that you were looking in your breaking analysis that was released a few days ago, four trends and one of them, he said under Selipsky's rule in the 2020s, there's going to be a rush of data that will dwarf anything we have ever seen. >> Yeah, it was at least a quarter, maybe a third of his keynote this morning was all about data and the theme is simplifying data and doing better data integration, integrating across different data platforms. And we're excited to talk about that. Always want to simplify data. It's like the rush of data is so fast. It's hard for us to keep up. >> It is hard to keep that up. We're going to be talking with an alumni next about how his company is helping organizations like Cisco Meraki keep up with that data explosion. Please welcome back to the program, Matthew Scullion, the CEO of Matillion and how Hoshang Chenoy joins us, data scientist at Cisco Meraki. Guys, great to have you on the program. >> Thank you. >> Thank you for having us. >> So Matthew, we last saw you just a few months ago in Vegas at Snowflake Summits. >> Matthew: We only meet in Vegas. >> I guess we do, that's okay. Talk to us about some of the things, I know that Matillion is a data transformation solution that was originally introduced for AWS for Redshift. But talk to us about Matillion. What's gone on since we've seen you last? >> Well, I mean it's not that long ago but actually quite a lot. And it's all to do with exactly what you guys were just talking about there. This almost hard to comprehend way the world is changing with the amounts of data that we now can and need to put to work. And our worldview is there's no shortage of data but the choke points certainly one of the choke points. Maybe the choke point is our ability to make that data useful, to make it business ready. And we always talk about the end use cases. We talk about the dashboard or the AI model or the data science algorithm. But until before we can do any of that fun stuff, we have to refine raw data into business ready, usable data. And that's what Matillion is all about. And so since we last met, we've made a couple of really important announcements and possibly at the top of the list is what we call the data productivity cloud. And it's really squarely addressed this problem. It's the results of many years of work, really the apex of many years of the outsize engineering investment, Matillion loves to make. And the Data Productivity Cloud is all about helping organizations like Cisco Meraki and hundreds of others enterprise organizations around the world, get their data business ready, faster. >> Hoshang talk to us a little bit about what's going on at Cisco Meraki, how you're leveraging Matillion from a productivity standpoint. >> I've really been a Matillion fan for a while, actually even before Cisco Meraki at my previous company, LiveRamp. And you know, we brought Matillion to LiveRamp because you know, to Matthew's point, there is a stage in every data growth as I want to call it, where you have different companies at different stages. But to get data, data ready, you really need a platform like Matillion because it makes it really easy. So you have to understand Matillion, I think it's designed for someone that uses a lot of code but also someone that uses no code because the UI is so good. Someone like a marketer who doesn't really understand what's going on with that data but wants to be a data driven marketer when they look at the UI they immediately get it. They're just like, oh, I get what's happening with my data. And so that's the brilliance of Matillion and to get data to that data ready part, Matillion does a really, really good job because what we've been able to do is blend so many different data sources. So there is an abundance of data. Data is siloed though. And the connectivity between different data is getting harder and harder. And so here comes the Matillion with it's really simple solution, easy to use platform, powerful and we get to use all of that. So to really change the way we've thought about our analytics, the way we've progressed our division, yeah. >> You're always asking about superpowers and that is a superpower of Matillion 'cause you know, low-code, no-code sounds great but it only gets you a quarter of the way there, maybe 50% of the way there. You're kind of an "and" not an "or." >> That's a hundred percent right. And so I mentioned the Data Productivity Cloud earlier which is the name of this platform of technology we provide. That's all to do with making data business ready. And so I think one of the things we've seen in this industry over the past few years is a kind of extreme decomposition in terms of vendors of making data business ready. You've got vendors that just do loading, you've got vendors that just do a bit of data transformation, you've got vendors that do data ops and orchestration, you've got vendors that do reverse ETL. And so with the data productivity platform, you've got all of that. And particularly in this kind of, macroeconomic heavy weather that we're now starting to face, I think companies are looking for that. It's like, I don't want to buy five things, five sets of skills, five expensive licenses. I want one platform that can do it. But to your point David, it's the and not the or. We talk about the Data Productivity Cloud, the DPC, as being everyone ready. And what we mean by that is if you are the tech savvy marketer who wants to get a particular insight and you understand what a Rowan economy is, but you're not necessarily a hardcore super geeky data engineer then you can visual low-code, no-code, your data to a point where it's business ready. You can do that really quick. It's easy to understand, it's faster to ramp people onto those projects cause it like explains itself, faster to hand it over cause it's self-documenting. But, they'll always be individuals, teams, "and", "or" use cases that want to high-code as well. Maybe you want to code in SQL or Python, increasingly of course in DBT and you can do that on top of the Data Productivity Cloud as well. So you're not having to make a choice, but is that right? >> So one of the things that Matillion really delivers is speed to insight. I've always said that, you know, when you want to be business ready you want to make fast decisions, you want to act on data quickly, Matillion allows you to, this feed to insight is just unbelievably fast because you blend all of these different data sources, you can find the deficiencies in your process, you fix that and you can quickly turn things around and I don't think there's any other platform that I've ever used that has that ability. So the speed to insight is so tremendous with Matillion. >> The thing I always assume going on in our customers teams, like you run Hoshang is that the visual metaphor, be it around the orchestration and data ops jobs, be it around the transformation. I hope it makes it easier for teams not only to build it in the first place, but to live with it, right? To hand it over to other people and all that good stuff. Is that true? >> Let me highlight that a little bit more and better for you. So, say for example, if you don't have a platform like Matillion, you don't really have a central repository. >> Yeah. >> Where all of your codes meet, you could have a get repository, you could do all of those things. But, for example, for definitions, business definitions, any of those kind of things, you don't want it to live in just a spreadsheet. You want it to have a central platform where everybody can go in, there's detailed notes, copious notes that you can make on Matillion and people know exactly which flow to go to and be part of, and so I kind of think that that's really, really important because that's really helped us in a big, big way. 'Cause when I first got there, you know, you were pulling code from different scripts and things and you were trying to piece everything together. But when you have a platform like Matillion and you actually see it seamlessly across, it's just so phenomenal. >> So, I want to pick up on something Matthew said about, consolidating platforms and vendors because we have some data from PTR, one of our survey partners and they went out, every quarter they do surveys and they asked the customers that were going to decrease their spending in the quarter, "How are you going to do it?" And number one, by far, like, over a third said, "We're going to consolidate redundant vendors." Way ahead of cloud, we going to optimize cloud resource that was next at like 15%. So, confirms what you were saying and you're hearing that a lot. Will you wait? And I think we never get rid of stuff, we talk about it all the time. We call it GRS, get rid of stuff. Were you able to consolidate or at least minimize your expense around? >> Hoshang: Yeah, absolutely. >> What we were able to do is identify different parts of our tech stack that were just either deficient or duplicate, you know, so they're just like, we don't want any duplicate efforts, we just want to be able to have like, a single platform that does things, does things well and Matillion helped us identify all of those different and how do we choose the right tech stack. It's also about like Matillion is so easy to integrate with any tech stack, you know, it's just they have a generic API tool that you can log into anything besides all of the components that are already there. So it's a great platform to help you do that. >> And the three things we always say about the Data Productivity Cloud, everyone ready, we spoke about this is whether low-code, no-code, quasi-technical, quasi-business person using it, through to a high-end data engineer. You're going to feel at home on the DPC. The second one, which Hoshang was just alluding to there is stack ready, right? So it is built for AWS, built for Snowflake, built for Redshift, pure tight integration, push down ELT better than you could write yourself by hand. And then the final one is future ready, which is this idea that you can start now super easy. And we buy software quickly nowadays, right? We spin it up, we try it out and before we know it, the whole organization is using it. And so the future ready talks about that continuum of being able to launch in five minutes, learn it in five hours, deliver your first project in five days and yet still be happy that it's an enterprise scalable platform, five years down track including integrating with all the different things. So Matillion's job holding up the end of the bargain that Hoshang was just talking about there is to ensure we keep putting the features integrations and support into the Data Productivity Cloud to make sure that Hoshang's team can continue to live inside it and do all the things they need to do. >> Hoshang, you talked about the speed to insight being tremendously fast, but if I'm looking at Cisco Meraki from a high level business outcome perspective, what are some of those outcomes that a Matillion is helping Cisco Meraki to achieve. >> So I can just talk in general, not giving you like any specific numbers or anything, but for example, we were trying to understand how well our small and medium business campaigns were doing and we had to actually pull in data from multiple different sources. So not just, our instances of Marketo and Salesforce, we had to look at our internal databases. So Matillion helped us blend all of that together. Once I had all of that data blended, it was then ready to be analyzed. And once we had that analysis done, we were able to confirm that our SMB campaigns were doing well but these the things that we need to do to improve them. When we did that and all of that happened so quickly because they were like, well you need to get data from here, you need to get data from there. And we're like, great, we'll just plug, plug, plug. We put it all together, build transformations and you know we produced this insight and then we were able to reform, refine, and keep getting better and better at it. And you know, we had a 40X return on SMB campaigns. It's unbelievable. >> And there's the revenue tie in right there. >> Hoshang: Yeah. >> Matthew, I know you've been super busy, tons of meetings, you didn't get to see the whole keynote, but one of the themes of Adam Selipsky's keynote was, you know, the three letter word of ETL, they laid out a vision of zero ETL and then they announced zero ETL for Aurora and Redshift. And you think about ETL, I remember the days they said, "Okay, we're going to do ELT." Which is like, raising the debt ceiling, we're just going to kick the can down the road. So, what do you think about that vision? You know, how does it relate to what you guys are doing? >> So there was a, I don't know if this only works in the UK or it works globally. It was a good line many years ago. Rumors of my death are premature or so I think it was an obituary had gone out in the times by accident and that's how the guy responded to it. Something like that. It's a little bit like that. The announcement earlier within the AWS space of zero ETL between platforms like Aurora and Redshift and perhaps more over time is really about data movement, right? So it's about do I need to do a load of high cost in terms of coding and compute, movement of data between one platform, another. At Matillion, we've always seen data movement as an enabling technology, which gets you to the value add of transformation. My favorite metaphor to bring this to life is one of iron. So the world's made of iron, right? The world is literally made of iron ore but iron ore isn't useful until you turn it to steel. Loading data is digging out iron ore from the ground and moving it to the refinery. Transformation of data is turning iron ore into steel and what the announcements you saw earlier from AWS are more about the quarry to the factory bit than they are about the iron ore to the steel bit. And so, I think it's great that platforms are making it easier to move data between them, but it doesn't change the need for Hoshang's business professionals to refine that data into something useful to drive their marketing campaigns. >> Exactly, it's quarry to the factory and a very Snowflake like in a way, right? You make it easy to get in. >> It's like, don't get me wrong, I'm great to see investment going into the Redshift business and the AWS data analytics stack. We do a lot of business there. But yes, this stuff is also there on Snowflake, already. >> I mean come on, we've seen this for years. You know, I know there's a big love fest between Snowflake and AWS 'cause they're selling so much business in the field. But look that we saw it separating computing from storage, then AWS does it and now, you know, why not? It's good sense. That's what customers want. The customer obsessed data sharing is another thing. >> And if you take data sharing as an example from our friends at Snowflake, when that was announced a few people possibly, yourselves, said, "Oh, Matthew what do you think about this? You're in the data movement business." And I was like, "Ah, I'm not really actually, some of my competitors are in the data movement business. I have data movement as part of my platform. We don't charge directly for it. It's just part of the platform." And really what it's to do is to get the data into a place where you can do the fun stuff with it of refining into steel. And so if Snowflake or now AWS and the Redshift group are making that easier that's just faster to fun for me really. >> Yeah, sure. >> Last question, a question for both of you. If you had, you have a brand new shiny car, you got a bumper sticker that you want to put on that car to tell everyone about Matillion, everyone about Cisco Meraki, what does that bumper sticker say? >> So for Matillion, it says Matillion is the Data Productivity Cloud. We help you make your data business ready, faster. And then for a joke I'd write, "Which you are going to need in the face of this tsunami of data." So that's what mine would say. >> Love it. Hoshang, what would you say? >> I would say that Cisco makes some of the best products for IT professionals. And I don't think you can, really do the things you do in IT without any Cisco product. Really phenomenal products. And, we've gone so much beyond just the IT realm. So you know, it's been phenomenal. >> Awesome. Guys, it's been a pleasure having you back on the program. Congrats to you now Hoshang, an alumni of theCUBE. >> Thank you. >> But thank you for talking to us, Matthew, about what's going on with Matillion so much since we've seen you last. I can imagine how much worse going to go on until we see you again. But we appreciate, especially having the Cisco Meraki customer example that really articulates the value of data for everyone. We appreciate your insights and we appreciate your time. >> Thank you. >> Privilege to be here. Thanks for having us. >> Thank you. >> Pleasure. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage.

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante aws is pointing the way to a revolution in system architecture much in the same way that aws defined the cloud operating model last decade we believe it is once again leading in future systems design the secret sauce underpinning these innovations is specialized designs that break the stranglehold of inefficient and bloated centralized processing and allows aws to accommodate a diversity of workloads that span cloud data center as well as the near and far edge hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll dig into the moves that aws has been making which we believe define the future of computing we'll also project what this means for customers partners and aws many competitors now let's take a look at aws's architectural journey the is revolution it started by giving easy access as we all know to virtual machines that could be deployed and decommissioned on demand amazon at the time used a highly customized version of zen that allowed multiple vms to run on one physical machine the hypervisor functions were controlled by x86 now according to werner vogels as much as 30 of the processing was wasted meaning it was supporting hypervisor functions and managing other parts of the system including the storage and networking these overheads led to aws developing custom asics that help to accelerate workloads now in 2013 aws began shipping custom chips and partnered with amd to announce ec2 c3 instances but as the as the aws cloud started to scale they really weren't satisfied with the performance gains that they were getting and they were hitting architectural barriers that prompted aws to start a partnership with anaperta labs this was back in 2014 and they launched then ec2 c4 instances in 2015. the asic in c4 optimized offload functions for storage and networking but still relied on intel xeon as the control point aws aws shelled out a reported 350 million dollars to acquire annapurna in 2015 which is a meager sum to acquire the secret sauce of its future system design this acquisition led to a modern version of project nitro in 2017 nitro nitro offload cards were first introduced in 2013 at this time aws introduced c5 instances and replaced zen with kvm and more tightly coupled the hypervisor with the asic vogels shared last year that this milestone offloaded the remaining components including the control plane the rest of the i o and enabled nearly a hundred percent of the processing to support customer workloads it also enabled a bare metal version of the compute that spawned the partnership the famous partnership with vmware to launch vmware cloud on aws then in 2018 aws took the next step and introduced graviton its custom designed arm-based chip this broke the dependency on x86 and launched a new era of architecture which now supports a wide variety of configurations to support data intensive workloads now these moves preceded other aws innovations including new chips optimized for machine learning and training and inferencing and all kinds of ai the bottom line is aws has architected an approach that offloaded the work currently done by the central processing unit in most general purpose workloads like in the data center it has set the stage in our view for the future allowing shared memory memory disaggregation and independent resources that can be configured to support workloads from the cloud all the way to the edge and nitro is the key to this architecture and to summarize aws nitro think of it as a set of custom hardware and software that runs on an arm-based platform from annapurna aws has moved the hypervisor the network the storage virtualization to dedicated hardware that frees up the cpu to run more efficiently this in our opinion is where the entire industry is headed so let's take a look at that this chart pulls data from the etr data set and lays out key players competing for the future of cloud data center and the edge now we've superimposed nvidia up top and intel they don't show up directly in the etr survey but they clearly are platform players in the mix we covered nvidia extensively in previous breaking analysis and won't go too deep there today but the data shows net scores on the vertical axis that's a measure of spending velocity and then it shows market share in the horizontal axis which is a measure of pervasiveness within the etr data set we're not going to dwell on the relative positions here rather let's comment on the players and start with aws we've laid out aws how they got here and we believe they are setting the direction for the future of the industry and aws is really pushing migration to its arm-based platforms pat morehead at the 6-5 summit spoke to dave brown who heads ec2 at aws and he talked extensively about migrating from x86 to aws's arm-based graviton 2. and he announced a new developer challenge to accelerate that migration to arm instances graviton instances and the end game for customers is a 40 better price performance so a customer running 100 server instances can do the same work with 60 servers now there's some work involved but for the by the customers to actually get there but the payoff if they can get 40 improvement in price performance is quite large imagine this aws currently offers 400 different ec2 instances last year as we reported sorry last year as we reported earlier this year nearly 50 percent of the new ec2 instances so nearly 50 percent of the new ec2 instances shipped in 2020 were arm based and aws is working hard to accelerate this pace it's very clear now let's talk about intel i'll just say it intel is finally responding in earnest and basically it's taking a page out of arm's playbook we're going to dig into that a bit today in 2015 intel paid 16.7 billion dollars for altera a maker of fpgas now also at the 6.5 summit nevin shenoy of intel presented details of what intel is calling an ipu it's infrastructure processing unit this is a departure from intel norms where everything is controlled by a central processing unit ipu's are essentially smart knicks as our dpus so don't get caught up in all the acronym soup as we've reported it's all about offloading work and disaggregating memory and evolving socs system-on-chip and sops system on package but just let this sink in a bit a bit for a moment intel's moves this past week it seems to us anyway are designed to create a platform that is nitro like and the basis of that platform is a 16.7 billion dollar acquisition just compare that to aws's 350 million dollar tuck-in of annapurna that is incredible now chenoy said in his presentation rough quote we've already deployed ipu's using fpgas in a in very high volume at microsoft azure and we've recently announced partnerships with baidu jd cloud and vmware so let's look at vmware vmware is the other you know really big platform player in this race in 2020 vmware announced project monterrey you might recall that it's based on the aforementioned fpgas from intel so vmware is in the mix and it chose to work with intel most likely for a variety of reasons one of the obvious ones is all the software that's running on on on vmware it's been built for x86 and there's a huge install base there the other is pat was heading vmware at the time and and you know when project monterey was conceived so i'll let you connect the dots if you like regardless vmware has a nitro like offering in our view its optionality however is limited by intel but at least it's in the game and appears to be ahead of the competition in this space aws notwithstanding because aws is clearly in the lead now what about microsoft and google suffice it to say that we strongly believe that despite the comments that intel made about shipping fpgas and volume to microsoft that both microsoft and google as well as alibaba will follow aws's lead and develop an arm-based platform like nitro we think they have to in order to keep pace with aws now what about the rest of the data center pack well dell has vmware so despite the split we don't expect any real changes there dell is going to leverage whatever vmware does and do it better than anyone else cisco is interesting in that it just revamped its ucs but we don't see any evidence that it has a nitro like plans in its roadmap same with hpe now both of these companies have history and capabilities around silicon cisco designs its own chips today for carrier class use cases and and hpe as we've reported probably has some remnants of the machine hanging around but both companies are very likely in our view to follow vmware's lead and go with an intel based design what about ibm well we really don't know we think the best thing ibm could do would be to move the ibm cloud of course to an arm-based nitro-like platform we think even the mainframe should move to arm as well i mean it's just too expensive to build a specialized mainframe cpu these days now oracle they're interesting if we were running oracle we would build an arm-based nitro-like database cloud where oracle the database runs cheaper faster and consumes less energy than any other platform that would would dare to run oracle and we'd go one step further and we would optimize for competitive databases in the oracle cloud so we would make oci run the table on all databases and be essentially the database cloud but you know back to sort of fpgas we're not overly excited about about the market amd is acquiring xi links for 35 billion dollars so i guess that's something to get excited about i guess but at least amd is using its inflated stock price to do the deal but we honestly we think that the arm ecosystem will will obliterate the fpga market by making it simpler and faster to move to soc with far better performance flexibility integration and mobility so again we're not too sanguine about intel's acquisition of altera and the moves that amd is making in in the long term now let's take a deeper look at intel's vision of the data center of the future here's a chart that intel showed depicting its vision of the future of the data center what you see is the ipu's which are intelligent nixed and they're embedded in the four blocks shown and they're communicating across a fabric now you have general purpose compute in the upper left and machine intelligent on the bottom left machine intelligence apps and up in the top right you see storage services and then the bottom right variation of alternative processors and this is intel's view of how to share resources and go from a world where everything is controlled by a central processing unit to a more independent set of resources that can work in parallel now gelsinger has talked about all the cool tech that this will allow intel to incorporate including pci and gen 5 and cxl memory interfaces and or cxl memory which are interfaces that enable memory sharing and disaggregation and 5g and 6g connectivity and so forth so that's intel's view of the future of the data center let's look at arm's vision of the future and compare them now there are definite similarities as you can see especially on the right hand side of this chart you've got the blocks of different process processor types these of course are programmable and you notice the high bandwidth memory the hbm3 plus the ddrs on the two sides kind of bookending the blocks that's shared across the entire system and it's connected by pcie gen 5 cxl or ccix multi-die socket so you know you may be looking to say okay two sets of block diagrams big deal well while there are similarities around disaggregation and i guess implied shared memory in the intel diagram and of course the use of advanced standards there are also some notable differences in particular arm is really already at the soc level whereas intel is talking about fpgas neoverse arms architecture is shipping in test mode and we'll have end market product by year end 2022 intel is talking about maybe 2024 we think that's aspirational or 2025 at best arm's road map is much more clear now intel said it will release more details in october so we'll pay attention then maybe we'll recalibrate at that point but it's clear to us that arm is way further along now the other major difference is volume intel is coming at this from a high data center perspective and you know presumably plans to push down market or out to the edge arm is coming at this from the edge low cost low power superior price performance arm is winning at the edge and based on the data that we shared earlier from aws it's clearly gaining ground in the enterprise history strongly suggests that the volume approach will win not only at the low end but eventually at the high end so we want to wrap by looking at what this means for customers and the partner ecosystem the first point we'd like to make is follow the consumer apps this capability the capabilities that we see in consumer apps like image processing and natural language processing and facial recognition and voice translation these inference capabilities that are going on today in mobile will find their way into the enterprise ecosystem ninety percent of the cost associated with machine learning in the cloud is around inference in the future most ai in the enterprise and most certainly at the edge will be inference it's not today because it's too expensive this is why aws is building custom chips for inferencing to drive costs down so it can increase adoption now the second point is we think that customers should start experimenting and see what you can do with arm-based platforms moore's law is accelerating at least the outcome of moore's law the doubling of performance every of the 18 to 24 months it's it's actually much higher than that now when you add up all the different components in these alternative processors just take a look at apple's a5 a15 chip and arm is in the lead in terms of performance price performance cost and energy consumption by moving some workloads onto graviton for example you'll see what types of cost savings you can drive for which applications and possibly generate new applications that you can deliver to your business put a couple engineers in the task and see what they can do in two or three weeks you might be surprised or you might say hey it's too early for us but you'll find out and you may strike gold we would suggest that you talk to your hybrid cloud provider as well and find out if they have a nitro we shared that vmware they've got a clear path as does dell because they're you know vmware cousins what about your other strategic suppliers what's their roadmap what's the time frame to move from where they are today to something that resembles nitro do they even think about that how do they think about that do they think it's important to get there so if if so or if not how are they thinking about reducing your costs and supporting your new workloads at scale now for isvs these consumer capabilities that we discussed earlier all these mobile and and automated systems and cars and and things like that biometrics another example they're going to find their way into your software and your competitors are porting to arm they're embedding these consumer-like capabilities into their apps are you we would strongly recommend that you take a look at that talk to your cloud suppliers and see what they can do to help you innovate run faster and cut costs okay that's it for now thanks to my collaborator david floyer who's been on this topic since early last decade thanks to the community for your comments and insights and hey thanks to patrick morehead and daniel newman for some timely interviews from your event nice job fellas remember i published each week on wikibon.com and siliconangle.com these episodes are all available as podcasts just search for breaking analysis podcasts you can always connect with me on twitter at d vallante or email me at david.velante at siliconangle.com i appreciate the comments on linkedin and clubhouse so follow us if you see us in a room jump in and let's riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time