>>Welcome back to Couchbase connect. My name is Dave Vellante and we're going to dig into a customer case study of sorts with two software engineers from a company called trendy all the largest e-commerce platform in Turkey. And with me are in MRA, tan Rivera, both software engineers at trendy. All welcome. Good to see you guys. Hey, before we get into the story, maybe you can tell us a little bit about trendy all. >>Let me answer that question first. Um, tri-annual uh, today is, um, 10 years old. Uh, actually, uh, it starts with them, um, e-commerce company, uh, Jen, uh, especially, uh, for clothing, uh, today, uh, it's serves several, uh, services, uh, mainly still e-commerce right. Um, we, uh, we do our business mainly on technology and we even have a say in technology, uh, technology is our main concern actually. Um, just like that actually now, >>So thank you for that. I mean, you started, I think, I think the company was founded in 2009, 2010. So you weren't, you were just, you know, kind of which we would consider the, sort of the modern era at the same time. When you look back 10 years, you know, major challenges, major advancements from a technology standpoint. So you at, at the time you had a, uh, a legacy database and you, you had a migraine, maybe you could describe the business conditions that drove you to think about actually making a change. What was the before, and then we can get into the after and what was driving that change? >>Um, maybe I could start it a bit. Well, uh, we have a recommendation domain and try new. It's like when you, when you look at a certain product, like for example, you look at a pencil, it it's commanding you, uh, any razor, uh, if you are going to buy a pink dress, it's going to recommend you a yellow dress. So if you're going to maybe buy pants, it will show you some t-shirts according to it. So, uh, since the recommendations, domain group larger, uh, we, we have struggled, uh, to keep it high scale, and it wasn't a relational DB at first, but that's even as product count increased and, uh, our right frequency increased day by day, uh, and our reef performance was affected very dramatically. Uh, I believe. Yeah. >>So you were using a traditional RDBMS, uh, and then, and, and the issue was you quite, you couldn't make the recommendations fast enough. And, you know, we always say what's real time. Real time is before you lose the customer. So you, you have to make those recommendations in time for the customer to act otherwise, you know, what do you do? Send an email after the fact, Hey, you bought this, nobody's going to pay attention to that. Right? You want to catch them in the moment. Um, and so, so what was it that, that led you to, to Couchbase and w and what was the experience of that? You know, whether it was onboarding, you know, the technology, you know, how difficult was it to get up and running to where you are today? >>Um, we were using ch Couchbase in, uh, in inter-annual, um, for several years, and we had experienced on that. Uh, and, uh, we actually, we need performance as described. So, uh, we convert our data structure to, from relational DB to, um, noise, Carol Levy, um, them actually on our recommendation, uh, platform, the main problem was, uh, invalidation process. You know, um, we are selling things and, um, in seconds they can be sold out and we, we shouldn't be recommend them anymore. And we are, we are keeping track of this by invalidation process and relational DB writing those data to our relationship Libby was, uh, was taking two, two minutes too much time. And, um, by changing this structure to, uh, pathways, we, we, we see that benefits, uh, and it takes so, so, uh, uh, short time, actually, >>I'm so sorry if, if I can just clarify w what was taking a long time, the, the updating the actual records, so that you could actually inform a customer that it was out of stock, or was it the coding that was too complicated? >>Well, it was, it was not because, um, there are millions of products intangible, and, uh, those issues are coming huge, actually. So we are keeping track of time if it's sold out or it's, it can be sellable, uh, when, when a product, uh, detail is seen by the customers, we are recommending some other products too, but those other products must be sellable too. So the main, the main problem was that, and, uh, we are writing them in our relational DB. There is a huge rights law actually. So it was not coding. It was the amount of data actually. >>Okay. And so it was the update intensity, um, within the database and the ability of the database to actually return accurate results quickly. So what was the after, like, uh, can you talk about sort of the, the business impact? What were the, the improvements that you've experienced? >>Yeah. Maybe I can ask her that, uh, like parties said that the main reason we switched is because that, uh, there are so many products coming near in trend, and many of them are being stopped being sold out and the updates to it, it was on a relational, the vendor rights, or too much that you couldn't, uh, dur customers that fast reply because the database was getting effected by the amount of high rights. Because when you think about it, there are millions of products coming, and there are millions of rights, uh, operations on the database. So those affecting the reach performance. So, uh, it, it could occur to you that when you click on a product, you would see maybe as took out product as a recommendation, or maybe a product that is not in the website anymore. So, uh, when we switched to, uh, Couchbase that, uh, we saw that, uh, it's using less resources, which, uh, using less posts, active, alive, and it's also, uh, giving responses faster. >>The main reason, uh, we were using relational DB at first was the invalidation process like five. He said, because it was, we had a consumer that was listening to messages, uh, the innovation messages, and then, uh, and then the writing them into database. But, uh, in the part, uh, it meant that actively writing to database that for every product document that you would need to update the document, but for, uh, for, for, uh, for relational DB, it would be vetoed easier to just make this product, uh, every available, false, or true. So that's why we were sticking with relationship with DB at first. And that's why we made it that first as a relational DB, but as time increased and our product count, and our sellers increased, we realized that, uh, we should find another solution to the invalidation process, and we should, uh, switch because, uh, I mean, it CA it has come to a point at one point that it would just maybe, uh, take a solid, so much time that, uh, we were scaling our consumers at nighttime to just not affect daily users anymore. >>Uh, so that's why that's the main reason we switched. And, uh, after switching, we had in, uh, like I said, the response time and high write throughput, and also one of the reasons is also because that the, uh, the application that was with the use of Couchbase because, uh, since strangled is growing larger than our main data centers. And, uh, like we can see that every day, sometimes we deploy our, uh, apps to yet another cluster. And we, that's why we sometimes need to have backups or different data centers, and Couchbase was providing very good relations, very good solutions to this, which is. Yeah. That's why we switched actually. So we asked >>Couchbase running it's if I understand it, it's running the recommendation engine. And do you still use a traditional RDBMS for the transaction system or is Couchbase doing both? >>Yeah, okay. Uh, we are, uh, actually inter-annual, we are in discovery a team, actually, we call it tribe and in discovery, tribe, uh, relational DB, I think, uh, now, uh, very small, uh, small, uh, teams are using it. Um, it's personally just very low actually. Uh, but, uh, other other tribes, for example, orders, checkout, and maybe, uh, uh, promotions, uh, something like other teams are still using RDBMS, but in discovery team, it's very important to serve customers very fast. We need to show them the products immediately. We need to personalize them. Uh, we sh we should, uh, show them, uh, related products in the meantime, in real time, actually. So in this current Stripe, we are, um, barely using it, uh, RDBMS systems, actually. >>How hard was it to migrate from the RDBMS? Because you hear a lot of stories about how difficult that is to do. You've got to freeze the code, you bringing up new code, you've got to synchronize the functionality. How did you manage that? >>Well, to be honest with you, just ask the data science team to just send the products. Uh, at the same time, we were like, we were keeping the legacy API open that the clients were still coming there. And, um, to be honest, there were lots of legs on that, too. So even if, uh, the, the newer products came a bit later, uh, it shouldn't be seen because it was always coming late. So, uh, we had, we made a new API that is connecting to Couchbase and we wanted the data science team to start feeding it, but we asked the clients to switch it by time. I mean, we were still supporting the old one, but, uh, when we, when we asked the clients to switch to the new API, we just closed the last one. So we didn't really migrate any data to be honest. Like we, we, it was from scratch. And since it's a, it's a recommendation domain, uh, we believe it's better to, uh, add data's from scratch because in our new domains, we are storing them in documents. They are always sending a new list to us. So that's how it gets updated all the time. So since it's not a user related data, it wasn't really like a migration process. >>Is this is part of the secret sauce that you're doing. Schema lists, no schema on, right to Couchbase. And is that correct? And how are you handling it? I'm like, how are you getting that awesome write performance? >>Well, the main reason we believe is that, uh, before, when it was relational DB, like for example, loan product to one product and a second product to first product, third product, first of all, that like you were duplicating the records so that when the product gets removed, uh, from, from a product recommendation, or maybe one of, if a product is getting invisible, for any reason, it should be removed, or maybe it could be a stockout that it means it's not that for every record, you are sending your records for invalidation, but in our new system, it means that this, uh, for this content, there are 24 contents let's say, and like four of them that's finished. It's not there. It's okay. You're just replacing the whole list so that you are not duplicating the records. I mean, this is not like first product first and first, the second, and first to third, and first changes you are replicating this, this change three times, like a delete, uh, product one from three, three product, one from two, and you are deprecating the deletion record, but now we are just replacing the list. So you are doing that all of the operation in 1, 1, 1, uh, Kafka queue message. If I should be able, if I was able to, uh, tell about it. So it's a bit hard to explain it in, uh, in speech, but, uh, we have a nice graphic that's showing how we are doing it now. >>That makes sense. Okay. Thank you for that. And so, as you think about, you're modernizing your application infrastructure, where are you at today? How do you see this modernization effort going forward >>Actually, um, today, uh, we are mainly looking for, um, cross cluster replication. Uh, all our products are, uh, uh, deployed, uh, different clusters and different geographical locations. Uh, we, we always using ch um, we try to always use, um, modern products and, uh, uh, try to avoid, uh, old relational databases, especially for our discovery. Right. And, uh, my mandala is modernizing it, uh, all, uh, engineer's keeping up to date with recent technologies and, uh, our customers are happier. They are not seeing some glitches, some, uh, rates, uh, or while they're using our products. >>Okay. So maybe I could double click on that. So, cause you mentioned the impact of customers and I'm interested in your organizational impact and what it means for you internally, but, but when you talk about cross cluster replication, is that to scale, uh, is that a performance impact? Is that for availability? What's the impact of that effort? That modernization effort? >>Uh, I believe it's, it's all, uh, main reason is availability. I believe. Uh, like we can't know when a cluster can go down, we can't be sure about it, uh, in a, in a system we can, but that we should be up and running all the time. And, uh, there should be some, uh, some backups that, uh, that can switch when a cluster goes down. But also the main reason, uh, well, one of the main reasons is to be able to scale because, uh, the, the clusters that we had wasn't enough, uh, considering our user base. So, uh, let's say you want to even extend your user base, but, uh, like the cluster is being a bottleneck to you because you can't get that much users, but, uh, when you do post cluster that you have backup and you have scalability and it's, uh, considering how new considering if the machines are newer, maybe faster response times. I don't know, uh, maybe, uh, network part would know that better, but, uh, yeah, but all of them, I will leave. >>Great guys. Well, thank you so much for sharing your story, uh, uh, MRA and Fati. Uh, appreciate you guys coming on the cube. >>Thanks a lot. Yeah. Thanks. Thanks. Thank you for, uh, hosting. >>Yeah, it's our pleasure. And thank you for watching. Couchbase connect online on the cube, keep it right there for more great content from the event.

(up-tempo music) >> Welcome to today's session of the cube's presentation of the AWS startup showcase. The next big thing in AI security and life sciences. Today, we'll be speaking with Aizon, as part of our life sciences track and I'm pleased to welcome the co-founder as well as the chief science officer of Aizon: Toni Monzano, will be discussing how artificial intelligence is driving key processes in pharma manufacturing. Welcome to the show. Thanks so much for being with us today. >> Thank you Natalie to you and to your introduction. >> Yeah. Well, as you know industry 4.0 is revolutionizing manufacturing across many industries. Let's talk about how it's impacting biotech and pharma and as well as Aizon's contributions to this revolution. >> Well, actually pharmacogenetics is totally introducing a new concept of how to manage processes. So, nowadays the industry is considering that everything is particularly static, nothing changes and this is because they don't have the ability to manage the complexity and the variability around the biotech and the driving factor in processes. Nowadays, with pharma - technologies cloud, our computing, IOT, AI, we can get all those data. We can understand the data and we can interact in real time, with processes. This is how things are going on nowadays. >> Fascinating. Well, as you know COVID-19 really threw a wrench in a lot of activity in the world, our economies, and also people's way of life. How did it impact manufacturing in terms of scale up and scale out? And what are your observations from this year? >> You know, the main problem when you want to do a scale-up process is not only the equipment, it is also the knowledge that you have around your process. When you're doing a vaccine on a smaller scale in your lab, the only parameters you're controlling in your lab, they have to be escalated when you work from five liters to 2,500 liters. How to manage this different of a scale? Well, AI is helping nowadays in order to detect and to identify the most relevant factors involved in the process. The critical relationship between the variables and the final control of all the full process following a continued process verification. This is how we can help nowadays in using AI and cloud technologies in order to accelerate and to scale up vaccines like the COVID-19. >> And how do you anticipate pharma manufacturing to change in a post COVID world? >> This is a very good question. Nowadays, we have some assumptions that we are trying to overpass yet with human efforts. Nowadays, with the new situation, with the pandemic that we are living in, the next evolution that we are doing humans will take care about the good practices of the new knowledge that we have to generate. So AI will manage the repetitive tasks, all the human condition activity that we are doing, So that will be done by AI, and humans will never again do repetitive tasks in this way. They will manage complex problems and supervise AI output. >> So you're driving more efficiencies in the manufacturing process with AI. You recently presented at the United nations industrial development organization about the challenges brought by COVID-19 and how AI is helping with the equitable distribution of vaccines and therapies. What are some of the ways that companies like Aizon can now help with that kind of response? >> Very good point. Could you imagine you're a big company, a top pharma company, that you have an intellectual property of COVID-19 vaccine based on emergency and principle, and you are going to, or you would like to, expand this vaccination in order not to get vaccination, also to manufacture the vaccine. What if you try to manufacture these vaccines in South Africa or in Asia in India? So the secret is to transport, not only the raw material not only the equipment, also the knowledge. How to appreciate how to control the full process from the initial phase 'till their packaging and the vials filling. So, this is how we are contributing. AI is packaging all this knowledge in just AI models. This is the secret. >> Interesting. Well, what are the benefits for pharma manufacturers when considering the implementation of AI and cloud technologies. And how can they progress in their digital transformation by utilizing them? >> One of the benefits is that you are able to manage the variability the real complexity in the world. So, you can not create processes, in order to manufacture drugs, just considering that the raw material that you're using is never changing. You cannot consider that all the equipment works in the same way. You cannot consider that your recipe will work in the same way in Brazil than in Singapore. So the complexity and the variability is must be understood as part of the process. This is one of the benefits. The second benefit is that when you use cloud technologies, you have not a big care about computing's licenses, software updates, antivirals, scale up of cloud ware computing. Everything is done in the cloud. So well, this is two main benefits. There are more, but this is maybe the two main ones. >> Yeah. Well, that's really interesting how you highlight how this is really. There's a big shift how you handle this in different parts of the world. So, what role does compliance and regulation play here? And of course we see differences the way that's handled around the world as well. >> Well, I think that is the first time the human race in the pharma - let me say experience - that we have a very strong commitment from the 30 bodies, you know, to push forward using this kind of technologies actually, for example, the FDA, they are using cloud, to manage their own system. So why not use them in pharma? >> Yeah. Well, how does AWS and Aizon help manufacturers address these kinds of considerations? >> Well, we have a very great partner. AWS, for us, is simplifying a lot our life. So, we are a very, let me say different startup company, Aizon, because we have a lot of PhDs in the company. So we are not in the classical geeky company with guys all day parameter developing. So we have a lot of science inside the company. So this is our value. So everything that is provided by Amazon, why we have to aim to recreate again so we can rely on Sage Maker. we can rely on Cogito, we can rely on Landon we can rely on Esri to have encryption data with automatic backup. So, AWS is simplifying a lot of our life. And we can dedicate all our knowledge and all our efforts to the things that we know: pharma compliance. >> And how do you anticipate that pharma manufacturing will change further in the 2021 year? Well, we are participating not only with business cases. We also participate with the community because we are leading an international project in order to anticipate this kind of new breakthroughs. So, we are working with, let me say, initiatives in the - association we are collaborating in two different projects in order to apply AI in computer certification in order to create more robust process for the MRA vaccine. We are collaborating with the - university creating the standards for AI application in GXP. We collaborating with different initiatives with the pharma community in order to create the foundation to move forward during this year. >> And how do you see the competitive landscape? What do you think Aizon provides compared to its competitors? >> Well, good question. Probably, you can find a lot of AI services, platforms, programs softwares that can run in the industrial environment. But I think that it will be very difficult to find a GXP - a full GXP-compliant platform working on cloud with AI when AI is already qualified. I think that no one is doing that nowadays. And one of the demonstration for that is that we are also writing some scientific papers describing how to do that. So you will see that Aizon is the only company that is doing that nowadays. >> Yeah. And how do you anticipate that pharma manufacturing will change or excuse me how do you see that it is providing a defining contribution to the future of cloud-scale? >> Well, there is no limits in cloud. So as far as you accept that everything is varied and complex, you will need power computing. So the only way to manage this complexity is running a lot of power computation. So cloud is the only system, let me say, that allows that. Well, the thing is that, you know pharma will also have to be compliant with the cloud providers. And for that, we created a new layer around the platform that we say qualification as a service. We are creating this layer in order to qualify continuously any kind of cloud platform that wants to work on environment. This is how we are doing that. >> And in what areas are you looking to improve? How are you constantly trying to develop the product and bring it to the next level? >> Always we have, you know, in mind the patient. So Aizon is a patient-centric company. Everything that we do is to improve processes in order to improve at the end, to deliver the right medicine at the right time to the right patient. So this is how we are focusing all our efforts in order to bring this opportunity to everyone around the world. For this reason, for example, we want to work with this project where we are delivering value to create vaccines for COVID-19, for example, everywhere. Just packaging the knowledge using AI. This is how we envision and how we are acting. >> Yeah. Well, you mentioned the importance of science and compliance. What do you think are the key themes that are the foundation of your company? >> The first thing is that we enjoy the task that we are doing. This is the first thing. The other thing is that we are learning every day with our customers and for real topics. So we are serving to the patients. And everything that we do is enjoying science enjoying how to achieve new breakthroughs in order to improve life in the factory. We know that at the end will be delivered to the final patient. So enjoying making science and creating breakthroughs; being innovative. >> Right, and do you think that in the sense that we were lucky, in light of COVID, that we've already had these kinds of technologies moving in this direction for some time that we were somehow able to mitigate the tragedy and the disaster of this situation because of these technologies? >> Sure. So we are lucky because of this technology because we are breaking the distance, the physical distance, and we are putting together people that was so difficult to do that in all the different aspects. So, nowadays we are able to be closer to the patients to the people, to the customer, thanks to these technologies. Yes. >> So now that also we're moving out of, I mean, hopefully out of this kind of COVID reality, what's next for Aizon? Do you see more collaboration? You know, what's next for the company? >> The next for the company is to deliver AI models that are able to be encapsulated in the drug manufacturing for vaccines, for example. And that will be delivered with the full process not only materials, equipment, personnel, recipes also the AI models will go together as part of the recipe. >> Right, well, we'd love to hear more about your partnership with AWS. How did you get involved with them? And why them, and not another partner? >> Well, let me explain to you a secret. Seven years ago, we started with another top cloud provider, but we saw very soon, that this other cloud provider were not well aligned with the GXP requirements. For this reason, we met with AWS. We went together to some seminars, conferences with top pharma communities and pharma organizations. We went there to make speeches and talks. We felt that we fit very well together because AWS has a GXP white paper describing very well how to rely on AWS components. One by one. So this is for us, this is a very good credential, when we go to our customers. Do you know that when customers are acquiring and are establishing the Aizon platform in their systems, they are outbidding us. They are outbidding Aizon. Well we have to also outbid AWS because this is the normal chain in pharma supplier. Well, that means that we need this documentation. We need all this transparency between AWS and our partners. This is the main reason. >> Well, this has been a really fascinating conversation to hear how AI and cloud are revolutionizing pharma manufacturing at such a critical time for society all over the world. Really appreciate your insights, Toni Monzano: the chief science officer and co-founder of Aizon. I'm your host, Natalie Erlich, for the Cube's presentation of the AWS startup showcase. Thanks very much for watching. (soft upbeat music)