 Great. Well, good morning everyone. I'm Anthony Farina from Couchbase. I lead our technology partnerships and solution partner relationships at the company. We're happy to be here at Red Hat Summit. As some of you noticed in the keynotes, the day one keynotes, both from some customer, joint customers like Amadeus, as well as some of the OpenShift team. We've demonstrated really some exciting innovation in the OpenShift in Kubernetes ecosystem. My colleague Mike Widerhold, who manages the engineering team, will co-present with me and we'll do a demo and an explanation of what we've done with operators in Kubernetes. But before we do that, I'll jump in just to provide a little bit of a level set of Couchbase, who we are, why we exist and why we're growing and why our customers are really investing with us. We're a venture-backed private company, really with the who's who list of investors and board members, including Mayfield and Excel. One of our board members is Don Chamberlain, who really co-invented SQL at IBM. Even Jeff Epstein, XCFO of Oracle, is on our board. So we are an enterprise, no-SQL data platform with a tremendous growth curve ahead, as illustrated by our customer deployment. So we're a horizontal data play. We have use cases that fit across different verticals, and some of our customers are here, whether it's financial, healthcare, travel, retail, e-commerce. A lot of use cases that we'd be happy to step through in more detail. But why is this happening? Why are customers really adopting Couchbase? Everyone talks about digital transformation. What are the pressures, existential threats for all of these enterprises, to be more innovative, respond with agility, and really show value and customer loyalty with really differentiated offerings? And the database is one of the key impediments. The traditional legacy relational databases have proven to be one of the impediments for making these digital transformation projects successful, which is why we've really had a lot of traction in the market. OLTP-based and OLAP systems have their limitations, specifically when it comes to schema rigidity, flexibility, the ability to deploy at pace, and to really innovate. And what a lot of people are doing is trying to layer in some point solutions to get you performance at scale, to give you a geo-replication, resiliency. So there's a lot of point solutions that come onto the market to address these things as complexity, as cost. And Couchbase, we've created a data platform, which we term the engagement database. The engagement database complements your OLTP and your OLAP investments, whether you have a Hadoop data lake, we have connectivity. But this is really, as Amadeus illustrated yesterday in the keynote, we play in customer engagement applications. So interactions, mobile, IoT, likes, tweets, browse, personalization at the edge is what we define as an engagement database use case. And to have an engagement database, it has to be cloud native, multi-cloud portable with those applications, has to be mobile first and really offline first, even if you don't have connectivity, the app always has to work and be responsive. We have SQL for JSON. So we're both a key value and a document store. So we've got all the built-in enterprise capabilities, and we're really built for performance at scale, which is really a unique differentiator for us. And with all the security features that are expected in an enterprise data platform. Just to illustrate what we've built here, we're memory first. We have a list with a persistent layer. We can scale horizontally. We're a scale out distributed database. And the data platform, in addition to having replication and indexing capabilities, we've iterated the platform to add new capabilities, mobile, analytics for complex query, query for operational queries with with NICL, N1QL, our SQL for JSON, and full-text search, inverted index capabilities. So we can flatten the stack with your deployments and take out a lot of significant redundancy and cost and give you high performance at scale. The customer problems that we typically solve and we'd be happy to discuss at the booth are around three vectors. How do you improve customer experience? I got to roll out a new app. I got to change my e-commerce. I have to change how I engage my customers. Those are CMO, CDO, CIO. How do I use the data at the edge for better monetization and customer loyalty? Faster time to market, typically a DBA or data governance or data modeling thing where the rigidity of a relational database is holding you back from innovating. So we address those use cases. And of course, in the IT layer, people that are struggling with Oracle ELAs or traditional database costs or you're scaling up exadata or you have kind of a lot of technical debt, mainframe that you want to offload, those IT and cost reduction plays are a good fit for couch base. What did we announce this week? So we have a great relationship with Red Hat. What we announced or Red Hat demonstrated in the keynote yesterday was our investment in the couch base autonomous operator in Kubernetes. So we've extended those APIs and we're the first NoSQL player to really have automated operations in Kubernetes on OpenShift. So no downtime, elastic scaling, and you can really run with confidence business critical applications on OpenShift across clouds. No vendor lock in with geo replication built in. We do this based on how we're architected. For those that are technical in the audience that would like to learn more, we have a lot of isolation in the services. So we're a peer to peer database. Each node can look the same as you scale out with index service, full text search, query analytics, etc. Or as Mike will demonstrate, you can tune some of those underlying services and a custom resource definition script. And you can optimize for cores for memory for disk based on the application and the access patterns that you have. I'll turn it over to Mike here shortly, but our solution fundamentally solves some of these core problems. Geo replication at scale with fast in memory performance, centralized management where the easiest database to manage, especially with this autonomous operator, auto provision, auto recovery for business critical applications. And we do it with OpenShift across any public cloud. You can run couch base in any public cloud through the marketplace. We run on bare metal. You can do it in private cloud. So that's just a quick summary on couch base. We'd be happy to discuss that with you further. For now, I'll turn it over to Mike Wieterhold who led our engineering effort on the autonomous operator. Yeah. So an operator basically something that allows us to encode exactly what a human would do when they were managing a cluster in couch base. But for all of that to happen automatically. And so we're going to show you today is a demo of some of those things running. And so today what that means is that you can just based on filling out a simple configuration, you can automatically deploy a couch base cluster. And you can also handle failure recoveries. So if you have nodes that go down, you can if it happens at three o'clock in the morning, it'll just be taken care of automatically by the operator. So that's what we can do today. But tomorrow we'll be able to do automatic upgrades, automated backup and restores. And our goal is to automate every task that can be that a human would do when they're managing their clusters. And so if you're running your operator in OpenShift, then the first thing that you need to do is install your operator. So in order to install the couch base operator, we give you a simple yaml configuration file. So I'm going to gloss over this really quickly. But the key thing is that basically you just want to pick the version of the operator that you're running, which is this line here. And to install it in OpenShift, you just run a simple command that says create the operator. And this will install a custom resource which tells Kubernetes about exactly how you define a couch base cluster. And we'll install the software to manage your couch base clusters. And so if I jump over here to the OpenShift web console, we can now see that we have a deployment that's running the couch base operator. We put this in a deployment because if your operator ever crashes, you want it to come back up so that it can keep managing your clusters. So now that we have the operator installed, Kubernetes knows how to create and manage couch base clusters. So what we want to do is create our first couch base cluster. So this is a simple configuration to create a three node couch base cluster running all of the services that we supported in couch base 501. And the key things to note here is the size is three. And if you're familiar with any of the other parameters that you can set in couch base, we set some of the memory quotas. We create one bucket. The bucket is equal to a database in couch base. And so to load this up, this is as simple as running another command which just says create the couch base cluster. And if I jump back over here after I do that to the OpenShift administration console, we can see our first pod has already come up. And I've created a route in advance so that we can get to the couch base UI. So this usually takes a second before the route will recognize the pod. There we go. And so I will just log in here and then we will be able to see the cluster being built. And so we can see that the first node in the cluster is already there. And we should see another node come in momentarily so we can see that it's already been created in the OpenShift cluster. Or it's already being shown in the OpenShift console. And so the first node has come in and we're going to have another one that comes in any second. And then once that last one comes in, the operator will kick off rebalance operation, which will distribute all of the data across all the nodes. Looks like my route went down. Okay. I'm going to create a node port or I'm sorry. Okay. So I just created a separate way to get into the admin console here. But now you can see that the entire cluster is already balanced and we have the one bucket that we created. So one of the really interesting things that the operator also takes care of is if you have a node crash. And so if I come back to the service tab and then I just go and I say I want to delete CBExample 0002, this would simulate a failure of a node. And so if I come back you can already see that the node is down in the couch base cluster. So we have our auto failover timeout set to 30 seconds and so after 30 seconds this node will be auto failed over. And what that means is that so right now a third of the data is actually unavailable in the cluster. But once the node is auto failed over we have replicas on the other nodes and so those replicas will be activated so your applications can continue to access the data. Once that happens we will add in another couch base pod and then we will rebalance all the data so that we get right back to the state that we were expecting to be in. So we can see that the node has just been auto failed over and we should see another node get added into the cluster any second. So we have our new node which was just added here and then we should see a rebalance operation kick off. I just kicked that off myself there. So those are the two main operations that we're able to do with the couch base operator and we also support other things such as auto scaling. So if you want to scale up your cluster it's as simple as coming back to this configuration changing three to four and that will automatically have the operator add a new node for you. Awesome. All right. Thanks Mike and thanks for folks if you want to learn more about the couch base operator we're in booth 342 right behind you here. I also want to call out some of the resources that are available. We did post at KubeCon a nice blog post on how to use the operator for Kubernetes. We are in technical preview and in beta in collaboration with Red Hat. So some of our customers are experimenting with the operator and you can hit that link to to learn more. There's also a Red Hat technical implementation guide that'll be available. We're also in the container marketplace and a number of resources for you to refer to. In addition our customers and a lot of our Red Hat joint customers are presenting at our couch base connect events. We we have a technical workshop today in New York and our main show where UPS United Airlines and a number of customers are describing how they use couch base and Red Hat will be a platinum sponsor for future couch base events including coming up in London next month and our big show in Silicon Valley later in the fall. So we look forward to engaging with you and appreciate your time here. Thank you very much.