Live from Seattle, Washington. It's theCUBE, covering DockerCon 2016. Brought to you by Docker. Now, here are your hosts, John Furrier and Brian Graceley. Okay, welcome back everyone. We are here live in Seattle, Washington for DockerCon 2016. It's the SiliconANGLE Media's theCUBE. It's our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier, my co-host Brian Graceley, another hot startup up here. Robinsystems, we've got the CEO and CTO, CEO, Pramal Bhuch and Partha Satala, CTO of Robinsystems. Welcome to theCUBE. Thank you. Good to see you guys again. Again, you guys are startups. We've got 15 million in funding last fall. You're doing something a little bit different with storage. Unshackling applications that have a container platform. So, explain that. It seems to be a lot of people focusing on storage to make storage go faster, better, all flash, what we see the whole time. But you guys take a different approach. Explain, take a minute, explain what you guys do specifically and why it's not just another storage container startup. So, what we are doing really is looking at how to apply containers to the problem of virtualization. So, what is a piece of it? But it's not everything. Really, when we look at applications like databases, applications that consume a lot of data, big data, Hadoop, Spark, even no SQL data like Mongo, Cassandra. What we see is that these are traditionally run on bare metal. Virtualization hasn't really made a lot of headway because they're performance issues with the hypervisor performance tax. Containers are a great solution. They eliminate a lot of the hypervisor performance tax, but they haven't really addressed the issue of data gravity. When you've got terabytes of data, how do you really make that portable so that you can get all the applications virtualized on containers? See that storage startup?  Exactly, and so, we believe that you need to look at the whole virtualization problem end to end. If you really want to truly solve the performance issues, provide performance isolation when you pack multiple applications on the same physical hardware and do that in the context of containers. Yeah, so it's a very different approach. You've got a lot of things going. I've got software defined storage. People are still working through. You've got containers, which a lot of times is very kind of new application centric. We're seeing this movement back to people saying, hey wait, it works for enterprise applications as well. Walk us through how you guys think about performance when you're dealing with software, you're not dealing with hardware anymore. Help customers understand that mindset because that hasn't necessarily been the case with storage and data management in the past. It's been very hardware centric. Yeah, go ahead. Yeah, the big difference between a traditional app that's continuous today and the enterprise apps is that the enterprise app is more complex and its need from the storage side is more complex. But beyond that, once an enterprise app is deployed, there's this life cycle management that becomes very important. Things like snapshotting, things like cloning. Or when it's done in the context of storage, just a storage subsystem, that problem has been solved. Once you elevate that to an application construct, that is more complicated, especially with the modern day distributed applications. You're not talking about a single volume that's being snapshotted, but you're essentially looking at a collection of volumes that spans the entire topology of an application. And in fact, spans the entire service that an application depends on, whether it's a database, an application server, and so on. The entire life cycle has to be done from that context. And again, from the performance point of view also, if you look at a complex service which has databases on the bottom and an application service on the top, each of them might have a different performance need in terms of an IO ops or throughput and so on. So essentially you've got to enforce the IO ops or the throughput upper and the lower limits based on what the application needs. So that essentially changes once you look, I mean, essentially infrastructure management changes once you look at it from the applications context. So all the big folks, we had Jerry Chen from Greylock on who always likes to come on the queue. He's been a CUBE alumni since we started and he's always got his finger on the pulse. I asked him the question, where's the focus? Let me use all my chat box and all this stuff. And what he pointed out was really kind of what you guys are having core focus. That's the applications are driving the infrastructure policy stack characteristics. One, do you agree with that? And two, how do you talk to customers and say, hey, I want the infrastructure just to work? I have VMware, I got virtualization, I want to virtualize, but I want my app workload to be really awesome and fast and guaranteed. I mean, what's the conversation like? Take us through that. So I mean, so far we've been looking at either infrastructure and isolation of apps, right? You're looking at the storage, you're looking at just containers by themselves and so on, right? So the conversation that you have with customers is essentially what can we do for their applications? How can essentially move application awareness into the infrastructure, right? And that essentially drives a bunch of use cases that you would not really look at here. A pure play, let's say a storage for containers or a pure play orchestration, for example, right? You want to add something more? Is that their pain point orchestration? I mean, where's the aha moment for your customers, for you guys, when you generally walk in there? Aha moment, I would say is predictability, right? Performance predictability, at the same time, given the consolidation, right? And that is something that you cannot achieve today, right? The moment you essentially put multiple apps in the same host, whether it's containerized or not, predictability is gone, right? In fact, some of the demos that you're showing at our booth, that customers come and ask us, how do you guys do that, right? How do you essentially enforce both max IOPS, min IOPS, and how do you do it across multiple apps? So what do they focus on when you guys give them the demo, when they ask those questions, obviously they're impressed, they're trying to dig into what the value is, and when you're at secret sauce, but what gets their attention, what's the one thing that in the demo that you guys are showing that gets the customer's attention? How do you do it, I guess? How do you enforce? Yeah, it's magic.  You know, infrastructure's always been a little more, it's a little slower moving, it's resilient for a reason. I mean, is what you guys are doing really trying to disrupt that balance that says if the applications are going that much faster, it's got to be software, it's got to be delivered performance, but I need to make it simple to deploy, simple to move things. Is that really the, you know, John talked about the AHA moment, but is that really the thing that people are looking for a new balance, a new equation for, to make that work? So a lot of people we are talking to, they are trying to figure out what all these new big data applications mean for infrastructure. And in many cases, they are kind of kicking tires trying to figure out what they do with containers, infrastructure, storage requirements, what the application is going to be like tomorrow, and how do we future proof the infrastructure. And I think that's a conversation where we are essentially an infrastructure layer, application agnostic, but we provide them that whole future proof portability, that's what they get some interest in. And are you guys, do you have the capability of somebody who says, I want to start small on top of Amazon or Azure, but also have an on-premises deployment? Can you link the two together? Are there ways to take advantage of different sort of resources? Yeah, so today most of our customers are on-premise customers, but we have several conversations going on on the hybrid cloud aspect where they're trying to move some of their workload into the cloud, and how they can do it with a single pane of management, with Robin being sort of the common layer that enables them to do that. So you guys talk about the application centric data center or application defined, software defines pretty much what's been the big buzzer, application specifically, what on the product side, how do you explain that to customers and say, what does that mean to, first, what is application defined data center mean to you guys? So if you look at virtualization, the traditional virtualization gives you VMs as a proxy to physical machines. When it comes to deploying applications, you still need to coordinate between the VMs to get the application up and running. What we are saying is that on top of providing that entire virtualization stack, compute network storage, we're also adding an extra layer of abstraction so that the customer, the user only needs to worry about deploying an application at a level of, give me 10 node cluster, and we are doing all the plumbing beneath that. So the whole combining that image management, deployment, orchestration, along with the traditional compute network storage virtualization, that when you put it all together, that's what we really talk, I mean when you say application defined data center. So Farth, I got to ask you the product question. You guys are startups, obviously you're pedaling as fast as you can. You got customers, you got some big names on the case studies you're working with. What's the product focus right now as priorities for engineering? What's the, and obviously the basic, it's stuff out solid shipping, but what new things you guys working on? What's the roadmap look like for Robin systems? So I mean we started off with focusing on data applications and at least in the near term we won't essentially focus on that and have a more complete story so that we essentially span all types of apps. The last cube we did at Strata, we showed a demo of how we do a Hadoop and how customers are running Hadoop, right? We're expanding that to a lot of other applications like Oracle, Cassandra, Mongo, modern day as well as the traditional enterprise databases and so on, right? And then the next step for us essentially would be to essentially expand this from just an on-prem solution into the cloud. And when you say cloud, it's not just taking cloud and then deploying our software there, but essentially taking the characteristics of a cloud, it's instant expandability and the elasticity that you get with cloud and then weave that into our product so that the customer gets a very seamless experience deploying these apps. So I'll compare and contrast the Hadoop ecosystem to the Docker ecosystem. We love to talk about the Hadoop because we've been covering it for a long time. We've seen the evolution and our point at the last Strata was we're still seeing customers saying it's still complex, I mean it's getting better certainly, but it's still complex and the total cost of ownership is high, but it's getting better. It's certainly working, big data, no brainer. Docker, we're trying to tease out the same thing. It's complex in the sense there's a lot of noise here, people who do different parts in the stack. How would you guys compare the two ecosystems? Because Docker seems a little bit more agile in terms of less cost, because it's very developer focused. We're trying to put our finger on cost of ownership and can you guys comment about your thoughts on the two ecosystems? Okay, so since you brought up two examples that have Docker and Hadoop, right? So I think the way you look at the problem is very different. I think the way you look at the problem from a Docker angle is a very container-specific thing. It's not really application-specific yet, and I'll expand a little bit on what that means. But when you look at a more complex application, it's not just one container. It's not even just a collection of containers. It's basically a group of containers playing different roles, and all of them are interconnected with each other, and each of them depends on the other for different things. And when we deploy these things and we essentially want the customer to look at it not from a container or like Trimble said or the virtual machine, or look at it from a pure application perspective. Just to take an example, right? Let's say you're deploying Cassandra. You would go and say, I want to deploy a Cassandra cluster. You don't go and say, I want to have these many VMs. You say, I need Cassandra. It requires this amount of scalability, so I want to create those many number of partitions. Each of those partitions requires different IOPS. How do I set it up, right? So essentially, define your application needs from the context of an application. So move at a speeds and fees just getting, standing up the resource, basically. And Hadoop has had some. So Hadoop is actually a very big example of that, right? Because Hadoop is a very complex thing. Just to get a Hadoop cluster going is very difficult. And in our product, it's essentially a push of a button. You define, go to the application creation page and just click. Easy button, as they would say. Easy button. One click, one click deploy of any complex data. Okay, so I got to ask the question on the website. You say unshackle your apps. What does that mean? When you say unshackle your applications. What do you guys do to unshackle apps? So let's say you are deploying a virtual machine today. You as a developer are using a virtual machine. Very rarely do you know where it is running. All you know is you go to the virtual machine or the VDI, let's say admin, and you say, I want to get a VDI thing, right? You don't really care about where it's being deployed. We essentially want developers to look at application deployment in the same way. Forget about asking for first. You say, I want to deploy this app, right? So once you do that, you're essentially taking away applications from being dependent on infrastructure to just defining what you want and you just get that magical thing. So the whole composable infrastructure kind of narrative that's been kicking around, certainly HP's writing that happy, is what you guys are offering. Exactly, and particularly for the applications that we are going after, they tend to be run on bare metal silos. So when you talk about unshackling, it is about that fluidity, the portability that we are talking about, where you don't really need to do physical provisioning for specific machines. We can completely take away the deployment times, the provisioning times, and make it all portable. That's the key part of the unshackling. This is a real changeover. Guys, I'd like you guys to spend a minute, talk to the audience about Robin Systems. Take a minute to share. There could be potential customers out there watching. Why Robin Systems? Why should they be engaging with you guys? I think today the biggest problem that we see with data heavy applications is that there is no real solution that allows the high performance and performance isolation that you get with bare metal silos. With the flexibility, the agility, the portability, with things like containers and VMs, we are trying to bridge that gap. And I think what you get by working with Robin Systems is a seamless fluid infrastructure where you can maximize your hardware utilization but also make it very easy to manage and deploy applications. So you have big focus on the delightfully easy to use interface, where you take away all the complexity of the plumbing out so that you can just focus on what matters to you, which is application management. And we take out of all the headaches, we're managing infrastructure behind that. Taking the complexity out of DevOps, taking all that complexity out of the Ops side, building the software. Like you said, I want Hadoop, just give me Hadoop. I want Cassandra, just give me Cassandra. So we've talked about that in our research. That's the way it should work. Yeah, we've talked about that from a research perspective. We think there's going to be about $300 billion out of that big cloud market that's just going to get embedded back into software and take it out of operations costs. Yeah, it reminds me of the famous Steve Jobs when he took over Apple. He moved away from speeds and feeds to solutions and what are we trying to do kind of thing. So that's awesome. Well, final question I want to ask you guys, and I'll put a little different spin on the question is, what's the coolest thing that you've seen your technology be used for in a customer scenario? I think for me, the most exciting thing has been how one of our customers who was really struggling to figure out whether to go on cloud, whether to stay on bare metal, their decilos, they looked at our solution and shut down both of their cloud and the bare metal plans. And we are going to use you to virtualize the entire deployment and that, when that aha moment happened to them and they changed their entire strategy and mood, completely locks up and barrel behind a Robin based solution, that was very exciting. Selfishly as a startup, it's a great first moment validation. The first customer really gets it, right? Totally gets it. And they actually change their infrastructure over. Absolutely. Do you think you agree at your coolest moment? Absolutely. What's the coolest technology that you have in the secret sauce tool chest that customers go crazy for? Can you share any tidbits on the IP? Well, from a technology perspective, I have not seen any company essentially bring applications into infrastructure, essentially making infrastructure more application aware, right? It actually spans a bunch of things from a technology perspective. It's not just not storage, it's also thinking about lifecycle management. So I think that is the core IP that we have. I've not seen anyone do that. Yes, it's a winning formula, application aware, fluid infrastructure, pools of resource. Robin systems here on theCUBE guys, thanks so much, co-found hot start up here at DockerCon 2016, it's theCUBE. I'm John Furrier with Brian Gracie. We'll be right back here watching theCUBE.


Well, you guys know that I have a lot of ring devices in my house. We've got the ring alarm system, we've got a whole bunch of ring cameras, we've got the ring doorbell, but I'm here with Nicholas Morpher to find out what else I need to buy from Ring. How are you doing today? Doing great. Doing great. How about yourself? All right. We're having fun. Tired already. Hey. All right. So you guys are branching out into lighting. Yes, absolutely. So we actually do have a lineup of our Ring Smart Lighting this year that we've rolled out a few new devices. We have some solar lighting as well as some smart LED light bulbs as well. So just expanding out that range of lights that we've got. And that's starting to add more to the ring of security, right? Yes. Use the marketing term. Come on. Yes. Hey, that ring of security, building that out as well. So yeah, one thing that's really, really great about these is that you can actually link them to your other ring devices, like your doorbells or cameras. So let's say someone comes up, you know, one of the devices goes up, you can have your camera start recording automatically, then just imagine your lights go lighting up that pathway. So let's say someone's trying to do harm or anything like that, boom, all these lights go up as soon as they step onto your driveway, kind of scare them off immediately. And then you can also get that alert way before it gets into that motion detection range of the product itself. I see. I see. So describe what we've got here. So what we've got here is we've got our lineups. So we've got a smart light, we've got a floodlight wired here. So if anyone wants to replace that existing light fixture, you have that option. We have a battery version. And then the newest one is the solar version of it as well. So just with a solar panel, this one does come with a rechargeable battery as well. So you can charge up that battery, put it in. And then once you hook up the solar itself, it'll just retain that energy, keep that battery up. This is like the floodlight cam but doesn't have a camera. It doesn't have a camera. They're motion activated. Just pure lighting is what we're going for here. Okay. All right. And I like these tall, posty looking things. So this is our existing pathway light and then this is our new solar pathway light. The solar, so the screens are right up here on top of it right there. Also does come with that rechargeable battery itself. And then how does that, that's a pole that does that stick in the ground somehow? So it does have a little stake that shoves into the ground. Okay. So what makes that not fall over like every other solar light I ever see on somebody's driveway? Well, I mean the stake is pretty far into the ground. I believe we do have some that comes with it to keep it secure into the ground itself. Okay. So a few additional things. We always provide all the tools you need with all of our products. So we make sure we include everything you need to make sure that's going to set up properly. Okay. So the smart lighting path light would light up. It would get motion, it's got motion sensing and solar power. Yep. It picks up somebody walking down the hall and then how is that talking to the internet or talking to the other device? So great question. So the way that works is actually this device right here. So the ring bridge. So what you do is you hook this up to your home Wi-Fi and then that sends out a thousand-foot radius. We're using a proprietary signal that we're using that allows all these devices to connect to the bridge and then in turn connect and link to your other devices within the ring app itself. So I like this. So when I change my network again, I move that ring smart lighting bridge. Yes. And all my lighting goes with it. I don't have to do every device one by one. Whoa. Somebody triggered the sensor. All the lights just went on. Scared us. Sorry about that. Okay. So we get the smart lighting bridge and we put that inside the house connected up to the Wi-Fi. Yes. And then you start adding the lights. Yep. So then you can do like a front yard if you want to. You can do a backyard side yard. And then you can start to create also routine. So if you have like- So you're creating areas. Yeah. You're creating areas, grouping them together as well. So you can have that activate. So let's say I have a floodlight in the backyard. I only want these lights to activate whenever that floodlight detects motion or if they pick up any motion, that's when I want that floodlight to turn on. It goes both ways. And not just that you triggered the light and that triggers the camera. If it triggers the camera, it'll also trigger the light. Yeah. So it's our link devices, which actually allows you to link things. So I currently have it at my own. I just have an additional floodlight cam and then I have a stick-up cam as well. So floodlight detects motion. That stick-up cam turns on automatically. So I get two different views of the incident that it picked up essentially. Now are you getting better video too? Exactly. Because you've added light to that, right? Yes, absolutely. Plus there are lights. There are lights as well. Light is nice when you walk out at night. Absolutely. You're just stepping right outside. Everything lights up. You're not blinded. You can't see anything. But now you're able to see with these things. And everything is so that they're battery operated. And everything is less than $100 as well. So you're not freaking to think on anything at all. The only thing that's right about $100 is our smart lighting transformer right here. And this is for people that have existing low-voltage lighting already. They just remove their transformer, replace it with ours. And then it makes all those devices smart. Smart lights to get them onto the Ring app and then connect it to the Ring bridge. So if you already have pathway lights or low-voltage lighting, just replace our transformer with that existing one. And then those all now become part of the Ring app. Okay. All right. That sounds good. So then I see smart lighting steplight. Yes. So this is a little bitty one. A little bitty one right here. So a steplight. A lot of people tend to use these inside of like, you know, closet stuff like that. As well as on top of steplights. And then we have a new solar version that we're coming out with as well for that. So we actually have them set up a little bit around the booth as well. Some of them are on the, we got them in white coming out. But yeah, so that's one of the new devices that we have coming out. So what about, I already am paying the way the Ring devices work. I think you, it was, yeah. So there's, it's 30 bucks per device. And then once you hit 100, you're done. It basically, yeah. So the way that that works is so it's. Per year by the way. Yes, absolutely. So thank you. Not per month. No, no, no, not at all. It'll be $3 per month. But the way that it works, so we have that Ring Protect Basic plan. That's basically for any device, usually a doorbell or a camera that you want to have the videos recorded. It's a totally, totally optional sign up for it. But if you want to, that's $3 a month or 30 for the year to save your videos for 60 days to download at any time. But when you get into that range of having four more devices, it makes sense to go with the Ring Protect Plus plan, which just taps out at $10 a month or $100 for the year. And that also covers the alarm system as well. Nobody believed me that I got, so I basically got the alarm system for 10 bucks a year because I was already at 90. So it's like I was making money right there. And so that's a really, really big selling point of that plan itself. So it covers all your devices at that address. So you can have as many doorbells or cameras. Keep adding them on. You're not going to pay any incremental cost. And it also includes that 20 for 7 professional monitoring, as well as slightly your backup on the base station of the alarm as well. Yes, and we like that part. So now, let's say somebody doesn't already have all of these devices like I do. They pick up one camera. They want to do the 30 bucks a year. Now they want to add some lighting. Do they have to pay for the lighting? So I mean, they're not going to have to pay so there's no additional fees on top of just the purchase of the item itself for the lighting. So they're just going to buy the product itself. But there's not going to be any tie-in to that protect plan. The protect plans are really going to be more for the alarm system as well as the recording capabilities. Okay, so since the lighting doesn't record anything, it's not going to be a cost to use. So there's no cost to us. Exactly. That is very cool. So if people want to find more out about Ring, where would they go? They would go to ring.com. That's such an amazing URL. I still can't believe Jamie got that. Anyway, thank you very much. I really appreciate that. No, thank you so much. We're stopping by. Let's go.


Thank you so much everyone for coming along this session. So my name is Mohawad Ali and I'm research data specialist in data architecture at ARDC. So today I will be presenting about the data architecture checklist that we have developed recently and I will be particularly focusing on the role of data quality in this data architecture checklist. So I will start by acknowledgement of the country. So we acknowledge and celebrate the first Australians on whose traditional lands we meet and I pay my respect to the past, present and emerging. So this is the content for today's presentation. I will start with some of the concepts of data architecture and the relevant engineering behind it and then I will walk through how data quality can help improve in the existing data architectures and then I have included a practical scenario where I have added the both concepts of data quality and the data architecture and following that I will walk through the data architecture checklist and I will also discuss some of the common challenges and existing obstacles in this domain actually. And following these challenges, we have proposed some solutions that how we can improve the existing architectures and workflows and since nowadays there are a lot of discussions around AI and ML. So I have also included how AI and ML can part its role in enhancing the data quality in the data architecture. Following that, I will discuss the future perspective of what next from here and then at last I will define some of the key takeaways and conclusions from this presentation. So this is the first slide which shows high level architecture diagram. So in data architecture, what it defines is data architecture basically defines the data strategy and data flows within the organization. So it basically consists of five layers. So it starts from the data collection then from data processing and then transformation and how the data is stored. So at the end, we usually discussed how the data is used and analyzed in terms of analytics. So in this figure, we can see that the first step is started from the data collection and acquisition. Then there are some kind of processing following by some kind of transformations and then storage in different databases. And then lastly, I have highlighted some kind of analytics in terms of visualizations and data science driven decisions. I have also included ML Ops in this data architecture diagram because nowadays there are a lot of discussions around deployment from the conceptualization. So ML Ops basically help us to convert the concepts into a kind of real time deployment and production. So following this data architecture diagram, so these are some of the data quality metrics that usually embed in data architecture. So some of the terms are quite common. Let's say there's a term of completeness, accuracy, consistency, timeliness, validity, uniqueness and relevancy. These terms are quite common across different domains but today we will be describing specifically in terms of data architecture. So when we describe these terms in terms of data architecture, let's say if you'll start with the completeness, we usually talk about the data pipeline. So in that data architecture, data pipelines basically ensures that all the data is captured and into the required formats and into the required systems. And in terms of accuracy, we usually check that in the data architecture, all the data sets are within the relevant formats and they are in the consistent formats. And there is a concept of data profiling that we usually consider to identify missing points and to ensure that the accuracy is in place in the architecture. When we talk about the consistency, which is the third one in this list, we usually talk about the data models. So data architects usually work on data models to keep this data consistent across different systems. So that this is also relevant to the data integration, which is a kind of pen point for different domains or different systems nowadays. And in terms of timeliness. So I mentioned this point because this is somehow also relevant to pipelines, which we are also called as ELTR ETL pipelines. So we usually use these pipelines in data architecture so that we can reduce the manual processes because most of the processes nowadays are manual. So by using these ELTR ETL processes, we can automate the processes and we can save the time. So in terms of validity. So let's say in terms of data quality, we usually define some kind of rules and constraints just to ensure that the data is maintained and the data is properly validated and updated accordingly. One of the things that has been just has been coming to my mind was like in terms of uniqueness. So when we usually work around the data models, we usually define different kind of relationships and within each component or each record of the data set, we usually prefer to have some kind of unique identifiers. And so the purpose of just like mentioning unique identifiers is to avoid duplication actually. Nowadays we are dealing around a lot of data sets, but duplication is one of the key problems. So to avoid this problem, we usually assign unique identifiers across different data sets of the architect within that architecture. Now how this is relevant to the organizations basically is from data architecture perspective, we work around with different organizations at different data consumers. Firstly, to understand what are they need? So today we are specifically focusing on the data quality. So I will be focusing on what we need to understand about the data quality needs at first step and how we can ensure that the relevant steps in the data architecture are meeting the specific use cases. So these are some of the metrics, but these metrics, the names of these metrics are quite common, but in terms of data architecture, we usually take these kind of steps, actually. So now we talked about the metrics. Actually, this is some of the practical implementation and this is from one of the research groups, which is known as the Infotech. So in this diagram, basically, it shows that there are five key components of the data architecture, which starts from the data creation and then data ingestion and then ends with the reporting and analytics. So in terms of data quality, there are different data checks across these layers, actually. For example, if we see at the first one, we have the databases. So when we walk around the databases, we ensure that the database is free from the errors. And if we come towards the data ingestion, we usually check the different kind of syntax and formats so that the data is ingested in appropriate manner. And the third step is the database, the data warehouses. And nowadays, there is a concept of data lake houses where we actually capture and store the data. So data warehouses and data lake houses themselves, themselves, actually maintain the data quality by working around different kind of data pipelines that they have built in. So in the last, we can also see that how in this data architecture, how we have ingested the different kind of data quality checks to ensure that the data is quality, data is properly collected, then ingested, then properly stored and delivered, and then consumed as the last step. So one and two, as I have shown in the lessons, one basically shows how each component in these diagrams attempting to fix the data quality issues, especially at the root cost level. So when you mentioned about the root cost level, it starts from basically the ingestion from the databases. So that is the first step. And throughout these data flow, actually, we actually usually assign different kind of checkpoints to ensure that we have a good data quality output at the end. So these are some of the common challenges and obstacles. They describe repetitive nowadays, let's say organizations are facing that in terms of data quality, we have unreliable data which leads to unreliable outputs, actually. And then there are inefficiencies in the data sets and to fix those inefficiencies, we have different kind of costs associated with it and cost is another factor that is like a challenge for fixing data quality issues in these data architectures. Now, what is the impact of poor data quality? Because it has a domino effect. So if the first step of the data ingestion is not handled properly, it basically leads to the wrong outcomes or the wrong decision-making at the end of the process in these data architectures. And one of the other obstacles that we have, or I have noticed is like that nowadays, there's a failure to realize the importance of data quality. So we usually do not give much importance to this concept that data quality is like one of the important parts of these data architectures, but they are often ignored nowadays. And also, I think we are not, usually we are unsure about where to start with the data quality. So this uncertainty also poses the challenges to the data architects that from what step we have to start because within all these layers in the data architecture, there are different layers and each layer we come across the different kinds of data sets and how to start in terms of lineage is also a problem. So we also come across this word of data silos nowadays. Actually, this is most repetitive word in data domain and the challenge in terms of data quality and data architecture is that the data is like basically stored in different isolated systems. Other departments and which makes it difficult to ensure that the data is consistent and have a quality or to maintain quality across the organization. Now this kind of data silos also leads to the lack of integration, data integration between the systems and the lack of integration actually results into inconsistent data definitions. And of course it leads to inconsistent quality standards. So these are some of the challenges and common obstacles that have been listed here. So as a potential solution, I have listed a table here. These are kind of very generic and basic solutions which can solve the existing problems. Let's say within these data architectures, what we usually do is let's say, as I shown in the previous diagram, we usually implement data quality checks at every stage of the architecture. Let's say when we talk about the different layers, there are different stages of pipelines in these data architectures. For example, that pipelines basically handles how we have ingested the data, how we have transformed the data, how the data is integrated and how the data is delivered. So by positioning the data quality checks at the different steps of the each layer, we can control or we can improve the data quality aspects in the data architecture. And apart from that, we can also improve the data quality by improving the results that we can usually collect from the data quality checks. Let's say we usually set some kind of thresholds or rules which says this is pass or fail statuses. So these kinds of messages or different quality scores can also help us to analyze the state and performance of the data quality in the data architecture. And yeah, and there are some other aspects that I have actually listed here. So one of the thing is in terms of visualizations also. So instead of manually going through individual component of the data, we usually nowadays work around the dashboards or different tools or charts, which basically shows the different kinds of forecasting and data quality trends to ensure that where are the anomalies and where are the data issues actually exist. So data visualizations are also one of the important roles to maintain a good data quality in this data architectures actually. And the last one, I think it's a kind of collaboration between the different teams. Let's say data architects usually work with the data engineers and data analysts and data scientists within the organization so that they can effectively communicate about the data quality issues and work on the existing challenges are the workflows actually. So I have to support previous statements. I have added an example of an organization that how data quality is like affecting the data architecture. So the example is, let's say, if there is an organization which is like trying to migrate their data from one platform to the other platform. So after the migration, actually they realized that the migration that they have done actually produced some kind of inconsistent results. And those inconsistent results were actually from the data quality issues, which were ignored at the first step. So what actually has gone wrong in this scenario is like that while migration from one platform to the other or from one domain to the other, it is necessary that time should be spent identifying the quality data quality issues. And the other problem that we usually face nowadays is there are a lot of manual processes to fix the data quality issues. And this manual processes usually leads to the high extension of delivery of time. And now to address these issues, let's say within this organization, what they'd usually do is they go back again and include some additional resources and inclusion of additional resources basically also increase the financial burdens and also the associated cost with it. And when we talk about the data quality, let's say in this example, if there are inconsistent results from one platform to the other, then it also initiates mistrust or the issue of the trust in the let's say in the new system, if the results are not up to the mark or what it usually supposed to appear in the next platform actually. Now, if that is the issue, then there are certain solutions also. Let's say this is another diagram which I have listed here. This diagram is simple one which shows what are the data resources and what are the other staging areas which can improve the data quality issues let's say in the previous example from migration from one platform to the other and within the complete data architecture. So the step one, let's say from the data resources and the second step, which is from moving the data from data sources to the staging area, we have actually, we usually let's say maintain different kind of checklists to ensure that the data which has been coming from the actual source have some kind of, it's free from the data quality issues. Now, in this staging area, in the data architectures, we usually have a data pipelines and within these data pipelines, we usually in just different kind of checklists. Let's say this is one of the checklists which is let's say in terms of operational checklists that how the data is operating. So these are the five steps which ensures that the data which is coming as a raw form should have free from errors or that data is free from errors by following these five steps. Now, when it goes from staging area to the data warehouse, we usually, what we usually checks is like whether it meets the business requirements, whether it's consistent with the business needs. So these are the other checks that we usually implement within these data architectures to ensure that the data quality, the integrity and data quality is maintained within these data architectures. So I listed this because there is a lot of discussions around data within the data which is metadata. So what could be the role of metadata quality in the data architecture actually? So how metadata actually quality usually impacts the real time implementation of data architectures. So I listed five of them, but they are not limited to. There could be much more than this one, but some of them is like, we usually talk a lot about nowadays fair and one of the component of the fair is discovery. So what usually metadata usually help is like the high quality metadata actually ensures that the relevant data could be easily find within the data architecture. And in terms of linear striking, so metadata can ensure that data can be properly tracked from its origin from various steps such as transformations and the movement throughout the organization. And in terms of integration, good metadata quality is actually important for successful data integration because when systems and applications use consistent and accurate metadata, it becomes easier to integrate data from various resources. So in terms of data consistency, I usually mentioned here that consistent and standardized metadata access within the data architecture ensures that different platforms or different organizations are using the same data language to maintain the consistency across different systems. It also supports the data governance and compliance because metadata is one of the foundations for effective governance. It basically helps to set the policies or to enforce the different data management policies actually in that architecture. Now, I have given a bit of overview of how data quality is important for the data architecture. Now, we can, in this phase, I will highlight how we can ensure good practices of data architecture and data quality. And for that, actually we have actually developed a checklist which is currently available on the ARDC website. So what we have focused in this checklist actually is that we actually compiled different components of data architecture. So in this checklist, we focused on the 17 areas which I will highlight in the coming slide. Now, what is the benefit for the end users from this checklist is that after using this checklist, actually organizations can have a better understanding of where their data architecture is at and what areas they need to follow up on. One of the things I think I should mention is like that this checklist is intended to be used as a starting point for teams or for the organizations to customize. And all the items in this checklist, it's not necessary that they would be applicable to all organizations and it's likely that organizations will need to air revise or remove the item based on their requirements. But this checklist basically aims to ensure that there are certain elements which needs to be embedded in the data architecture practices and this checklist will initiate further discussions and reflections on how we can improve these existing data architectures. So this is one of the snapshot from the data architecture checklist. So these are the 17 areas which are shown in the bullets. We started from the questions around existing architecture. Then we gone down into the data sources, data formats and data types and data integration and transformation and techniques and how data pipelines and workflows can reduce the existing manual works and then there's a long list of components that we have listed here. So this checklist is currently available on Zenero. I have added URL in this presentation actually. One of the component that we have focused in this checklist is that the first bullet that is shown in this slide is basically a core list of data architecture but within this data architecture, there are three other cross capabilities. Let's say in data architecture, we also our team, let's say we have a team of data governance, sensitive data and fair principles and these three areas are also come across the different components of the data architecture. So I will walk through each component of data architecture checklist in the coming slide. So this is the first one. So as I mentioned previously that these are the different components. Let's say if we started from the existing architecture. So this checklist ensures that let's say do you have an existing data architecture in your organization? If you have, then what are the problems? If the data architecture is outdated, redundant, it's not properly aligned with the business requirements. And what are the current needs? So we actually started by focusing on the existing data architectures and then going into more details of data resources. Let's say from where the data is coming from, how the data is collected. Are there any devices which have been used during the data collection? Are there any social media? So there are different resources that we have focused on here. And in terms of data formats, let's say we actually highlighted what kind of formats are you being just like using in your data architecture? And also we have highlighted some kind of metadata schemers within that checklist actually. Nowadays because there is, nowadays we are in the age of big data. So when we talk about the big data, there is data is usually clustered into three types, structured, semi-structured and unstructured. So we also included this component to ensure that what kind of data types that your organization is using. Now, in terms of transformation techniques, how the data is like moved from one place to another just to make it for analysis-ready. Let's say, have you used some kind of existing techniques such as ETL processes or ELT processes to clean and organize your data? And then we also included the effect of pipelines and workflows just to reduce the manual steps. We usually do in data transformations. And in the data ingestion, because there are two kind of data which is basically ingested into different kind of systems. One is the base streaming and the other is the real-time streaming. So it's important to design the architecture based on how the data is coming into your system. So whether you are using the base streaming or whether you are using the real-time streaming for your objectives actually. So if you are using both base streaming and real-time streaming, and then there is a concept of Lambda architecture which is the best suited for the data architectures. We also included the component of data storage, although ADC is not providing storage at this time, but we focused typically based on the repositories of the data warehouses. Let's say whether your data is stored on-premise, whether it is in cloud or whether your data exists both on-premise and cloud in hybrid situation. One of the key aspect in data architectures is databases. So let's say we have different databases and these databases are dependent on the data types, whether the data is based on the tables or whether the data is based on different kind of documents. So that will decide whether it's relational or non-relational in terms of databases. So in the third one, we actually focused on the data quality aspect and which is quite aligned with this presentation also. Let's say what are the key steps that have been taken to ensure that the data quality is maintained across the data architecture. Let's say one of the technique could be the data profiling and there are some other techniques and which have been discussed in the previous slides, let's say automating the data pipelines to ensure that the data is free from the errors. So in terms of data validation, when the data has been presented in the last stage, it is important to ensure that the data is free from any inconsistency so that the data that has been used in the last stage for decision-making should have a clean and appropriate data. So when we talk about the data decisions, because we talk a lot about ML and AI, so even for ML and AI, they are heavily dependent on the incoming data, so it's also important to maintain the data quality for these ML solutions. And in the last, sorry, and in the last we have visualizations part, let's say nowadays organizations are using business intelligent tools such as Power BI and other tools to visualize the big data to get real-time alerts and predictions of the data sets actually. These are the cross capabilities that usually data architects work with the other members of the team, let's say data governance. Let's say it's the governance that's in place in the architecture, for that we have more details on data governance in our website and our data governance specialists have actually designed those checklists to ensure that the data governance policies are in place actually. And we have also teams on the sensitive data. So when we talk about the data in the data architecture, we come across also across the sensitive data. So for sensitive data in ARDC, we also develop a guidelines around how we can work around the sensitive data and how it can be useful for the data architectures. When we work around the data quality in the data architecture, then we also ensures that the data is like, data follows the fair principles. So to understand how the fair principles are relevant to this component, again, we have a fair principles document in the ARDC websites, which can be easily accessed by actually anyone. And I have also included the hyperlinks in this slide. So that was the checklist. So after that checklist, actually what we realized is that when we talk about the architecture, we mix different kind of technologies within those architectures because there are different kind of architectures. Let's say this figure shows seven to eight different kind of architectures and each architecture has a different kind of role and responsibility. I have listed, let's say, from data architecture what usually they look after, what systems architectures they usually look after, what is the role of infrastructure, architecture, enterprise architecture. So what is the role of ML and AI architecture? So I have listed this figure just to make, to clarify that how we can select the right architecture based on the needs and more details can be found at the ARDC website actually. Now in terms of solutions, how we can improve these data architectures and data quality and data workflows. So we have actually listed a couple of steps here because data architecture needs to keep pace with the technological changes. So these are some of the recommendations to improve the data architecture along with the data quality. Let's say the first step is the, we have to assess the current state of the architecture. And in terms of data quality, we need to understand what are the requirements of data quality and we have to apply or design the data architecture accordingly. And in terms of existing architecture, we need to understand that in which areas we need further improvements and how we can design different strategies and workflows so that the data architecture can be improved based on the current requirements or the current stages. So there is also a concept of a gap analysis where we actually draw a comparison between the current state of the architecture and the future state of the architecture. And within this gap analysis, we also focus on the data quality aspect. And this is one of the important aspect in the data architecture that what is the existing situation or what is the existing state of the data quality and how we can improve this data quality aspect in the future state of the architecture. And I think a proper planning and road may be also important to optimize are to improve the existing data architectures. And a proper planning and a workflow is, I think it's also important to keep the data architecture up to date. And I think one of the other things that has been coming into discussions was like, we need to promote collaboration between the different members of the data actually. If you foster data-driven culture, we can better handle the existing issues because as I mentioned in the previous slides, then nowadays most of the organizations are working as a silo. So if you promote collaboration between different data teams, then we can handle these data issues within the data architecture more effectively. So there are a lot of discussions around how we can use AI and ML for improving data quality in the data architecture. So these are some of the components that I have listed here. So let's say AI can help us in automating the data cleansing and standardization. And we usually do this by automatically impute the missing values, rectify the inconsistencies and bringing the different standardized formats across the architecture. And in terms of anomaly detection, we usually use in machine learning and AI we use supervised and unsupervised learning. So this is important because if an organization is working on a big data, let's say petabytes are a big data, manual processes will usually not work. So by ingesting the AI and ML solutions and by doing some kind of feature engineering, these errors of data quality can be easily identified and rectified. So yeah, so the rest of the ones are like how we can improve the data quality monitoring. Let's say we can improve by ingesting the predictive models and ingesting the data linear striking. And nowadays, data linear striking is usually being done by using the historical activities of the data and usually AI, data science and ML use different kind of feature engineering to detect what are the historical trends and how we can attract these changes for the future. And I think one of the important aspect is like democratizing the data quality. Let's say AI can help us to do this by promoting different kind of data quality dashboards which could be visible to different members of the team. Now, what could be the, how a modern data architecture would be look like and what could be the key features that we have to focus on? These are some of them. It's not necessary that all these will applicable to the different organizations, but these are some of the key items that we have found that these could be the key features in the modern data architectures. And one of them is the scalability. And the other one is the reusability because nowadays there are a lot of data pipelines but they are not reusable. So by introducing scalable and reusable data pipelines we can combine different kind of intelligent workflows and these workflows can help us to improve the data quality issues. And the second one is in terms of data integration because nowadays let's say from here you see where the data is like, let's say it's coming from different or from various sources. It's important to have a seamless data integration let's say by ingesting ELT processes or considering other approaches such as data pipelines and even driven architectures for efficient data integration. Now there we discussed in the previous slides that how it's important to convert the manual data flows into a data flows automation. This is because to reduce the manual data processing and analysis and in terms of technologies, machine learning can help us to automate these processes. Actually let's say one of the use cases machine learning ops where we use different kind of workflows and pipelines to automate these pipelines and reduce the human processes or human labor worlds. So it's also important to consider the batch and real-time processing requirements because some of the data just like in terms of batch comes in terms of batches that they're like not comes on a frequent basis but some of them are real-time. Let's say in terms of IoT devices most of the IoT devices they actually release data in a real-time. So it's important to consider the requirements for batch and real-time processing. So one of the enterprise architect's name is Charlie Raviri actually listed six essential steps using which we can modernize current data architectures with data analytics and AI which I have listed here. Other aspect that I have listed is like our data architecture should support both on-premise data and the cloud data needs so it should be hybrid. And the cost factories I think is there for a long time so we have to balance the cost and the simplicity within these data architectures. And in terms of data storage because scalability is I think it's a problem for most of the organization. So we can adopt a new technology such as data lakes or data lake houses to address the scalability needs actually. Now from where from here is what's next from here is like the guides are the checklist that we have developed the starting points and we are welcoming our community to have a look at on this checklist and guides for continuous improvements and we will regularly review and update this checklist so that we will remain, we can remain update with the evolving data landscapes or the evolving data landscape challenges. So if I will summarize these and five key points the conclusion is that we can prioritize data quality in data architectures and by doing this we can avoid the negative effects or domino effects within the data architecture so that the data driven solutions should lead to this success not to the disaster. Nowadays I think it's the important thing is it's not just about defense algorithms and impressive reports. I think it's also about building trust through reliable and accurate insights and to develop these I think data quality is one of the important factor. And in terms of the checklist these checklists can definitely improve the data quality but it's not a magic bullet actually. A checklist basically ensures that the key aspects and the key ingredients of the data architectures are in place and considered and they're addressed in a systematic manner and by including these kind of checklist organizations can improve and maintain their existing architectures and also the key components such as the data quality. The last one I think which is important one is like every organization has unique architecture that basically reflects their infrastructure, their culture and their business requirements. However, I think in every architecture there are key components are the key elements that we actually try to capture in the developed checklist actually. So these are some of the highlights from my side. So thank you once again for listening and yeah, that's it from my side.


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He's playing zebra bass, boy. Dude, dude, that was a freaking, like, 10 pounder and a freaking two pounder on bed right there. There we go. All right, these fish are not wanting to bite. But I know exactly how to catch them. I totally forgot to have them with me. I got this little tiny Texas rig. And that's what it's gonna take. I got this chatter-donk on right here.  It's super windy. You guys can't really hear it on this pond, but super windy outside. The weather drop was just crazy. So 30 degree drop from yesterday. These fish are a little finicky, but I'm actually seeing a good bit of them up shallow. I haven't seen any giants yet. I see a lot of bass cruising. And it's actually kind of hard to see them because the clouds are up right now. But I'll tell you, the best way to catch them is definitely to tie on this Texas rig and flip in front of them because a couple of them have seemed pretty active. And I think I can give them the bite. So I'm gonna put on a quarter ounce of blue weight with this three-odd extra wide gap hook with a little speed worm on it. And we're gonna go around and just pitch and flip around a lot of these pads and just kind of keep our eyes open and watching around up shallow because I've seen so many bass. And I believe that they're gonna eat this thing. Let's get started, guys, see what we can do. I don't know if you guys can see this, but all the beds that I've seen are huge. There's some of the biggest beds I've ever seen on a pond. And I was thinking about it, I'm like, it almost looks like it's not even a bass bed. And I started to think deeper and deeper and deeper. I'm like, well, we are in Florida and there's a lot of tilapia. And those are tilapia that have taken over those old bass beds and they've just made them just frickin' huge, man.  The thing is almost three foot deep right there. This is just crazy though. You don't see bass beds like that. I got one, no way. He spit it out, he spit it out. The speed warning might be the trick, guys, gosh. I thought that was a bite when I first threw in there. I first casted out in the middle in these pads, guys. That was a fish that was biting me. He was just walking with me. Man, stick him or anything, he should have eaten it. Yeah, he just slammed me.  Okay, we're getting bites, that's all that matters. Sorry about the wind too, we're like kinda hidden right here but I'm telling you, it is whipping today. It is pretty crazy. Honestly, guys, I think we're gonna frickin' smoke him on the speed growl. Or the speed, the speed worm. Not a bass right there. No. Oh, oh my God, it was. Okay. What? What? Okay, this fish is not big and this is why they were not eating that chatter-donk. I hope they're not all this size. I've seen a lot more bigger than this, okay? Let's just start off the video with a little bass. Amen, you cannot complain. That was pretty cool. So that thing I saw in the water actually wasn't a fish but as I started to reel it out of the water this fish came out of nowhere and then I just pitched it in there and he just didn't even hesitate. Little guy wants to be a big one but thank you for buying, buddy. There's a lot of fish in this small pond. We just gotta find the big ones. That's a good sign after a cold front. I know he was super small but just to see that some fish are swimming around and you can see that one ate it that quick. I mean, shoot, fish must be all right today. I'll tell you, any other day, guys, I would be throwing a ding frog out here. He got all these lily pads, shallow water, grassy. You know, this place does not scream a frog. I don't know what place screams a frog. This is some frog fish in territory. The moment I pulled up here, I was like, dude, poppin' frog. Yeah, that's a better one. Not a big one by any means but he's freaking. Whoa, what is that? Oh, it's a tilapia. I'll say that fish is three times the size of this thing. Okay, he's not big, but we're catching him. We're finding something. We're finding some bass, that's all that matters.  He's bigger than the last one. He's not big, but I appreciate you, Biden, bud. We gotta start somewhere, guys. We are figuring him out today, trying to see what they want. You know, us switching up to this worm was obviously a great choice and we're getting bites now. So now we just gotta hook a big one. By the way, if you guys wanna check out the kick in the bass TV X Luz combos, they're only available on my website, kickintheirbass.com. I have the link in the description. Got right hand bait casters left hand and awesome spinning combos. One of the most versatile rods out there, you know, it's a seven foot medium heavy. You can throw just about anything on it. And for a lot of you guys that are bank fishing and overall just palm fishing going around and throwing whatever, it's a great combo for that, so. Ooh, dude, that bite was so good. These fish are so small, man, but they're just slammin' it. There's so many bass in these pads out here. We are definitely probably gonna have to go find a new pond if they're gonna all be this size, but I don't mind catching a few while we're here. Wouldn't be nice if you were pulling five pounders out of pads like that.  Like, I'll just flip it on the edge of that pad and I'll kinda, oh my gosh, I really don't think I had a bite there. I don't think I was just thinking I had a bite.  You'll just feel one hard, you'll feel one hard thump and then you just run it off with it. Dude, fish is blue. He's blowing up on my line right now. Once I get my worm, yep. Oh no! I was about to say once I get my worm right there he's gonna slam it and sure enough, he did. That was a bigger one. You can't tell me that wasn't a good fish unless it was a tilapia. There's a bass on bed right there, boys. He's actually on bed. We're gonna catch him, all right? See, that fish was not locked on bed. You just skirt it all. Come on, I'll put it right back where you want it. All right there, buddy, come on. Now the wind's gonna make it to where I can't see. Okay. There's bass in here. Hopefully there's some big ones. It is so windy. Going back to the moving bait. Going back to the chatter-donk. We caught a plenty of small fish. I know the small ones are gonna try to tear up this worm no matter where we go. Don't get me wrong, get to catch big ones, but if I really wanna cover water, hit a bunch of different places and just figure out where these big fish are, I just really feel like this chatter-donk is our best bet. Oh my goodness, that's a freaking monster, guys. Oh my God. I'm over here seeing bass about this big, thinking, man, there ain't nothing in here. Roll up on like a seven pound or just, I don't know what he was, oh my God, guys. Dude, this thing is huge, guys. There's no way this bass is on bed, right? Hold up. Where did he go?   And I wish I was paying attention because I probably could have caught him before walking up on him. I mean, he was just sitting here, which is crazy to me. I wonder if he's like the king of the castle. Do you think he's the king of the pond or do you think that there's another bass in here that's competition with him or even bigger? I don't know. I wanna say there's probably a lot of good fish in here, but it's just hard to tell, man. Guys, I think I need to put on that Texas rig. Something's telling me I need to put it on my face. What is that thing, dude? It's not freaking easy. What is that? It's not a tilapia, he just slamped. Bro, what in the mess is that thing? It's striped, guys, it's on freaking bed.  Bro, what is that? Look at how aggressive he is, bro. Gosh. What is this thing? It's a thing, zebra bass, boy. Bro, I wish you guys could see what's happening.  Dog, I gotta get this on film somehow. I wish I had somebody with me. Bro, I like gotta catch this thing just to show you all this zebra bass, dude. It's the most aggressive thing I've ever seen.  I don't even know how to get this sucker off the hook, but we gonna figure it out today. Yo, I just freaking, why is it, you look so much different in the water, buddy. It is a tilapia. You were the most aggressive fish I've ever caught, probably like, I'm impressed by you. I'm so impressed. That was more fun than catching a bass on bed. That was insane. Got one looking at me. All right, he's laying right where you go to sleep, buddy. Oh, I know it's tempting. Uh-oh, yeah, you see that little wiggle, wiggle action. Yeah, come on now. You can do it. Listen, don't bite. Just takes one second. I was gonna get him. To be honest, I wanna find another.  All right, y'all, I think we're gonna go to a new spot. It was cool to see some big fish today, but I just don't think we're gonna have an opportunity of those, maybe we can catch them some other time, but today I just don't think it's gonna happen. We need to go to a new spot and see if we can find some different fish. All right, y'all, we're at a new little place. No, it's not, it's still a freaking small one. I'm about to say I finally got one that's like a pound and a half, two pounds, but no. Hey, I'm just glad we got a fish right there. He freaking hammered it. Right there on the edge of this grass, I'm like trying to figure out this place. I'm just like looking in the water, it's very interesting, but that's our first bass in this pond. Very interesting place. It looks really good though, but I can tell you like some of the best places of the fish I can't really get to. But yeah, I'm not throwing way up on the banks.  And that's exactly where I got that fish to bite. And I wanna see that's a bed right there. So that kind of keyed me to think, I'm like about five foot off the bank. You know, there might be some beds and bassin in that grass right there. So, see if we can find some more. I don't know if that was just a lucky little cast right there, but that was my first like real cast on this bank. Oh, that's a better one. Let's go. Okay. He ate it as I was reeling it, guys. Look at that one. Okay, not a big one, but that is better than what we've been catching. That's freaking sweet. I'm actually standing on top of this stump right here. I was like starting to see in the water and got him to bite as I was reeling in. That's pretty cool. Right above that grass, I was just taking it with that worm. Maybe that's a good way to catch them today. I was thinking about putting on that chatterdunk, but I don't know, I might just stick to that worm they're eating it. All right, y'all. It is the next day. And we're gonna see if we can catch some fish. I actually just pulled off at a random spot. I've never even fished in this area before. I guess we're gonna find out. I don't know if there's any fish in here. I don't know anything about this place. We're actually gonna start off with the worm and just kind of cover some water with it and work it real thoroughly. Might need to put on the chatterdunk. Not gonna lie. But for right now, since I already have a tide on, I'm gonna throw it for a second. See if we can get some bites on it. And if we need to tie on the chatterdunk, that's what we'll do. There's actually some bass beds right here. I'll see them down this bank. I might wanna put on that chatterdunk, because it is a little flat on this bank. If I was seeing some fish pulled up super shallow, probably be a good bet for me to fill this worm, but I might need to tie on that chatterdunk. But we'll see. Let's throw it around for a second. I think I'm gonna do it sooner than later. Since it's telling me I need to put this on and cover some water.  I don't know anything about this place. I don't know how it lays out. Like to really fill that bottom of this chatterdunk and figure out how I can cover water in here and figure out the best way to like turn these fish on and start eating. Not a bad one. Let's go guys. He's not fighting at all. Yeah, he ain't big, but I'll take him. Little guy that ate chatterdunk. All right, we're just gonna release you like that, buddy. There you guys. All right, not a big one, but we caught something. Definitely some fish in here guys, I just don't think they're that big. Feel this cast right here. Oh, right as I said, I'd say it looked pretty dang good. You might bite it again, I didn't even stick them. That's a better one. Let's go, dude. Yes, yes, baby. Look at that right there. Not a big one, but a two pounder. I'll take them. Took you two times, buddy, but you ate it. But you ate it, you've been caught before too. I see a little hook mark in your mouth.  He's been caught a few times. Pretty little fish right here. Not a big one by any means, but hey man, that's not a bad one. I do not mind starting off the day with something like this. I mean, technically they're a second fish, but nice. Definitely probably the biggest one for today's video. That right there just gave me confidence that there's a lot bigger fish in this pond. He was a little skinny, you could tell. But I'll tell you, there's 100% some bigger fish in this pond. Would not surprise me if we caught a four or five pounder. There's a little metal drain like that. No, there's one up here. He ate it right away. That's pretty crazy. I clicked my dang reel, he's already on there, guys. He is not big, but that was pretty crazy. I go to where I couldn't get these dang things to eat it. The way he choked it like that and I didn't even really, that's pretty crazy.  Thank you, bud. You go through a lot of those when you're trying to find these places, guys. You hit random spots, you know, it's bound half, and you're catching a lot smaller guys. That's just how it is. That's not a bad one. Yeah, these fish got a big mouth, skinny body. Look how skinny this fish is, guys. This fish aren't too healthy in here.  Yeah, they're not too healthy, especially in springtime right here. I mean, he could be spawned out, but. All right, boys, this pond does not have much potential. I'm glad that we caught what we did out of these two.  Freakin' mega grass carp. I was just hoping he would eat it.  I just always do that, I don't know why. All right, he's not that big, he's a good one. That was my fault. I should have just got him on the bank. He's right on the seawall. I mean, he wasn't big, but he wasn't a bad fish. He was definitely one of the biggest fish that we caught this episode.  That was a freaking like 10 pounder and a freaking two pounder on bed right there. Oh my goodness. Dude, that bass, I thought it was a freaking carp. Oh my gosh, guys, there might have been three fish right there. I don't know. That one was so big.  Boys, boys. Those boils right there, that ain't no two, three, four pound bass, okay? And the crazy part is there's 10 pounders in here. You know why? I fish these ponds a lot.  Dude, I thought he just ate me. Gosh, that's a big one. Oh my goodness, what happened? He just slammed it in, freaking, what is going on? He literally swam back after. I think there's so many fish. I just saw two different beds right there, but then I think that one is eight and he's a different one. I actually see one right here. I'm gonna catch, he's only about two pounds. I'm just gonna snack him up real quick. Cannot believe this. We found some bigs on there right now. Oh, there's a big in the distance too. I don't want to catch him first though. Gosh, that's not freaking. I might, dude, that's the one. I might just need to go for it. Yeah, we're gonna go for it. One of them's gotta eat it, right? Is that just playing with it? All right, we're gonna come, that's a small one. It's a male. I mean, it's a two pounder.  Oh my goodness.  Look at that. That is a healthy, freaking stud right there. Thank you for buying, buddy. I'm gonna get you right back in the water. You better go tell him to bite.    I will catch you guys on the next episode where we're gonna go after these big fish on bed with this worm right here. And I promise you, we're gonna catch some freaking monsters in this episode. Make sure you guys are subscribed with post notifications on. Hit the like button if you guys enjoyed the video thus far. And I'll catch you guys in the episode where we catch these big ones. I literally see one every bit of eight to 10 pounds right in front of me. I'll catch you guys in the next episode. Guys, we got three bass in front of us. One that's about eight pounds, one that's about five and we have about a three pounder. I'm literally looking at all three of them now. Oh my God, they just boiled like crazy. One just hit me. I got one.