It's like seven and a half. Yeah, it's like a mill. It's like a mill. OK, what do we use in there? We got it. A mill. What are you using here? I mean, the inches. The inch ruler? OK. So how many inches was the bottom length of our rectangle? In the mill. Seven. How long is it supposed to be? You had the rectangle there. It was five. OK. All right. What happened when we tried to draw around it with the big marker? What happened? The wobbles. OK. What happened when we tried to draw around it with the big marker? Were we able to draw close enough to the squares? No. No. So we added some gaps in there, right? But when we measured this before, we said it was how long? Five. Five inches. When we measured this side, how long was it? Three. Three what? Square inches. How long was the side? Three inches. Three inches. OK, so this side was five inches. This side was three inches. Can you get that labeled? You want to try? All right. All right. Good body. Good body.


In Sports 1996, Olympic gold medalist Choma Junwaseh's education is a must for athletes, footballers and sportsmen in general. She dropped this hint where six Kung Fu winners from the just concluded national sports festival Edo 2021 were awarded checks as part of efforts to encourage excellence in sports. Plus to be Africa's correspondent, destiny more more has more. Child beneficiaries of the guest choose were beaming with smiles. Spots legend and Olympic gold medalist Choma Junwaseh points out that acquiring education family is very essential for a bright future. Nigeria athletes should make sure that they take their sports serious and at the same time take their education serious because we know I personally I believe from my experience that you will be a better athlete or a better footballer than when you are educated otherwise people will be using your brain and it happened to me and that's why anywhere I go I always tell them that education is very very important it should go parry-passive with our sports because when you cannot go further again in your education you can you know make sports make do with your sports and if you cannot get to the climate with your sports you can make do with your certificate and get a job. Kung Fu athletes won medals at the just concluded national sports festival in Edo state and for the Chinese community it was exciting to see Kung Fu as one of the sports at the ceremony hence this gesture of rewarding them with checks. The representative says he's happy to be involved with Nigerian athletes who dimmed it fit to learn Kung Fu and he expresses optimism that Kung Fu will be recognized internationally in the future. Nigeria is also part of our own country so far we are all day here invested for Nigeria we are live Nigeria we are work for Nigeria this also part of our own country so we really help with education and sports so this time because of Edo sports competition so we are community also want to more push more for sports Mingbasi let them go into even Olympic self you understand so because of Chinese community won't do it. Honestly I'm so elected for what Chinese community are doing in Nigeria I think if not an umbrella state that have hosted their athletes you know I think this is the second one you know if just a community can do this I believe is a very big welcome idea. For the team this move is just part of the move to strengthen relations between Nigeria and Chinese community. Destiny Momo for Plus TV Africa.


Equality is human. Speak. Ever wondered why, as a woman, you have encountered situations where your voice has been threatened or forced into silence. That's because your voice is your power. True, women are empowered. What type of empowerment are we talking about? Financial, isn't it? Tremendous work has been done to get women financially empowered. But with your money, as a woman, even then, your voice is shut down a lot. Remember going to the market and being grouped, but can't really say anything, try to assert yourself, and your family shoots you down because of your gender.    I bet the answer is yes. And you most likely felt powerless. True empowerment will be a woman literally using her voice to determine and control her life choices. What exactly do I mean by your voice, the sound of you? When you find your voice, you don't find something new. Instead, you find a little more of you. You find a little more freedom to speak, express, and let yourself be heard. When you speak, are you listening to, heard or seen? Yes, right? So why the need to find and harness your voice? Because as a woman, we are generally dismissed on the basis of our gender. Therefore, we're actually not seen, heard, listened to, or respected. The most important thing to consider is, what are the fallacies? And what are those voices that keep you from speaking? If you don't feel free to speak, and many of us don't, those fallacies need to be told and the lies within them challenged. What were you wearing? Why did you scream louder? Are you sure you didn't enjoy it? Ah, that's why you haven't found your husband. No one will marry you. Women are not allowed to speak here.  What do you know? Keep quiet when men are talking. Can't you control your wife? Women are shamed into silence. It is shrinking themselves.  Even in the bedroom, a woman's voice is muted. I'm not allowed to express myself sexually. I can't say what I want done to me or what I would like to do. I can't dress to express my sensuality. I can't speak my truth. The search and enjoyment of Big O.  While he can impregnate a whole village, yet I am the one with the burden of using contraceptives. How did you get pregnant? We're not alone. We are left alone with the dilemma during men's cause. Body being betrayed by nature literally not considered a woman anymore or cast aside for a younger woman. I'm shut up on the premise that I cannot give birth, should not have desires anymore and therefore not entitled to natural desires. These issues seem insurmountable. But to be a whole human being, we must find our voices and speak up courageously even in the bedroom. It isn't going to be easy or Rome wasn't built in a day. So the next time you are asked what were you wearing? Clothes. What do you know you answer with the knowledge instead of shrinking? Over to you guys. Oh my goodness. Like literally spoke half of my heart. I practically jumped out or right on her back as she finished talking. But I just wrote down in my notebook this week that the power to say no you're right to say no.  I want to say no to you. When she said talking about all things were shrinking ability not to be able to be ourselves to accept ourselves for who we are men of course, many things and the part about being cast aside for a younger woman you know when we were discussing before the program I said why is that the issue of competition why do you think so those things those narratives that have been sold to us and I'm like look you know what the meaning women say you know what so you think that when I as a younger man or you as a young woman think that I will now come and just rubbish me like you but I'm going to deal with you trust me men will start thinking twice when we are when we're each other's friends and when we're standing up for each other so when you're when you're saying no and someone is asking you where do you want him to go I'm wondering what of all the times you didn't want how is that even how is that even something to ask where do you want him to go hahaha awesome do you understand what I'm saying and yes you're right comfort I mean we've been made to shrink over time where again we've been shamed into believing that even the natural things that come to us as women announcing and so much weakness and because we're again fighting to be seen we're fighting to be heard we're almost having to suppress that innate thing that makes us women but I want to compete in the corporate world and I have to dress like a man I have to wear those power suits and just lose my femininity I can't want to go into a bank I mean see yours meeting and wear nice floral dress and just look nice no I have to conform and look like a man we just have to change the narrative I think I mean if we talk about all these barriers and things there's so many things that each one of us at different time can just identify as things that we've gone through and I think it's such a crying shame that even at this time with all the things that women bring to the table we're still we're still being silenced you know I was just talking while everyone was talking the theme of this IWD for 2022 is breaking the bias so in my head I'm saying how can we actually break the bias because everything comfort had mentioned there are things that a lot of women are familiar with some might be familiar with it more than others but at different places you've actually experienced some form of bias or stereotypes so I'm thinking in my head okay we may not necessarily I mean we can change what is happening in the present but I'm also thinking for the future the younger ones coming on ground how you groom your children I'm blessed to have great parents and from my family are all girls one thing I know my father drilled in our heads was that you are as good as a guy you're not worse than a guy you're not less than a guy you are as great as a guy so don't let anybody tell you otherwise and that sort of pushed every one of us to actually achieve the best we can not every female has had that kind of you know affirmations while we were growing up so it's important that even as we raise up our kids and as we mentor people we also teach them and let them understand that they are not limited because they are females even because you are married you can still go ahead to do as much as you want to do may I just say something though I'm thinking where are these men coming from they are not aliens someone raised them so do we as women again then take responsibility for the fact that how are we raising our sons because these people are very behavioural they learn to believe that women are inferior women are less than so do we have a responsibility to take as women to say maybe we are dropping the ball somewhere I disagree actually I don't just think it's a bad thing because I think over time you are saying something I agree with you around the conversation that yes we are dropping the ball but it looks as if we are even going further and dropping the ball now and raising the girls now honestly I really don't know where we are going to see the next generation but as we said we are here to break the bias and the bias here is that somehow women are less than guys but everyone is a human being we should start by looking at both genders first as human and if we look at them as human man which is the cause is right for the woman which is the standard before we round up I actually wanted to say something on what she just mentioned because I feel like over the years we have actually placed the responsibility solely on women I believe that if a guy is able to show examples to his sons they are going to learn better that this is a way to treat a woman she is equal she is less than you but that guy again must have been raised by his mom who has shown him that I know we shouldn't say raised by the mom because obviously parenting is a two way there is mom and dad we have seen that the raising is usually left to the mom isn't it for a man who grows up seeing that example in his home of his father affirming his mom maybe he would be able to show that to his daughters but if you are growing up in that environment where mom is just subjugated she is not heard she is only seen then that is all you know and that is what you are going to do isn't it equality is human women please speak your truth truth is after the break


Hello everyone, I'm going to present the chess paper entitled A Compact and Scalable Hardware Software Co-design of Psyche. My name is Pedro Mat, Costa Massolino, and this work was done together with Leila Bettina, Patrick Longa and Yoast Henez. While doing this work, me and Yoast Henez were both at Radbaude University. Now we both are not any more affiliated to Radbaude University. Patrick Longa was at Microsoft Research and Leila Bettina is still at Radbaude University. So let's get started. So in this presentation, I'm going to focus mostly on the contribution of the paper, which is the hardware architecture. I'm not going to talk so much about how Psyche works, or mod-gram multiplication works, or any of the other stuff works, the algorithms themselves, because I want to mostly highlight how architecture was chosen, even though those algorithms influenced the architecture, I want to focus only more on the architecture side. But because they were influenced, I have to talk a little bit about the requirements of Psyche. Then after that, I'm going to follow with the literature how they made their work, and then some are our approach itself.  Later, I'm going to talk about the results. And together with the results at the end, I'm going to talk about some difference between our current print paper and also the THS version, because there was some change done. So what do we need to make Psyche? So for Psyche, Psyche is a protocol that works on top of elliptic curves, in this case, mod-gomerally elliptic curves. It can work with other elliptic curves, but you should work with mod-gomerally elliptic curves. And these mod-gomerally elliptic curves are built on top of the quadratic extension field. This quadratic extension field is built on top of a prime field. So this prime field is just operations on top of a prime, and this prime range between 434 bits up to 751 bits. Before, when this project started, it was between 500 and 3 bits, up to 964 bits. This change on the size completely highlights the fact that when you start doing something for new post-quantum schemes or even new public key crypto systems, you should be prepared to change parameters, change on some parts of the design and stuff along those lines. So it's always nice to have a very easily tunable implementation. On top of all of those stuff, like for these mod-gomerally elliptic curve operations, we're also talking about a treat-as-versing procedure. That's how, let's say, the fast procedure of doing SIDH works by doing the walk between the elliptic curves and the isogenes to the isogenes. And this procedure itself requires a lot of stack operations and data-ray operations. And also you need Shake256 that is on the SHA-3 standard. So how do we tackle this problem? For this problem, the best idea is to focus on the base of the triangular. So that's prime field operations. You focus on those operations. You focus on the Ketchak F as well. And then you do those on hardware. Then you build everything on top as a softer layer. The FP inversion, the extension quadratic field as a softer layer. You could also do some of them a little bit more into the hardware side. But then it's a trade-off between flexibility and also make it easy to maintain. Software is easier to maintain, usually from hardware. And as you grow, you make everything as a function of the order. So then you can call, put more arguments or remove arguments. So what's our solution? Our solution is a 16-bit CPU. So how I said it, we need function calls, we need data arrays, we need stacks operations, and those usually are well done for CPUs. So then we chose to use make a CPU and then make the prime field operations as a co-processor of such CPU. So that's Carmella. Carmella is the co-processor which was mostly of the project spent time on it. The Ketchak F is the Ketchak package. So the authors of the Ketchak already did a good hardware design. So we just took it and we just integrate into our CPU. So how is the approach of the literature? So the literature more older is more on the side of SIDH. So they did only SIDH. And they started with using affine formulas with fast inversion units, but that didn't go so well. So then they changed to projective formulas, which has better results. Then later they did more optimizations on the multiplier and then they got other parameters as well, added shake so they can have psych. And in that architecture mostly how it works is you have different multipliers themselves and a special other subtractor unit together in a main block of memory. Those multipliers, they are fed loaded and stored.  So then you need a really good scheduler for that. Another approach very different from that one is to focus only on the field arithmetic. So no SIDH no psych just to try to solve, make really fast field arithmetic. There was no SIDH in this case of paper. But it's a pretty it's a difficult problem. So if you make a very fast field arithmetic you probably most likely are going to get really good results. Then there was another paper as well that uses some carry safe notation and and that one also is a multiplier for SIDH. But our solution and you should pay more attention is to focus more on the side of doing a CPU. So be very flexible and be more can be easily tunable for any parameter in case of any change. Okay, so let's start focusing on Carmella. And Carmella is built around a multiplier and I added that builds the MAC the multiplier accumulator. And the multiplier is a 256 or 128 bits depending on the version of Carmella. We have two versions so the 256 bits version of Carmella is made for the seven series of FPGAs of Xilinx that uses rectangular multipliers and the 128 bits version of Carmella is for smaller units that have square multipliers like the Spartan 6 and older series at least that has enough. The adder itself is done for the multiplier to be used to get in the multiplier accumulator. That's why we have this special adder and then on top of this multiplier accumulator we put some pipeline stages 8 or 4 depending on the operation and then on top of that we build the FP operations. So let's focus first on the multiplier which is a problem by itself. So the seven series I was talking about has a rectangular multiplier of 18 by 25 bits sign it or 17 by 24 unsigned it. And the idea here that we want to build a sign and multiplier so then we can both use it for unsigned operations and signed operations. So we are built in 257 so the most second bit is used to basically to flag the multiplier into unsigned mode or signed mode. So one way to build it is to ignore the multiplier as being a rectangular one and just use as a square one and then when you do that basically you're going to need 256 bits multipliers and that's quite a lot. So then to reduce the number of multipliers we end up using a technique by Roy et al. That has been proposed a long time ago and in this technique we use a technique called the tilling where the idea here you see the problem as my 256 or 257 bits multiplier is a square of 257 by 257 and I want to fit as much as possible rectangular ones inside of that square.  So this figure here is up to scale and you have here 161 multipliers. For the 120 bits multiplier we don't use the tilling because we are talking about the Spartan 6 series which has square multipliers and we built everything around of square multipliers so we don't need to use tilling we just use the schoolbook itself. So then but the problem with this is we don't do final multiplication we just generate partial products and partial products are basically when you do long multiplications when you were at school that means you have you take one part of the decimal and you multiply by the big number then you have a lot of values later that you have to add. That's the same thing you have a lot of values that are just partial multiplications that you want to add. In this case we have 30 partial multiplications that we have to add. The best way to add is to use a unit called the compressor. The compressor basically is a unit that do only additions in one bit. So basically it takes the bit 0 of every one of the 30 additions and add them all together generating a 5 bits number. So then with this 5 bits number what happens is that you do all of this for every bit then you are basically compressing all 30 additions into 5 additions, which is way easy because I'm not doing carries and carries are usually the unit that makes the adders slower. So then you are reducing from 30 to 5, then you reduce from 5 to 3, then 3 to 2. When you are in 2 basically you cannot escape you have to do you have to solve the carries and you solve the carries by using a special adder in this case the adder that we used is this one is used both in this part and also another part for the accumulator. So this adder is a special adder because it has a special architecture around it. Usually for adders it's better to ask the tool to do it, like ask Vivado, ask Zalynx ISC to do it and they will do it to give you a really fast adder. But then for this size of bits like around 200 to 500 most likely this adder will not be as fast as possible most likely the most a very compact one cycle adder but it will not be as fast as it could be. So then you could use this other architecture that was proposed by the group of authors in the title and it's called the add-add multiplex where basically you just add two numbers and then you add with the idea of okay I'm going to add this number in case of carry 0 and in case of carry 1 then later going to solve this situation with a regular adder then after solving this the carries I'm going to feed it again I'm going to recurve the carry and I'm going to choose between the solution with carry 0 or carry 1. Basically and this makes this faster from just doing normal addition. The cost of doing using this technique is you're going to use more lookup tables but it's okay so then after you I decide okay I have this multiplier I have this accumulator but we need pipeline stage in order to make the frequency higher otherwise the frequency would be very slow. But how many pipeline states? So we want here to match two things so one thing that I want to match is I want to do two fp squared operations if I want to do two fp squared operations I can do it with eight multiplications and four additions that would be using this cool book technique and in order to do so I'm going to do eight and four so then technically I want to put eight pipeline states in my mark because if I put eight pipeline states that means I can do eight parallel operations then I can use the idea of doing always two fp squared and for additions I want to put only four pipeline states but I cannot put like less pipeline states unless it's an addition because basically I turn off or ignore the output of the multiplier therefore in the next architecture I'm going to show this a little bit better so here you can see that you have here the inputs are registers here in the registers A and B and my multiplier here it has internal five pipeline states then the adder is or the accumulator is used here so then I multiply two values then I accumulate them to here written by optimized adder and then the accumulator goes back to here and can be also shifted when I'm doing addition basically I'm going to ignore the output of this multiplier and I'm going to compress the two values so the two values are added here and then compressed and then we are going to skip these extra pipeline states that are here to match the multiplier and I'm going to add them together giving up to four pipeline stages the S here is it has a mask for doing the addition so then I can do mask additions and mask subtractions basically the idea here is I want to always do the addition but I want the addition to be effective or really not effective it's just a way to do additions without really if's analysis but just like people doing hardware I mean in software where they just use masks so now let's explain how the state machine works given the mark I'm going to do the 8 to 4 parallel operations the 256 or 128 bits operations so we have this 257 bits sided mark and how do we make up to 1024 bits words well we just make it split into words so then for example if I need only one word then this one word is sign it and then it's 256 then if I need up to 412 bits then the lowest significant is unsigned then the other part is signed the same for 768 where only the most significant word is signed by word here I mean 256 bits of words so that's how it works and for doing multiplications of these words then we use a product scanning technique through multi-government application in the product scanning technique basically you just scan to the product output instead of scanning to the operands while doing the multiplication so for example I want to do the multiplication of two words and then first I look what are the multiplications that affect the output of word 0 of the product oh those are then I do these operations then I shift to the next word of the product which is word 1 and then the first of word 1 and you can see here that basically what happens is that in the operands scanning the index of the operands just increments usually or maybe decrements let's say in a linear fashion and here your index of the operands just has to keep stable you add or subtract the other you add one and then subtract the other in order to both the index of both operands to keep equal so here and then we have we do additions to subtractions directly and by additions to subtractions like we just do the addition or the subtraction then the value is a negative number or a positive number but since we are using a sign representation the multiplication can work with negative numbers then later after we do all the processing we need to be sure that the output is between 0 and P-1 so then we have one extra operation that makes it from minus prime to the prime up to 0 to prime-1 and then we also add it after the chess paper we added addition and subtraction that also performs this extra reduction step in case in case it's needed so how do we control of this I'm saying that we use a state machine so basically you can think of all those algorithms that I just said with all the operands size and we just unroll all of them so if you unroll all of those algorithms basically end up with 300 up to 500 states and this is quite that really big a lot of states so then in order to be sure that everything just stays good and everything works I decided to make my own state machine and it's quite easy to make your own state machine if the number of ifs of the path of the state machine is pretty stable then everything is really simple because here what we have here is the row and the program counter so the program counter just stays in the set to the first state that I want to execute so let's say the first state of the operation then it keeps incrementing as it goes until it meets like oh this is the end of the operands size operations so for multiplication you can have the same program for both two operands size and then at some point just change okay so in this point for this operands size you have to go to this path so then you need this kind of if logic here besides that it's just the row and for the addresses of the operations you just use basically shift registers that keep rotating the addresses so basically every time that I am doing an operation of let's say the value 0 of the multiplication of value 0 1, 2, 3, 4, 5, 6, 7 and it keeps rotating and then at some point it always know which address I am working on because it's rotating all together so is this everything enough? well I just said to you how I did all the prime field arithmetic which is already a pretty hard problem by itself but that's not psych that's the only prime field arithmetic we're still missing a lot of stuff we're even missing even the inversion of the prime field are fp squared operations Montgomery elliptic curves and all the things necessary for Psyche and SIDH so the remaining parts of Psyche are run on top of the CPU so now we're going to focus mostly on parts of the CPU itself and not so much in Carmella so the CPU is a Harvard CPU with a custom instruction set basically the instruction set I made as simple as possible and tried to make as big as possible to be easily decodable that was my entire goal so then it performs some basically operations, additions, subtractions shifts, rotations logical operations, comparisons jumps, conditional jumps loads and stores and push and pop and I'm focusing, trying to focus more on load stores and push and pop because those are mostly the operations that are really well needed for the function calls and also for the data pointers itself and all the operations are all also needed as well so this is a cyclical basis CPU so then the CPU that basically takes the operation the codes, executes then takes the next one all in the same heart it doesn't try to do some pipeline or anything like that and the CPU basically takes the operation and if it doesn't match the operation for itself it just sends to Carmella then Carmella will have to execute it and the CPU has some kind of pipelining just in the address resolution of the instruction can be solved a little bit later and let's say in the terms of pipelining it's kind of okay because there is an extra state to be sure that the address resolution is updated in case the values of the registers are also updated so it's fine in that sense and the 16-bit ARU you have a DSP and a BaroShifter separately because I end up building the BaroShifter separately instead of using the DSP it looked like a little bit better and for the BaroShifter you look to the paper there is the the citation there from where I took it and the RD registers the base run one thing to pay into account that they both share some memory region so what happens is that both the base run and RD registers they have the same values in both locations this can be seen kind of as a cache unit but doesn't really use a cache unit in the other sense of being fast for memory it's just that the registers share the same positions so then when you do some operations you can do use the values from the base run and sometimes you want to use the values from RD registers and that's it so the high level of the architecture basically is you have a local bus of 16 bits that's the bus that connects everything and you also the pro itself is not connected to this bus it's a Harvard CPU to program the bus only the external loading is possible which is done to here to the external communication this is not really connected the CPU cannot load the values from the prong only the external communication can access the prong the values on the prong are then goes to an extra instruction and then the address is resolved then goes to the current instruction which is then executed or passed to the core processor itself one thing to pay into account is that because how Carmella works with the 8 or the 4 operations and basically each instructions of Carmella takes the same size as one instructions of the CPU then all the Carmella instructions has to be one after another so you always have 4 blocks of Carmella edition operations and also 8 Carmella instructions here and you also have a stack counter to know because of the pushes and pops the mac run which is basically the run that Carmella uses with the 256 bits bus but that can also be accessed by the local bus and the base run is another run that's amazingly used by the CPU where it can store some other values related more to the CPU from Carmella itself so like the pointers and the data arrays and some points, some stuff is stored there together with the kcheckf values like to shake out both values so for the results I want to highlight a few things the first thing is the first thing that I want to really highlight is there's never a really let's say a solution that's good for everything our solution here is that we want to show and we want to give is a solution that works for all the parameters it doesn't need to be reprogrammed it's not really fast but it's extremely well flexible if it's deployed and it's going to work with all the parameters and everything it is done, it's ready it's a good way but if you want something really really fast then you should maybe look for other results like the selected results they have a lot faster results for us and if you have requirements for this kind of timings then you should need to expand the amount of resources our solution on the other hand if you are if your timings can be a little bit let's say you accept bigger timings then our solution can be well fitted it has way less DSPs from selected results and slices as well for the same parameter and we give not only this parameter we give also the other parameters as well as a consequence and that's pretty much our contribution and when we compare this with other schemes these are pretty old results there are new LATs based implementations but the main idea here and I also kept these results here because I think the new results waste a little bit more resources but SCIKE needs a lot of resources for a lot of lots of time it needs lots of time to do stuff and also needs lots of resources it's a very not very resource friendly scheme but one thing that I want to see it here is that our SCIKE implementation because it's a CPU with the Carmella core processor and we have all this ellipt curve stuff all the prime field arithmetic we can also do PCC as well we can also do scaling multiplication so P224 up to P521 and even a bigger prime field up to the limit of almost a thousand bits and of course this is all done with the same CPU so no V2DL needs to be redone, no FPGA has to be reprogrammed just the CPU it could even share the same program of the SCIKE you could have the SCIKE program and the ECC program in the same and in the PRO itself you can have both programs and just start one and start the other so our solution could also be used later as a hybrid, now at the end of the day it cannot do the hybrid but it could be used as a hybrid so between the THS and the A-Print that's online I think since June or maybe May so the two things the main thing that I want to highlight here is not only that we have the scaling multiplication but also we fixed a bug in P503 so P503 had a bug that was discovered by Ultimaco because they took architecture and did some testing themselves and then they discovered there was a bug for the P503 parameter so then as a consequence I had to update the VHDL files and some other stuff related to the limits of the representation of the modcromed representation and then everything just worked fine and then this was updated and then it is already there and I want now to conclude my talk and I hope you liked it, if you liked it don't forget to subscribe to EACR and to press the like button


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.  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.  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.  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 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.  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.