 Hello and welcome. My name is Shannon Kemp and I am the executive editor of Data Diversity. We'd like to thank you for joining this Data Diversity webinar, analyzing billions of data rows with Alteryx, Amazon Redshift and Tableau sponsored today by Alteryx. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we'll be collecting them via the Q&A in the bottom right-hand corner of your screen. If you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag Data Diversity. If you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the upper right for that feature. As always, we will send a follow-up email within two business days containing links to the slides, the recording of this session and any additional information requested throughout the webinar. Now, let me turn it over to our moderator for today, Roman Kaler, the Alliance Marketing Manager at Alteryx. Roman, hello and welcome. Thank you. Hello, everyone, and thank you for joining us today as we present Analyzing Billions of Data Rows with Amazon Redshift, Alteryx, and Tableau. Today's presenters are Adrien Moon, Brandon Javez, and myself, Roman Kaler. Adrien is an amazing analyst turned analytics consultant for RxP services. It's worth noting that Adrien is also an Alteryx analytics certified expert, otherwise known as an ace. We'll also be joined by Brandon Javez, who is with our webinar co-sponsor, Amazon Web Services. Brandon is a cloud artisan for the Amazon Partner Network with a focus on handcrafted, small batch, sustainable, and organic cloud architectures. And finally, I'm Roman Kaler, and I'm a part of the Alliance Marketing Team with Alteryx. And with that, let's go ahead and begin. This is a leading platform for self-service data analytics. It provides analysts with a unique ability to easily prep, blend, and analyze all their data using a repeatable workflow, then deploying your analytics app scale for deeper insights in hours, not weeks. It enable analysts to access, prep, and blend all of their relevant data for the analytics. It doesn't matter where that data is actually stored. It could be anywhere from the likes of Hadoop, Redshift, or Excel. With Alteryx, you can also enrich your internal data with leading third-party data for additional insights and help creating the perfect data sets for your needs. Once you have your ideal data set, use the same workflow to perform your analytics, predictive, statistical, and spatial. Our predictive capabilities are based on the R language, but we've created drag-and-drop tools so that note coding is actually required. And our spatial analytics tools utilize data from TomTom to do sophisticated location or geospatial analysis. Once you run your analysis in Alteryx, we make it easy to enable reporting, output data for visualization, or create analytic apps that allow business decision makers to customize and run their own analytics without having you create custom reports each and every time. All of this is done again with one repeatable workflow. Okay, so where does Alteryx fit into the data integration market? On the right, you see the rapidly growing data prep and blending market led by Alteryx. Gartner correctly called out this market as the place where the majority of spending for business user data integration is taking place today. Data analysts appreciate the ability of Alteryx to allow them to prep and blend whatever data they want to use in their analysis. They're no longer limited to the data that IT is able to pull for them. Once this is done, Alteryx uses the same workflow for analytics and delivers deeper insights in hours, not the weeks that are typically required with the other approaches that you see here. And with that, I'd like to hand it off to Brandon. Brandon? Great, thank you. Hey everyone, I'm Brandon Chavez. I'm a Solutions Architect with Amazon Web Services. And I work in the Amazon Partner Network to work with our technology partners to build solutions on top of the AWS platform. So if I can change the slide here. Great. I'd like to talk a little bit about Amazon Redshift. So let's answer the question, what is Amazon Redshift? And Redshift is our fast, fully managed and petabyte scale data warehouse service. And Redshift makes it simple and cost effective to analyze all of your data. And it can plug right into your existing business intelligence tools. So Redshift is optimized for data sets that range from a few hundred gigabytes to a petabyte or more. And it's priced at less than $1,000 per terabyte per year. So we find that that's about a tenth of the cost of most traditional data warehousing solutions. So to summarize, it's a fast and cost effective platform and you can launch a cluster in just a few minutes, right? And so you can also scale it up to meet your needs with just the push of a button. And so Redshift is not only just cost effective, but it's also easy to use. So right up front, provisioning a cluster takes about 15 minutes. There's no upfront cost to get started, like all of Amazon Web services, but you just pay hourly as you use it. And a huge benefit of Redshift is that it manages the work needed to set up, operate, and scale a data warehouse. And so that includes things from provisioning infrastructure capacity to automating kind of routine ongoing administrative tasks. So things like backups and patching. On top of that, Redshift automatically monitors the nodes in your cluster and the drives in the nodes and helps you recover from failures. When you look at the hourly price for Redshift, keep in mind that that's for a single Redshift node, and the annual cost per terabyte is if you ran the node nonstop for a whole year. Redshift also doesn't charge for the leader node. So your cost is determined by the number of nodes running per hour in your cluster. Our smallest SSD node lets you get started for about 25 cents an hour. So I'm talking about things like nodes and clusters. It probably helps to dig in a little bit into the architecture on the back end. So Redshift is a columnar, massively parallel processing data warehouse, and it's designed to be run as a clustered system. To interface with this cluster use, we present the Postgres protocol over standard connectors like JDBC and ODBC. So that allows you to connect your SQL client or business intelligence tools directly to the cluster. The leader node, as I mentioned, is basically your SQL endpoint. And that leader node is basically responsible for storing metadata, coordinating query execution, and serving as an endpoint for the rest of the cluster. The actual data, though, is stored across the compute nodes in the cluster. And the queries on your data are executed in parallel across these compute nodes. So compute nodes can have data loaded to them in parallel from a variety of sources, and that includes AWS services you're used to already. So Amazon S3 or DynamoDB or Elastic MapReduce. And we provide a copy command that natively interfaces with these sources. You can also store backups of data to S3 in parallel. And you can do that very quickly because the nodes in the cluster communicate with each other and S3 over a 10 gigahertz interface. There's also two different options for the hardware platforms underneath your cluster, and they meet slightly different use cases. So you can have a cluster that's built on top of either dense storage, which is the DS platform nodes or dense compute nodes, which is DC. And so dense storage, as it implies with its name, the nodes allow you to create a very large data warehouse using traditional magnetic hard drives, hard disk drives, for a very, very low price point. Alternatively, the dense compute nodes allow you to create a very high performance data warehouse using a lot of fast CPU, large amounts of RAM, and solid state disks. So they're priced a little bit differently, but it depends on what your use case is and how you want to interface with your data. And so Redshift can provide this data warehouse and functionality at a price and performance point that we see work for a variety of verticals and use cases. So if you're a traditional enterprise, you're running your own data warehouse, moving to Redshift can help save you money and add a lot of flexibility. If you're a company with big data, Redshift can make it a lot easier to start a data warehouse and make all of that data available to analyze. And last, Redshift is a great base platform for software as a service offerings, and it can add analytic functionality to any of your applications. Another reason that Redshift is so suitable for such a variety of verticals and use cases is that really there's several ways to manage and interact with your clusters. So if you prefer a more interactive way of managing clusters, you can use the Amazon Redshift console or the AWS CLI, the command line interface, or if you're an application developer, you can use the Redshift query API or one of the AWS SDKs in the language of your choosing to manage clusters programmatically. And so data warehouses in the traditional sense, in contrast to Redshift, they were very expensive, they were inflexible. We found that they required significant expertise to implement and operate, and they were really restricted for the most part only to very large companies that could afford to invest resources, both capital and human resources, into them. They required large teams in order just to manage these data warehouse implementations. And so our customers had a lot of challenges with this. So that's what led us to build Amazon Redshift. But we find that companies of all size really have data that when loaded into Redshift and analyzed, it can provide them insights and critical business information, so it really allows you to be agile and find meaningful data no matter the size of your company. And this enables you to use your data to acquire new insights through your business and your customers. So regardless of the size of the data set or the company, Redshift really offers fast query performance using the same SQL tools and business intelligence applications that you've used today. So we encourage you to take a look at it and see if it can help your business in a way that allows you to provide additional insights for your customers. So that's my overview of Amazon Redshift. I'm actually going to pass it off to Adrienne here to continue talking. And while you do that, Brandon, I want to say thank you, and we had one quick question come in for you. Is Redshift an in-memory data processing architecture? Oops, sorry, I was muted. Maybe I'd like to talk about that kind of offline. So it's essentially a, so it's a columnar database. I would want to ask a little bit more about that question, I think, so maybe I can sync up offline afterwards during the Q&A if that's okay. Perfect. Thank you. Good morning, everyone. Adrienne here. Before we begin, just a little bit of a disclaimer. All data here that's used for the presentation is just for sample data, and it's really to demonstrate capability of AWS, L-Tricks, and Tableau. No actual Mason data will be shared. I'll take to cover four key areas in my presentation today. A quick introduction, some of the business challenges that are faced, using best of brief technologies, and finally, sharing with you some of the key learnings that are actually encountered. Amesin is Australia's award-winning low-cost mobile provider, operating on the Optus network. We're dedicated to delivering simplicity, fairness, and low prices. Currently, Amesin is one of the largest mobile virtual network operators with over 700,000 customers, and one of the few telcos in Australia with a net promoter score in the 50s and 60s. In addition, we've also won numerous Money Magazine Best of the Best awards. As for me, up till last month, I was the analytics manager for Amesin and was responsible for analytics, strategy, and execution across all areas, including finance, sales, marketing, and HR. My expertise is really about decision support and management consulting and how do you actually use technology and visualization to bring out, to improve business stakeholder engagement and try key decision-making. At Amesin, we have a bold vision for analytics. It's not about how much data you have, it's about how you actually get insight out of it in that timely, continuous manner. I personally find that AWS, Altrix, and Tableau enable us to ask and answer questions in that continuous, iterative flow, which is so important to the business. Every question that's being asked leads to more questions. The living at Dashboard is not the end, rather it's the beginning. How Altrix, Workflows, and Tableau Dashboards are continually refined to help answer more questions in the business. Most importantly, we believe the tools we have combined with our journey is what gives us that competitive edge. So many of you will ask, why have our analytics program? Well, we've seen benefits in three main areas. First of which is workforce productivity. By enabling line of business people to quickly build on a baseline of analytics, they can solve their own specific business problems quickly and do not have to wait on the BI team. The second of which is really reduced time to insight. With Altrix, instead of just gathering requirements, means they ask a question, what's the business problem we're trying to solve? Altrix enables us to get to that data faster. Projects that take two to three weeks are now down to a day. Finally, data-driven decision making. People can look at this data, do some discovery, and really arrive at answering themselves, solving their own problems faster. So I'd like to now cover a second important area about business challenges and how we actually overcome them. So Amazin has a wide variety of data sources, and a lot of data is being generated daily. As you can see on the left, we ingest data, you know, XML data, GSOM data. We've got source systems and different databases as well. We have over 10 billion rows of data with nearly 20 million rows at a daily. Every time a call is made, an SMS is sent, or internet traffic is being used, a row of data is generated. Competition is really intense as well in the mobile sector, where we compete for business against the three major carriers, as well as a whole host of other mobile version and mobile operators. In addition, we've got a small team of analysts that serves a really wide audience, from retail sales to finance, from customer acquisition to retention and HR. I personally find that traditional tools just can't keep up with that speed and velocity of business demand. It's tools like Ritchif, Eldritch, and Temple that enable all of us to become analysts and enable analysis to be delivered to the business at the speed of thought. So I'd like to quickly cover the best of great solutions that were selected as well, and hopefully share some key learnings. This is our BI stack at Amazin. On the left is a typical source system. You know, we've got multiple source systems coming in, and on the right is our visualizer. With AWS, it reaches sitting right in the middle and Eldritch sitting in between the visualizer and the data warehouse. So the other question that a lot of people ask is, why do you actually need IT, ETL, and data planning capabilities? Why do you actually need Eldritch? Well, I personally find that it's really the speed and agility that Eldritch gives us allows anyone to answer questions in minutes, rather than actually spending his time writing requirements and cutting code. It really enables anyone in the business to become an analyst, and it's not limited to the BIT. In doing so, we really lift the productivity of the entire company by enabling people in the business to effectively ask and answer their own questions. This is how we're actually using Eldritch, Tableau, and Richard at Amazin. The first of which is, Tableau can be used to directly connect and visualize big data directly from Richard for any other data source. But with Eldritch, we find that Eldritch is really good at blending data that's not in the Richard database with data that's in the database as well. And you can validate it and also apply very complex business rules before visualizing on Tableau. It's also really important to note that it's not just a one-way stream. You can see that with Eldritch in the middle, it's really important that you continuously iterate between the two tools when you're discovering more data. What it really enables us to do is iterate in a matter of minutes. Here's another look on the setup of how we pull in different data sources, some of which are a bit more complex than normal. For example, XML and JSON, and combine that with data from our databases before visualizing in Tableau. Our architecture as well, in terms of server architecture, the workstations are all completely in the AWS cloud. So we know that AWS is really, really scalable when we're analyzing more data. Finally, we use both Tableau Server and Desktop as a collaboration mechanism to deliver a variety of dashboards, data sources, and most importantly, insight to our business leaders. Our internal Tableau Dash server has several key areas from an exact dashboard and reporting to finance reporting and marketing reporting. I'd like to now share with you some of the key learnings that I have acquired over the last couple of years, and hopefully you can get some value out of that. So this slide says it all. How many business leaders look like that? Analytics is really for everyone. It's not just the IT of the I-specialists. Remember, you don't have to be a specialist to use tools for metrics at Tableau. It's all about democratizing that opportunity. Most business leaders really want to combine analytics with their own industry experience to make better decisions. The best way I find when you showcase a tool like weather, the outreach for analytics at Tableau is to really sit down one-on-one with a business leader and show them how you can use analytics to combine data and visualize in Tableau, and then you can make that business recommendation. And best of all, you can do that in a matter of minutes. Often you can build a dashboard in less than five to ten minutes, and that way you can actually test your ideas with the business stakeholder in that dynamic flowing manner. Another key point to note when enabling business leaders is really to take the time and make it relevant to the different people. Senior management is more interested in insights and outcomes. Spend time with them. Show them how the dashboards are built and what opportunities there are to improve business performance, whether it's through revenue generation or cost reduction. Functional leaders are, however, more interested in a narrower field where they want to spot trends that can improve operational performance. For example, HR leaders want to see metrics around staff turnover at the absentee zone, if it cools. Front-line leaders want to see metrics around how many calls are handled in the call center. Sales want to see metrics around how many sales occurring in each store. Finally, it's really important to remember that to celebrate success and keep iterating. When we started at Mason, BI was suddenly non-scalable. It required analysts to actually have coding experience. So there were a few disparate analysts using SQL to deliver analytical solutions. Business leaders and users were not empowered to analyze their own business. Where we are today, we actually combine Altrix, Tableau, Wood, Bridges to deliver insights, not just dashboards, to management. We're always looking for ways to actually improve their financial performance. Each time we use the tools like Altrix and Tableau and to build new dashboards, we don't just send people a link. We take the time to actually sit down one-on-one with them and explain to them what trends we're seeing, both positive and negative, and what we actually think can be used to improve business performance. It's really this personalized approach that I believe is the key ingredient to success. The other interesting point is when you get a group of cross-functional leaders for half an hour to actually click through and see what trends they might find in the data, that's really about empowering and getting different people together. There's no single one person actually has the full answer. And where we will be in the future is really slice and dice with real-time P&Ls using color data sources, as well as embarking on the journey for predictive analytics to look at churn. I hope you find this presentation inspiring and really will help you to build that creative analytics culture within your organization that's both empowering and satisfying and bringing out the best in people. They really treat key takeaways that I hope you get from this presentation. It's all about democratizing that opportunity, making it relevant to different stakeholders, spend time with them, break down those silos and organizations, and finally, celebrate success. Keep people involved and energized. So that's it from me. I'll now hand it back to Roman. Thank you, Adrienne, and thank you, Brandon. We have a number of questions that I'd like to address, and the first is, Adrienne, what are the additional benefits that Altrix offers other than a standard ETL framework like Informatica? I think the key thing about Altrix is that it actually can do spatial and predictive analytics. ETL is just there to actually enable business leaders to actually get things in to begin with, but it's really built as an advanced analytics platform and tool. So in terms of the predictive and spatial analytics, I think the most powerful thing about Altrix is you don't actually need to tab a lot of code. There are over 30 predictive models built already natively in Altrix where any business analyst essentially doesn't need a PhD. They can just drag and drop it in and the predictive model will be built. That way you can actually focus on the advice you're trying to give to the business rather than struggling with trying to actually create a model. I think that's the biggest win in terms of the Altrix capability. Fantastic. And I know you and I have had several conversations about this offline, but I know you also said that in your role as an analytics consultant, you've often come across this misconception that Altrix is just another ETL provider, but that's definitely not the case. Do you have any specific examples you could bring up in terms of this is a perfect example of how Altrix goes beyond ETL? So one of the examples of using Altrix beyond ETL is that Altrix actually has spatial analytics built in. So if anyone wants to work out how far a storage from the other by trade area radius is essentially really simple to do. Any business analyst can actually do it. It's about four to five clicks. There's a lot of the concepts actually applied to a variety of measures. So it makes anyone capable of using this particular tool. It's really interesting to see in a MISIM where a finance planning manager can actually get into the tool and actually analyze his P&L himself with just a little bit of guidance from me initially. I think that's really an empowering thing for the business. Fantastic. Thank you. And again, Adrienne, how long did it take you before you felt comfortable and productive with Altrix as well as your use of AWS Redshift? And to add that as Tableau as well, I know that at MISIM you were using that particular BI stack. So what was the learning curve like? I think the learning curve, I found it to be pretty gentle. And the reason was I simply downloaded Altrix product, did a few online things, you know, watched a few YouTube videos. And within a couple of hours I was becoming productive. Obviously to master the tools take a long time. But I think most people actually get the value in that usual 80-20 route where I think you spend a couple of hours in it, you do a bit of analysis, you actually implement the tool for your particular use case. And I really believe that it's one of those things where it gives you that Google moment where almost anyone can do it. And that's the point. The penalty for getting it wrong is just typing it in Google and doing it again. Similarly in Altrix, the penalty for getting it wrong is just do another workflow. It'll only take you a few minutes to drag and drop things in again. So the other interesting thing about Altrix I find is because it's actually very visual in nature. So you can actually stand up in front of very senior stakeholders and explain to them in English the business rules that you apply. So traditionally if you have done that in code, it's very hard to explain a few thousand lines of code to a business stakeholder. But now there's full transparency and visibility into numbers and the rules applied. And I think that's one of the other powerful things about Altrix. Fantastic. Thank you. And what can Altrix offer that other BI tools such as Cognos can't? So I can take that question. In regards to what we can offer, like Adrienne said, we have the advanced analytics that we offer as well as the ease of use, especially with a drag and drop user interface. As well as the ability to place that power in the analyst hand. So there is no need to know SQL or have IT run these queries for you. As a business analyst, you can actually do this yourself. And moreover, you can blend numerous data sources together. So that's actually a great difference as well. And Adrienne, I don't know if you have anything to add to that, but have you experienced how Altrix differs from other BI tools such as Cognos in particular? Yeah, I've used both tools in the past, and I've used Cognos as well. And what I generally find is that Altrix is really geared towards business people. So if you have to use a tool like Cognos, typically you would have to have the IT depth resource in place to get it done or BI resource to get it done. As opposed to Altrix, a normal business user, you spend a couple of hours with them and just show them the basics. Most of them are pre-productive after that. You know, it's always really important to explain things in very simple layman's terms. So I think that's where Altrix really excels. Thank you. And you mentioned virtual desktops in AWS. From a security standpoint, does that mean that once the data is in the secure cloud platform, it does not leave that secure environment? And perhaps we can start with Brandon for that, and Adrienne, you can add your thoughts as well. Brandon, you are on mute, so perhaps I'll have Adrienne chime in until we can get you off mute and go from there. So Adrienne, what are your thoughts on that? Sure, I think all our data is held in AWS in a secure environment, secure way. So we're pretty comfortable with that. You know, there's a VPC in AWS where all our data just stays there. So essentially, you know, it's much more secure that way, and no one can actually, it's authorized to pull data down or create flat outs. Everything actually stays within AWS, within that secure, controlled environment. Yeah, that's correct. I think the best way to describe that is that once you upload data to Amazon, especially when it's S3, for example, that data is controlled by you and it stays exactly where you put it until you decide to move it. And so for S3, again, using that as an example, there's a few different options on how you can encrypt data at rest. You can also use HTTPS for data encryption in transit. And that is a common place for holding all of your Redshift data initially before you load it into the cluster. You can also control access to the cluster using things like security groups or ACLs, or you can lock down access to the particular subnet that the Redshift cluster lives in. So in general, yes, the security posture for Redshift and S3 and the whole ecosystem that provides this data warehousing functionality is very secure and you can probably find a combination of features and security options that will meet your companies for compliance regulation requirements. Perfect. Thank you. And we have another question as well as greetings from Brazil. So hello to our international attendees today as well. The question is, is it possible to use in-database features of all checks to process data inside of AWS? Ben, can you speak to that? The short answer is yes. Definitely something that we can do. Yeah, I think so. Maybe to answer that at a higher level, Altrix is basically an overlay on top of Redshift if that's the right way to describe it. So Redshift provides some of the underlying data warehousing functionality and then there's a number of ways to interface with Redshift, Altrix being one of them in this case, to allow you to extract data in a useful and meaningful way for you. So you could write raw SQL queries against your Redshift database, but sometimes that's not the most effective way of doing that. And, of course, it's not the prettiest way, right, because you have to view it through some sort of SQL interface. But Redshift is basically the underlying functionality, kind of the back end for a lot of these different methods of interfacing with that data, and Altrix is one of them in this case. Yeah, just to quickly jump in as well, yeah, Netflix does support in-database for Redshift in particular. So I've actually used it that way as well. And we found that it's much more efficient because you're actually pushing it down to the database. Thank you both. And Adrienne, how can I drive an analytics culture at my company? What were the bigger pickups you encountered and what had you overcome them? I think the first of which is spending time with people and really understand where they're coming from. Most people in the business actually have a day job, so try to understand the challenges and the questions that they're trying to answer. And just spend time with them. Use the tools for what they are. When you actually show people how easy it is, essentially I don't actually try and build things myself. I physically give them the mouse and tell them, drag this in here. Really engage them that way. And most people, when you give them the mouse and you actually show them, you just click here, drag that in here. That really blows them away because a lot of them are like, oh, I don't have to call anyone. Anyone can do this myself. And it's really that empowering moment. It's really, really interesting to see. Thank you. I have another comment. I am a Informatica developer, so does this mean I can use Altrex as an ETL tool, even though I am a developer, not a business user? So yes, many ETL developers enjoy the ease of use Altrex provides versus the traditional stack ETL tool. So definitely can still utilize Altrex in that fashion. Do you have anything to add to that, Adrienne? Yeah, I see a lot of Informatica people as well. They prototype in Altrex, and it's the same sort of logical flow. So it's all about the way you think. And then it's just how it's manifested out. A lot of people, it's just a way of thinking. And then you just follow the tools, and then the tools will sort of essentially build code for you. It's a code generator, very similar to what Informatica does. Okay. Adrienne, are the end users or business users expected to join tables? How do the business users understand the join or the relationship between the data? And normally start business users off with something pretty simple. Something where you would join, say, sales actual data with a budget spreadsheet. Something as simple as that. Just to showcase a concept. And then when you start doing very advanced joins, normally it's all about showcasing about where things are and helping them through that part process. So if it's really advanced, we will often build a workflow for that. But if it's simple, what I personally find is the most important thing from a business user's perspective is to actually show them the concept. And then the application of it just comes with experience. That's what it is. Fantastic. And Adrienne, how difficult did you find getting access to data in your company? How do you overcome the issue of data access as a business user? I think the key in that is actually to sit down with people in IT and people in charge of the data warehouse and sort of explain to them what you're trying to achieve and actually show them what you're actually achieving. So taking away that fear of what you're doing with the data, being always transparent with people. I think that's really important. For example, once I was exposed to the bill, happy to bring people in from the IT side and show them and database side and show them essentially what business rules and logic that's been used. So it's a great tool from that perspective. Fantastic. And I have a comment that says, so what would you say if somebody felt that such a stack, such a BI stack with essentially making IT people obsolete? What would you say to them? I would actually disagree because I think it allows them to focus on the more important things. How often have IT developers or the IT developers been always asked to just add that extra column or do this, do that, things that are really basic in the book? By essentially giving this control over to the business people, you're actually empowering them, right? So that IT can focus on the important things like provisioning of the richage cluster, vacuuming the tables, optimizing for speed, which that's a really important role as well that they play. Different important things also, by giving them this opportunity, they essentially are brought to the table and viewed differently by business people, as not the guy who stopped it, but rather the guy who's trying to help people. So I think quite a shift from holding things very tight to a culture of influencing. Thank you. And here we have another one. I know I can share workflows via Altrix. So why would I need a visualizer such as Tableau? Yeah, that's a good point to make. I honestly believe that you always want to use best of great products together. So Tableau is really pretty good at visualization. I think it's one of the market leaders, and that's why I prefer to do a lot of visualization in Tableau. But I do a lot of data prep in Altrix as well, because what we find is, or what I personally find is, the more you prep in Altrix, the faster your dashboard becomes in Tableau. So that's why you combine technologies together to actually use spreadsheet on the AWS side, Altrix and Tableau together, and then you get maximum benefit off of that. Thank you. And next question. I know that Altrix works with the MongoDB. Does it work with graphs, no sequels, databases as well? And the answer is, it actually does not work with graph databases. Sorry, I'm just looking through a number of windows here as the questions come in. Thank you for all the questions there. Fantastic. Just trying to keep up with them. That may have been the final question unless I see anything else come through. With that, I'll go ahead and start to wrap up. However, if I see any additional questions come through, I'll be sure to get them answered before we jump off. So thank you for joining us today and for your great participation. We hope you found this session helpful. And as the next steps, please visit us at altrix.com, backslash Altrix for Tableau, and download your free trial of our Altrix designer. Our host today, Dataversity, will be sending out a follow-up email with this particular link, as well as a link to our webinar recording. Thank you and have a wonderful rest of the day. Rahman, thank you so much. And Adrienne and Brandon, thank you to both of you. Adrienne, especially for joining us from Australia at the wee hours of the morning. Always great to have you. No worries. Thank you. To provide our audience. Let's get to the apologies. And a thanks to Altrix for sponsoring today's webinar. As Rahman mentioned, I will be sending a follow-up email within two business days with links to the slides, links to the recording, and additional information requested throughout, including the links to the download area that you see. I hope everyone has a great day. Thanks for being involved in everything that we do and being so engaged with all the great questions. And we will see you in the next webinar. Cheers.