>>Hey everyone. Welcome back to Las Vegas. This is the Cube's live coverage of day. One of snowflake summit 22 fourth annual. We're very happy to be here. A lot of people here, Lisa Martin with Dave Valante, David's always great to be at these events with you, but me. This one is shot out of the cannon from day one, data, data, data, data. That's what you heard of here. First, we have two guests joining us next, please. Welcome Matthew Scalian. Who's an alumni of the cube CEO and founder of Matillion and Jer staying chief data architect and global head of data engineering from Western union. Welcome gentlemen. Thank >>You. Great to be here. >>We're gonna unpack the Western union story in a second. I love that, but Matthew, I wanted to start with you, give the audience who might not be familiar with Matillion an overview, your vision, your differentiators, your joint value statement with snowflake, >>Of course. Well, first of all, thank you for having me on the cube. Again, Matillion S mission is to make the world's data useful, and we do that by providing a technology platform that allows our customers to load transform, synchronize, and orchestrate data on the snowflake data cloud. And on, on the cloud in general, we've been doing that for a number of years. We're co headquartered in the UK and the us, hence my dat accents. And we work with all sorts of companies, commercial scale, large end enterprises, particularly including of course, I'm delighted to say our friends at Western union. So that's why we're here today. >>And we're gonna talk about that in a second, but I wanna understand what's new with the data integration platform from Matillion perspective, lots of stuff coming out, give us an overview. >>Yeah, of course, it's been a really busy year and it's great to be here at snowflake summit to be able to share some of what we've been working on. You know, the Matillion platform is all about making our customers as productive as possible in terms of time to value insight on that analytics, data science, AI projects, like get you to value faster. And so the more technology we can put in the platform and the easier we can make it to use, the better we can achieve that goal. So this year we've, we've shipped a product that we call MDL 2.0, that's enterprise focused, exquisitely, easy to use batch data pipelines. So customers can load data even more simply into the snowflake data cloud, very excitingly we've also launched Matillion CDC. And so this is an industry first cloud native writer, head log based change data capture. >>I haven't come up with a shorter way of saying that, but, and surprise customers need this technology and it's been around for years, but mostly pre-cloud technology. That's been repurposed for the cloud. And so Matillion has rebuilt that concept for the cloud. And we launched that earlier this year. And of course we've continued to build out the core Matillion ETL platform that today over a thousand joint snowflake Matillion customers use, including Western union, of course we've been adding features there such as universal connectivity. And so a challenge that all data integration vendors have is having the right connectors for their source systems. Universal connectivity allows you to connect to any source system without writing code point and click. We shape that as well. So it's been a busy year, >>Was really simple. Sorry. I love that. He said that and it also sounded great with your accent. I didn't wanna >>Thank you. Excellent. Javier, talk about your role at Western union in, in what you've seen in terms of the evolution of the, the data stack. >>So in the last few years, well, a little bit of Western union, a 70 or 170 year old company, pretty much everybody knows what Western union is, right? Driving an interesting synergy from what Matthew says, when data moves money moves, that's what we do when he moves the da, he moves the data. We move the money. That's the synergy between, you know, us and the organization that support us from data move perspective. So what I've seen in the last few years is obviously a shift towards the cloud, but, you know, within the cloud itself, obviously there's a lot of players as well. And we as customers have always been wishing to have a short, smaller footprint of data so that the movement becomes a little lesser. You know, interestingly enough, in this conference, I've heard some very interesting stuff, which kind of helping me to bring that footprint down to a manageable number, to be more governed, to be more, you know, effective in terms of delivering more end results for my customers as well. >>So Matillion has been a great partner for us from our cloud adoption perspective. During the COVID times, we were a re we are a, you know, multi-channel organization. We have retail stores as well, our digital presence, but people just couldn't go to the retail stores. So we had to find ways to accelerate our adoption, make sure our systems are scaling and making sure that we are delivering the same experience to our customers. And that's where, you know, tools like Matillion came in and really, really partnered up with us to kind of bring it up to the level. >>So talk specifically about the stack evolution. Cause I have this sort of theory that everybody talks about injecting data and, and machine intelligence and AI and machine learning into apps. But the application development stack is like totally separate from the, the data analytics and the data pipeline stack. And the database is somewhere over here as well. How is that evolving? Are those worlds coming together? >>Some part of those words are coming together, but where I still see the difference is your heavy lifting will still happen on the data stack. You cannot have that heavy lifting on the app because if once the apps becomes heavy, you'll have trouble communicating with, with, with the organizations. You know, you need to be as lean as possible in the front end and make sure things are curated. Things are available on demand as soon as possible. And that's why you see all these API driven applications are doing really, really well because they're delivering those results back to the, the leaner applications much faster. So I'm a big proponent of, yes, it can be hybrid, but the majority of the heavy lifting still needs to happen down at the data layer, which is where I think snowflake plays a really good role >>In APIs are the connective tissue >>APIs connections. Yes. >>Also I think, you know, in terms of the, the data stack, there's another parallel that you can draw from applications, right? So technology is when they're new, we tend to do things in a granular way. We write a lot of code. We do a lot of sticking of things together with plasters and sticky tape. And it's the purview of high end engineers and people enthusiastic about that to get started. Then the business starts to see the value in this stuff, and we need to move a lot faster. And technology solutions come in and this is what the, the data cloud is all about, right? The technology getting out of the way and allowing people to focus on higher order problems of innovating around analytics, data applications, AI, machine learning, you know, that's also where Matillion sit as well as other companies in this modern enterprise data stack is technology vendors are coming in allowing organizations to move faster and have high levels of productivity. So I think that's a good parallel to application development. >>And's just follow up on that. When you think about data prep and you know, all the focus on data quality, you've got a data team, you know, in the data pipeline, a very specialized, maybe even hyper specialized data engineers, quality engineers, data, quality engineers, data analysts, data scientist, but they, and they serve a lot of different business lines. They don't necessarily have the business, they don't have the business context typically. So it's kind of this back and forth. Do you see that changing in your organization or, or the are the lines of business taking more responsibility for the data and, and addressing that problem? It's, >>It's like you die by thousand paper cuts or you just die. Right? That's the kind >>Of, right, >>Because if I say it's, it's good to be federated, it comes with its own flaws. But if I say, if it's good to be decentralized, then I'm the, the guy to choke, right? And in my role, I'm the guy to choke. So I've selectively tried to be a pseudo federated organization, where do I do have folks reporting into our organization, but they sit close to the line of business because the business understands data better. We are working with them hand in glove. We have dedicated teams that support them. And our problem is we are also regional. We are 200 countries. So the regional needs are very different than our us needs. Majority of the organizations that you probably end up talking to have like very us focused, 50 per more than 50% of our revenue is international. So we do, we are dealing with people who are international, their needs for data, their needs for quality and their needs for the, the delivery of those analytics and the data is completely different. And so we have to be a little bit more closer to the business than traditionally. Some, some organizations feel that they need >>To, is there need for the underlying infrastructure and the operational details that as diverse, or is that something that you bring standardization to the, >>So the best part about this, the cloud that happened to us is exactly that, because at one point of time, I had infrastructure in one country. I had another infrastructure sitting in another country, regional teams, making different different decisions of bringing in different tools. Now I can standardize. I will say, Matillion is our standard for doing ETL work. If this is the use case, but then it gets deployed across the geographies because the cloud helps us or the cloud platform helps us to manage it. Sitting down here. I have three centers around the world, you know, Costa Rica, India, and the us. I can manage 24 7 sitting here. No >>Problem. So the underlying our infrastructure is, is global, but the data needs are dealt with locally. Yep. >>One of the pav question, I was just thinking JVE is super well positioned funds for you, which is around that business domain knowledge versus technical expertise. Cause again, early in technology journeys tend, things tend to be very technical and therefore only high end engineers can do it, but high end engineers are scar. Right? Right. And, and also, I mean, we survey our hundreds of large enterprise customers and they tell us they spend two thirds of their time doing stuff they don't really want to do like reinventing the wheel, basic data movement and the low order staff. And so if you can make those people more productive and allow them to focus on higher value problems, but also bring pseudo technical people into it. Overall, the business can go a lot faster. And the way you do that is by making it easier. That's why Matillion is a low code NOCO platform, but Jer and Western union are doing this right. I >>Mean, I can't compete with AWS and Google to hire people. So I need to find people who are smart to figure the products that we have to make them work. I don't want them to spend time on infrastructure, Adam, I don't want them to spend time on trying to manage platforms. I want them to deliver the data, deliver the results to the business so that they can build and serve their customers better. So it's a little bit of a different approach, different mindset. I used to be in consulting for 17 years. I thought I knew it all, but it changed overnight when I own all of these systems. And I'm like, I need to be a little bit more smarter than this. I need to be more proactive and figure out what my business needs rather than what just from a technology needs. It's more what the business needs and how I can deliver that needs to them. So simple analogy, you know, I can build the best architecture in the world. It's gonna cost me an arm and leg, but I can't drive it because the pipeline is not there. So I can have a Ferrari, but I can't drive it. It's still capped at 80, 80 miles an hour. So rather than spend, rather than building one Ferrari, let me have 10 Toyotas or 10 Fs, which will go further along and do better for my cus my, for my customers. >>So how do you see this whole, we hearing about the data cloud. We hear about the marketplace, data products now, application development inside the data cloud. How do you see that affecting not so much the productivity of the data teams. I don't wanna necessarily say, but the product, the value that, that customers like you can get out >>Data. So data is moving closer to the business. That's the value I see, because you are injecting the business and you're injecting the application much more closer to the data because it, in the past, it was days and days of, you know, churn the data to actually clear results. Now the data has moved much closer. So I have a much faster turnaround time. The business can adapt and actually react much, much faster. It took us like 16 to 30 days to deliver, you know, data for marketing. Now I can turn it down in four hours. If I see something happening, I'll give you an example. The war in Ukraine happened. Let is shut down operations in Russia. Ukraine is cash swamp. There's no cash in Ukraine. We have cash. We roll out campaign, $0 money, transferred to Ukraine within four hours of the world going on. That's the impact that we have >>Massive impact. That's huge, especially with such a macro challenge going on, on the, in, in the world. Thank you so much for sharing the Matillion snowflake partnership story, how it's helping Western union really transform into a data company. We love hearing stories of organizations that are 170 years old that have always really been technology focused, but to see it come to life so quickly is pretty powerful. Guys. Thank you so much for your time. Thanks >>Guys. Thank you, having it. Thank >>You >>For Dave Velante and our guests. I'm Lisa Martin. You're watching the cubes live coverage of snowflake summit 22 live from Las Vegas. Stick around. We'll be back after a short break.