 Hey everyone, welcome back to Las Vegas. This is theCUBE'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 Vellante. Dave, it's always great to be at these events with you, but man, this one is shot out of the cannon from day one. Data, data, data, data, data. That's what you heard of here first. We have two guests joining us next. Please welcome Matthew Scullion, who's an alumni of theCUBE, CEO and founder of Matillion, and Harbir Singh, chief data architect and global head of data engineering from Western Union. Welcome gentlemen. Thank you. Great to be here. We're going to 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 theCUBE 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 the cloud in general. We've been doing that for a number of years. We co-headquartered in the UK and the US, hence my daft accents. And we work with all sorts of companies, commercial scale, large 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 going to talk about that in a second, but I want to understand what's new with the data integration platform from Matillion's 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, insights 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 shipped a product that we call MDL2.0, 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 right ahead log-based change data capture. I haven't come up with a shorter way of saying that but enterprise 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 but today over 1,000 joint Snowflake Matillion customers use including Western Union, of course, we've been adding features that are 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 ship that as well so it's been a busy year. Exquisitely simple, sorry, I love that he said that and it also sounded great with your accent. I don't mind the accent. Thank you, excellent. Javier, talk about your role at Western Union and what you've seen in terms of the evolution of the data stack. So in the last few years, you're a little bit of a Western Union, a 170 year old company, pretty much everybody knows what Western Union is. Driving an interesting synergy from what Matillion says, when data moves, money moves, that's what we do. When he moves the data, we move the money. That's the synergy between us and the organization that support us from data movement perspective. So what I've seen in the last few years is obviously a shift towards the cloud but within the cloud itself, obviously there's a lot of players as well and we as customers have always been wishing to have a smaller footprint of data so that the movement becomes a little lesser. 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 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 are a multi-channel organization. We have retail stores as well as 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 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 because I have this sort of theory that everybody talks about injecting data and machine intelligence and AI and machine learning into apps but the application development stack is like totally separate from 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 worlds 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 once the app becomes heavy, you'll have trouble communicating with the organizations. 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 are delivering those results back to the leaner applications much faster. So I'm a big proponent of, yes, it can be hybrid but 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 as well. In APIs or the connective tissue? APIs connective, yes. Also I think in terms of the data stack there's another parallel that you can draw from applications, right? So technologies 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 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 sits as well as other companies in this modern enterprise data stack as technology vendors are coming in allowing organizations to move faster and have higher levels of productivity. So I think that's a good parallel to application development. And just follow up on that. When you think about data prep and all the focus on data quality you've got a data team in the data pipeline, very specialized, maybe even hyper-specialized, data engineers, quality engineers, data quality engineers, data analysts, data scientists, but 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 are the lines of business taking more responsibility for the data and addressing that problem? It's like you die by a thousand paper cuts or you just die, right? That's the kind of thing. Because if I say 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 guy to choke, right? And in my role, I'm the guy to choke. So I have selectively tried to be a pseudo-federated organization where I do have folks reporting into our organization where 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 in 200 countries, so the regional needs are very different than our US needs. The majority of the organizations that you probably end up talking to have a very US-focused, more than 50% of revenue is international. So we are dealing with people who are international. Their needs for data, their needs for quality, and their needs for 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 organizations feel that they need to. Is there need for the underlying infrastructure and the operational details as diverse, or is that something that you bring standardizations to? So the best part about 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 decisions of bringing in different tools. Now I can standardize. I'll say, Machillion 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 infrastructure is global, but the data needs are dealt with locally. Yep. One of the part of your question, I was just thinking, Harvey has super well positioned funds for you, which is around that business domain knowledge versus technical expertise. Because again, early in technology journeys, things tend to be very technical and therefore only high-end engineers can do it. But high-end engineers are scarce, right? And also, I mean, we survey 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 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 Machillions are low-code, no-code platform. But Harvey and Western Union are doing this, right? 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. 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 owned 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 going to 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 miles an hour. So rather than spending, rather than building one Ferrari, let me have 10 Toyota's or 10 Prius's which will go further along and do better for my customers. So how do you see this all? We heard 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 want to necessarily say, but the product, the value that customers like you can get out of 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 in the past, it was days and days of churning the data to actually deliver results. Now the data has moved much, 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 data for a 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 us shut down operations in Russia. Ukraine is cash swamp. There's no cash in Ukraine. We have cash. We rolled out campaign $0 money transfer to Ukraine within four hours of the war going on. That's the impact that we have. Massive impact. Especially with such a macro challenge going on 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. Thank you guys. Thank you. For Dave Vellante and our guests, I'm Lisa Martin. You're watching theCUBE's live coverage of Snowflake Summit 22, live from Las Vegas. Stick around, we'll be back after a short break.