So nothing says I am behind for Venezuelan rumble, indeed it is. Diplomatico, it's the limited release pot still. Lean over, make sure that's still in shot. It's important to have the room in shot. Just from some stuff. Spitfire Labs, if you're not aware of Spitfire Audio, they mostly felt like they filmed scoring and TV scoring, so it's a lot of orchestral stuff, a lot of really pretty piano stuff I've got there. The pack, which is really really nice, but they also have this range of labs, sample sets which are all free, and I think everything I made, yeah it was all free stuff. He's on the cider as per, as per, just in case you can't, as per. So the sequencer isn't running at the moment. The samples are just on the loose. Do some of the LFO doing. The LFO is just doing a tiny bit of pitch wobble today, so far. Glad to see you back. I've been trying to pluck up the motivation to do my next proper video that have been failing and just ended up playing Fallout 4, which is fine. I'll be putting pressure on myself. I've got some friends in that area and I know it's really tough for some people at the moment, so hopefully you can stay warm and safe. Turning the master probability down. Bed to some, yeah you literally just arrived, just after I said it actually.  You can get paid samples from them as well, but yeah these are just bits from, has anyone else, because they always brought a new case just so I could just buy one earlier. Cock the tracks as well. I really, really want one of those. Try to avoid it for long enough. It always gets not, it's a rum, actually. It's Diplomatico, and it's the pot still. It's just the Digitech that's inspiring, and I've just felt it as well. Life is a synthesis. Rune imagination. Consciousness. The LFO of consciousness is the ebb. If anyone else has some positive synth affirmations, drop them in the chat. Tenner receiver. And the energy we give feel like our oscillators. Just get some lowness amongst this tape orchestra, I think, which is what this is from. It's got a load of orchestral samples which have been run through, like destructive tape processes. Is there any, is there any joy synthesis and music technology? Basically just sweeping in and out in this case. Some from tape orchestra, but they're all just little loops using Spitfire Labs. Yeah, drop a link on this video to that in a couple of synths. It would be a great place to start. Find things that way, that's really cool as well. The other thing I would say is that a lot that you can do with a single sample, but in the sequencer, earn yourself. If that's, the factory samples are brilliant, then there's so much stuff that you can do inside there. There's so much you can do with so little that maybe it's a great shell as well from Exeter. Footsteps, nature in general can be interesting. But again, it kind of took samples and really explored those as well because there's so much you can do in the, maybe you've seen it, you might not have seen it. In December I did a video, one sample challenge where I just took a single sample. I think that's an interesting exercise. The little synth arpeggiator, now I'll pet you on here, maybe slower and faster. That's so slower and faster, slower and higher. I mean, so that's true something. Just a thumbs up to prove this is live. You can see all the cycle waveforms. I've just got the looping sample and I'm changing the tuning to low so it doesn't have an every single one. I've just picked out some notes. Here's this track as well, the bass track, a bit pressure on and I'm just sweeping that filter installation. It's answered on the stream. They show us on the digitone as well, approach it and achieve those vibes would be different from a workflow perspective. If you're interested in that sort of stuff on digitone, I have a video on the channel which is called How I Drone on the Digitone, which kind of looks at this sort of arrangement, a bit more chill than this actually, believe it or not. Arrangement is coming from the fact that I've just got a loop. The same bit different kind of vibes and that video goes through like balanced. So yes, the flow is set to affect the sample length and it's looping. So you've got all this jumping around track. I'm changing the direction as well so you can see the playhead just jumping on. I need a research but that kind of the combination of novation tracks and rhythm would be similar to digitone. I don't know the full specs of the rhythm yet. I suspect not. Be very surprised if the rhythm is all that much like the digitone in terms of what you can do with it. It might be a bit more like a digitact and a model cycles maybe, but the digitone from a synthesis perspective is very very deep and I think that's sometimes for the circuits where you've got it's lacking a little bit unless you go into the deep editing I guess. Minimum two s width single ush over that. You feel like it's getting towards bedtime anymore. Modular because my modular rack is full now. There is no space left in it. Let's pose some questions and cheers to you as well my friend. I think on this track we've got kind of like a strings thing. 16 HP blank in play. My bank balance wishes that it was but no it's completely fine it on modular grid if you want to know what's in it. If you just search for my username. Yes the last module that I added to it was awmd at n410 and I tell you what they're not joking when they say that it's a tight squeeze to get that module in. I thought I was going to get it in for a bit. That gives me just a little more hands-on control with nice sliders over some stuff which knobs one string here. Yes I have interest in that because I've got but I imagine it's very very good. I try to have a sort of completed jam the finished track. It's a bit different I guess with the acceleration on it's a bit funny sometimes and also later. As I published a new recording this evening it's going to be all about the digitone and voice management which sounds absolutely gripping. I'm sure that you get the very best from the digitone it's an often overlooked aspect I think. And then there'll be a video I think about my modular rack now that it's finished. An opz video about how I use the tape track because it's not the way that most people use it I don't think. Or I've been told that I should be getting a corg op6 at some point which I think will be very good fun because I don't know whether you know this about me but I quite like FM synthesis and FM synths around is it as good as a digitone? Or may have to try and answer that question. So that's what should be coming up on the channel. If I can find the energy to actually record the videos. It's weird jamming where I managed to put out gems but now I just it's completely trained me I think and I just cannot find the energy. I say I've finished modular rack and you're quite right a full modular rack. There's no space in it it doesn't mean it's finished. In fact just earlier today I was talking to all my friends about doing a trade for something for one of his mantis cases so I could have some more space. It's never truly finished as you will know. So yeah stay safe everyone take care of your saves and others and I'll see you again soon. Have a lovely wonderful evening.


Hello everyone, myself Mr. F. R. Syed. I work as an assistant professor in the department of computer science and engineering at Walsh and institute of technology. So, I will put the topic for my today's lecture is handling signals in Unix system. Now, at the end of the session the students will be able to describe the handling of the signals in Unix system. Let us first see what is a signal in Unix system. Signals inform the processes of a synchronous events. Now, processes may send each other signals. Now, those signals could be sent either with the kill system call or the kernel may sometimes send signals internally. Now, there are a total of 19 signals in the system 5 relates to of Unix system and these signals are categorized. So, let us see those categories. The first one is some of the signals may be related to the process termination like those signals which are sent in case a process exists or signals send with the death of child parameter. Next category is there are some signals that are related to process induced exceptions like a process may try to access any address that is outside its virtual address space or we can also have a process that is trying to write on a memory that is dedicated as read only means no such write operation could be performed on that memory part. Also a condition could be like execution of any privileged instruction or in case any hardware errors occur. So, signals related to such events are coming under this category. The next category is the signals that are related to some kind of unrecoverable conditions during a system call like conditions as you can see as an example running out of system resources. The next category is there could be some signals for tracing the execution of a process. We have the next category there could be some signals that could arise due to unexpected error conditions. Now, what could be the error conditions? We will see some examples. The first one is making a non-existent system call. I mean a system call that actually does not exist if such kind of a system call is executed such kind of signals could arise. The next such condition is as we know a pipe does require a reader and a writer process associated with it. So, a condition like writing a pipe that has no reader process. So, that is another an error condition. The next type is using an illegal reference value for LC system call. The next category is signals originating from the processes in user mode like when an alarm signal is to be sent after a period of time or some arbitrary signals are to be sent to each other with the kill system call. Next is signals related to terminal interaction like if a user hangs up the terminal. So, signals could arise at that time or signals that could arise when the user presses break or delete keys on the terminal keyboard. Now, this is the process state diagram and we are going to see over here how the signals are going to be checked and handled. Now, there are two possibilities where the signals are checked and handled. Now, these are the two possibilities. The kernel checks for the receipt of a signal only when a process is about to return from kernel to user mode or when it enters or leaves the sleep state. So, this is indicated in this diagram over here when from kernel to user mode and moving between sleep and wake state. So, there is no instant effect on the process running in the kernel mode and a process never executes in user mode before handling the outstanding signals. Now, next we see an algorithm for recognizing a signal the name of the algorithm is ISSIGS signal. So, it returns as output true if the process received signals that it does not ignore otherwise it returns false. So, while the received signal field in the process table entry is not 0 it will find a signal number sent to the process and if the signal indicates it is a death of child and if ignoring death of child signal that means, if the death of child signal is to be ignored in that case it frees the process table entries of the zombie children otherwise if the death of child signal is to be catched then it returns true. Now, this is when the signal is for death of child otherwise if it is not going to ignore the signal meaning the signal will not be ignored in that case also it returns true and at the end turns of the signal bit in the received signal field in the process table. So, now the students are expected to think and write the answer to the following question what are the different ways in which a process responds to a signal. Now, pause the video and write your answer. Okay now let us see the answer to the question what are the different ways in which a process responds to a signal? The first way in which the process responds to a signal is it either ignores the signal or second one it will handle the signal and third one it will perform some default action. So, these are the three ways in which the process may respond to the signal. Now, let us see the topic of handling of signal. Now, there are three cases for handling signals the first one is the process may exit on receiving a signal that is the process may come to a termination when a signal is received second one it will ignore the signal meaning it does nothing when a signal occurs and third one it may execute a particular user function on receiving a signal. Now, this is a syntax for the signal system call old function equal to signal into bracket two parameters signal comma function where signum is the signal number of the signal for which the procedure is to be done function is the address of the user defined function that is to be executed on receiving the signal indicated by signum and the return value old function was the value of function for the most recent call to signal for signum. The process can pass value 1 or 0 instead of the function address and the future occurrences of signal are ignored if the parameter value is 1. Now, this is the algorithm for handling of the signals algorithm PSIG. So, the first thing that it does is gets the signal number set in the process table entry and then clears that signal number from the process table entry. If the user had called signal system call to ignore the signal then it returns otherwise if the user specified function to handle the signal then it gets the user virtual address of the signal catcher that is stored in the user area. Then it clears the u area entry that stored the address of the signal catcher and then it modifies the user level context and the system level context and ultimate late returns. If the signal is such that the system should dump core image of the process then in that case it creates a file named as core in the current directory and then writes the contents of the user level context to the file named as core and ultimately it invokes the exit algorithm immediately. Now, the kernel first determines the signal type and turns off the appropriate signal bit in the process table entry as we have seen in the algorithm and if the signal handling function is set to default value the kernel will dump a core image of process for certain type of signals before exiting. Now, the kernel dumps core for signals when something is wrong with the process like either the process is executed an illegal instruction or has accessed any address that is outside its virtual address space. The kernel does not dump core for signals that do not imply a program error that is in case of receipt of an interrupt signal. The quit signal indices the core dump and the processes behave differently in different scenarios like if it receives a signal to be ignored it behaves as if nothing has happened and if it receives a signal that it has previously decided to catch in that case it calls the user function for handling the signal. This is the reference used for the video lecture. Thank you.


Any reviews and recommendations I do, I mentioned this in the introduction but I'm gonna say it again right now, it's gonna contain spoilers.  I wouldn't recommend you watching this video right now because I can't help but give spoilers. What we're going to do is take a look at Eternal Warrior number 1-8. What I just did recently was a couple of weeks ago, I sat down and I read Eternal Warrior 1-8. I think I read 1 and 2 first the first day and then got totally hooked and read number 3 to 8 the following day and I decided to start off with this book for two reasons mainly. If you've been watching my comic book videos you know that I'm an old-school valiant fan. I really like the books Unity, Pre-Unity and a little bit post-Unity in the early 1990s and after finding out that valiant that a relaunch is going back and buying some of the relaunch issues and reading those and I got into that after reading Rai, finding out that Rai was coming out, the relaunch of Rai which was really excited about because I really like that character, I really like the universe presented there right. So I read, you know the first book I read of the valiant relaunch was Rai and loved it and then I started buying some of the other books I believe about Quantum Woody Knife and Laugh My Ass off but Harbinger Wars, Bloodshot the crossover, the whole thing there including number one all the way up for Harbinger and Bloodshot Red Doze and God just got blown away and what I ended up doing is dropping everything else and almost everything else and going with almost exclusively valiant. So the reason I decided to start off with this is because valiant comics is one of the things I've been reading a lot right that's my main reading of comic books for the last few months. The second reason I started decided to go with this book with this set is because I've been going online and talking to some valiant fans different forms and stuff and every now and then if you run into a valiant fan at a comic bookstore you end up chatting if you find out they're valiant fans just to see what they think of the universe right because it's brilliant it's like catching people by surprise it caught me by surprise right and you sort of find out what they've been reading and what they've read and because people there wasn't too many people that jumped on right away there's a lot of people that are trying to read back issues and catch up right and Eternal Warrior is one that I hadn't read I'm doing major catch up play right and I found out after talking with people is the people were reading the valiant relaunch from the beginning or early on is a lot of people didn't appreciate Eternal Warrior I guess as much as I did and I can understand why because it's eight issues and the first four issues they they don't directly link to five to eight right so there's a huge gap it goes from one in the spectrum to the other in the spectrum and the cliff angle for number four was so amazing that you wanted to see what happened next right and it is it is a little bit different than some of the other stuff that I've read but I really loved it okay so that's the other reason I wanted to do this is because I know there is about some of the valiant fans I've talked to very few of them not many have read Eternal Warrior and it's it's an amazing read I really loved it and I you know going back to the 1990s Eternal Warrior I liked it I'd like to turn warrior he made his first appearance in solar manatee Adam number 10 and the first full appearance in manatee Adam solar number 11 right and I was reading solar at the time and you know solar one to ten alpha Omega storyline were written by Jim Shooter Barry art by Barry Windsor Smith and Bob Layton if you haven't read those tennis shoes you should definitely read those tennis shoes there one of the greatest stories ever told right as for the storyline what is this relaunch all about now in the first issue we get introduced to gelat and the timeline for the first issue or for the first four issue is as follows it shows gilad in the present right in the present day valiant universe which is at the present for us as well right set in the present setting and it's in that period where gilad the Eternal Warrior has chosen to live an isolationist lifestyle right he's disconnected himself from humanity and there is no geomancer he's not being the protector of the geomancer he's given up that mindset that that philosophy that was born into right so he's a little pissed basically so that's how the storyline sort of starts and does flashbacks to sort of the medieval period where it was around where humanity was tribal based right and the story from that period is set in a time where gilad's eternal warriors tribe is about to go to war with another tribe and their brutal savages or whatnot you know it's it's a battle to the death really to try and annihilate each other and gilad has a daughter and a son and that sort of starts off issue number one of the relaunch of eternal warrior and halfway by halfway through this I was blown away because it does things which is like wow very heavy and it gives the character development and this is brilliant and the relationships the way they build up the relationships are amazing right and one thing what the eternal warrior is is since he's been around for well for a very long time he's he's he goes through periods there's a lot of emotion involved between him showing him from this period at the present going to the medieval period and it does flashbacks to other periods as well okay and that's what happens in the first four issues of of the series right that is issue number one okay nice cover and issue number two issue number three and issue number four sort of follow the storyline right building on in that's character or sorry gilad character the eternal warriors character right and sort of going between the present where you know other people enter his life and it gives us a sort of a feel for why he's in the headspace that he's in at the present which is sort of isolationist point of view right where he really doesn't want to have anything to do with humanity right he's just living off the land and not interacting with any human beings or interacting as minimally as possible right and the cliffhanger for this for issue number four is absolutely magnificent when I finished issue number four I got hooked halfway through issue number one because I didn't really connect with the eternal warrior from the 1990s that that much I love the storyline but I found them to be I really didn't get attached to him I couldn't get I couldn't feel what it was feeling halfway through issue number one of this relaunch you got hooked I got hooked anyway and it got better and better as I read issue number four leaves you with a cliffhanger that you're dying to read issue number five which should be the continuation of issue number four unfortunately it's not right for those of you who are dying to read what happens after issue number four we don't know I told you there's spoilers in this review issue number five to eight pick up in 4001 AD which is the time period of where rye is around right rye exists so the first four are occurring in whatever thousand BC and the next four issues of this eight issue set occur in 4000 AD so there's a 6000 year or whatever gap between the two periods and that's one thing that I talked to some valiant some people some valiant I did I was able to talk up with a couple of people talk with a couple people that had read the eternal warrior and they really didn't like that aspect of it right for me initially I hated it I read eternal warrior number four and I really wanted to read what happens next and we're left hanging right I guess that's why this called cliffhanger right and just to give you a feel of how amazing the build up to this is the first four issues and the way it leaves you there is this is the same type of cliffhanger that you would find in the walking dead right the walking dead comic books the series is fantastic if you're watching the series right it's it doesn't do everything the comic book does which initially threw people off but I think it does a brilliant job of telling a certain perspective is fantastic right but the comic books are in a round of their own they're absolutely amazing and the one thing about the walking dead comic books are the cliffhangers when you get to the end of one issue you can't wait to read the next issue okay and that's the way eternal warrior number four was for me right when I picked up eternal warrior number five was really disappointed find I've been continued until I started reading and eternal warrior number five by number six I was totally immersed into the storyline and it is absolutely brilliant it's sort of post-apocalyptic right but not cyberpunk post-apocalyptic it's sort of post that has happened a long time and nature has grown back and humanity has sort of gone back to a sort of a tribal period and there's different tribes around right the first four issues and the next four issues the last four issues issue five to eight vary so much in tone and pace that I think that's one of the reasons that eternal warrior is not highly ranked among people who have been following the relaunch of the valley universe for me I I really love the pace and I really loved what they did the first four issues of this are sort of chaotic and they show show gala the eternal warrior in chaos with himself right it has him in the present and it shows flashbacks of different periods and it shows us you know all the things he had to do some horrendous things he had to do to protect the geomancer because supposedly it was the will of the earth right so he had to stand by and allow slaughters to occur right he had to walk away from atrocities and that really we get the sense for information number one to four how that has played out in his life and how how the bitterness has sunk into him when they show him as an isolationist right because he's had to do some crazy stuff you know he's the immortal immortal warrior right he's been around at present you know he's I guess it came to be in 3000 BC right right now if present day you valiant is now that's 2000 so 5000 years just imagine what you would have seen in 5000 years and the first four issues really portrayed out really well and they show the chaotic nature of gala right the eternal warrior where he's unstable a little bit right angry the eternal warrior in number five to eight the storyline right we're in number one to four the timeline jumps in number five to eight the timeline doesn't chop it stays in 4001 AD right which is absolutely brilliant and it sort of mimics the mood of the character of the eternal warrior during this period right and again it's a period where it's tribal okay and gilad is the head of his tribe the protector of his tribe right and from what I remember I don't remember Geomancer being in this in this last last eight issues right so he's the protector of his tribe and something happens and he has to go on now walk about and retrieve something to help the tribe and that's the most I can do without giving spoilers right unfortunately that and it's worth reading right but basically he has to go out on an excursion to find a cure for his tribe and what happens is the character they pair up gilad with is his granddaughter right so he's an older gilad right he's been around for 6000 years just imagine what he's seen post apocalyptic and all the things he's had to do so he's a wiser character his character is a set in stone he's paired off with his granddaughter which is like ten years old plus or minus a camera was 8 or 12 or whatever it is right so a 10 year old brilliant 10 year old inquisitive 10 year old and very high energy 10 year old right so you see that in this in these eight issues a gilad a grandfather's interaction with his granddaughter the grandfather being the wise old man and the eternal warrior being the wisest old man there could be right he's like 6,000 7,000 years old right and him interacting with someone who's lived 10 years and bug I like huge I'd about the world and wanting to know more and more and they sent these two off on a little journey on a little treasure hunt basically right and it's a brilliant storytelling was very well done I really got into this completely differently than I got in the first four issues the force first four issues were super exciting they created the universe for the for the valiant universe really it's like what's going on with the eternal warrior and who are some of the main players main people in this universe and the eternal warrior want to for as far as I see it for the valiant universe is must reading because they introduce they don't introduce they mention different castes right that are around and they're different families and they're the ones who've been growing controlling certain aspects of how society functions and it left a lot of cliffhangers they're gonna build up a lot I believe on what happens from eternal warrior one to four right eternal warrior five to eight standalone storyline futuristic possibly tie in with right because it's around the same period right and it's sort of a lot of different characters come in and the interaction between Gillard and his granddaughter is amazing and Gillard is immortal right and his granddaughter is ten years old and he knows the way he sees it when he's seeing a lot of the stuff he's very calm and quiet and it builds up such a strong bond between them that you can sense how much Gillard loves his granddaughter it's absolutely amazing it's one of the best storytelling is relationship buildings that have seen in comic books between an older character and a younger character very well done very well done indeed and in eternal warrior number eight right it it is a cliff hanger it has a cliffhanger and it's a brilliant one at that where it's very it's very quiet cliffhanger you're not anxious about it you're more more inquisitive about it thinking about it and the style joke about it it's in a weird way where the granddaughter this world that Gillard is in is post-apocalyptic but it's not cyberpunk right it's more nature-oriented tribal so there's very minimal technology and towards the end of this through their excursion Gillard's granddaughter finds technology and starts working with it playing with it and wants to know more and from the mindset of what the dialogue that the eternal warrior has we realize that technology was really one of the main causes of the downfall of humanity right so he's really you know apprehensive about his granddaughter playing with technology but he's a wise grandfather right he's a wise human beings a wise teacher so he allows that seat to grow right he doesn't stop it he just posts questions to the granddaughter right like the granddaughter asks Gillard to do certain things which children would do without thinking right but grown-ups would not right like kids if you watch kids and I heard this from an episode of a cowboy beep-op of all places right brilliant animation series if you ever want to immerse yourself in animation cowboy beep-op is it right and samurai shampoo and all that stuff right but in an episode of Kamala beep-op one of the characters turns around and says there's nothing as brutal as a child right no human as brutal as a child and 100% correct you see children grabbing insects I used to do unfortunately when they would tear him apart just to see what would happen and in the eternal warrior 5 to 8 you there are multiple times where the granddaughter wants Gillard to do something because she knows he's very powerful and by doing that it would mean Gillard would have to kill a lot of people and he doesn't turn around and say no that's bad right because what he's gone through what we see in eternal warrior 1 to 4 really is embedded in the next four issues right so Gillard turns to her and says explains that if he needs to do this that means that many people have to die and if he doesn't do this maybe this many people after it's amazing mathematics logic right so he says that to the daughter and says if you still want me to use your choice I will do either this or this and you know when you're reading this that if you have no idea what the daughter is about to say right because of what happened in eternal warrior 1 to 4 right because eternal 1 to 4 is more brutal and you get the sense that Gillard would do either one irrelevant of what the granddaughter would want irrelevant of the consequences if he thought which one was the right choice or the wrong choice because he wants the granddaughter to make the decisions to make the choices and learn from those choices or pay for those consequences right and it's a brilliant read the cliffhanger is more a breath right the cliffhanger at the end of issue number four for eternal warrior is more anxious you're dying to read issue number five I hope they continue the series they continue the storyline because it's I believe it could be and it is to me right now is one of the strongest characters in the valiant universe not necessarily in the crossover books a unity I've read unity I'm reading unity I'm read all of it I'm reading unity and I read the valiant and this eternal warrior here is much darker much more calculating much calmer well the older one versus the near one so brilliant read if you want to hop onto the valiant universe and if you're new to valiant you don't know these characters you shouldn't worry too much okay because all you really need to know for the eternal warrior they explain all the stuff throughout the books through these eight issues so you really don't need to know anything but it's good to know that eternal warrior lives forever he's immortal right that's the only thing you really need to know because when valiant that they're relaunched they said there are no canon stories from the past there are no stories that are set in stone that you have to know for the universe that exists now everything's off the table right or everything's on the table they can do anything they want okay it's a great place to start if you're new to valiant and if you're old-school valiant I know a lot of people who are reading valiant they haven't read the eternal warrior because it's not one of the top things people talk about and I've talked to some people who've read it and they're a little they didn't like the jump from four to five and but I really loved it after reading the whole thing and thinking about it and letting it bounce around in my head I was like wow this is amazing right so no matter what group you belong to you might like this this book especially the character development and the interaction and the different families and the universe that's being created fantastic after this is eternal warrior I believe days of stale three issues set that I haven't read yet which I plan on reading and eternal warrior appears in unity and crossovers or whatnot right I hope you liked I hope you could pick up these books fantastic reason if you do read them I hope you enjoy them right I'll let you know what else I find and I'll definitely do more reviews and recommendations of comic books and at some points comic book related stuff may they be books may they be movies TV shows music whatever that's it for now I guess I'll see you guys in the next video


Altrix is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over 20 plus years has been that Altrix has always been a data company. Early in the big data and Hadoop cycle, it saw the need to combine and prep different data types so that organizations could analyze data and take action. Altrix and similar companies played a critical role in helping companies become data-driven. The problem was the decade of big data brought a lot of complexities and required immense skills just to get the technology to work as advertised. This in turn limited the pace of adoption and the number of companies that could really lean in and take advantage. Now the cloud began to change all that and it set the foundation for today's theme du jour of digital transformation. We hear that phrase a ton, digital transformation. People used to think it was a buzzword, but of course we learn from the pandemic that if you're not a digital business, you're out of business. And a key tenet of digital transformation is democratizing data, meaning enabling not just hyper-hyperspecialized experts, but anyone, business users, to put data to work. Now back to Altrix. The company has embarked on a major transformation of its own over the past couple of years, brought in new management, they've changed the way in which it engaged with customers with a new subscription model, and it's top graded its talent pool. 2021 was even more significant because of two acquisitions that Altrix made, hyperana and trifacta. Why are these acquisitions important? Well traditionally Altrix sold to business analysts that were part of the data pipeline. These were fairly technical people who had certain skills and were trained in things like writing Python code. With hyperana, Altrix has added a new persona, the business user, anyone in the business who wanted to gain insights from data and let's say use AI without having to be a deep technical expert. And then trifacta. A company started in the early days of big data by Cube alum Joe Hellerstein and his colleagues at Berkeley. They knocked down the data engineering persona and this gives Altrix a complementary extension into IT where things like governance and security are paramount. So as we enter 2022, the post isolation economy is here and we do so with a digital foundation built on the confluence of cloud native technologies, data democratization and machine intelligence or AI if you prefer. And Altrix is entering that new era with an expanded portfolio, new go to market vectors, a recurring revenue business model and a brand new outlook on how to solve customer problems and scale a company. My name is Dave Vellante with the Cube and I'll be your host today. In the next hour we're going to explore the opportunities in this new data market and we have three segments where we dig into these trends and themes. First, we'll talk to Jay Henderson, vice president of product management at Altrix about cloud acceleration and simplifying complex data operations. Then we'll bring in Suresh Vitaal who's the chief product officer at Altrix and Adam Wilson, the CEO of trifacta which of course is now part of Altrix. And finally, we'll hear about how Altrix is partnering with Snowflake in the ecosystem and how they're integrating with data platforms like Snowflake and what this means for customers. And we may have a few surprises sprinkled in as well into the conversation. Let's get started. We're kicking off the program with our first segment. Jay Henderson is the vice president of product management at Altrix and we're going to talk about the trends and data, where we came from, how we got here, where we're going, we got some launch news. Hello Jay, welcome to the Cube. Great to be here. Really excited to share some of the things we're working on. Yeah, thank you. So look, you have a deep product background, product management, product marketing. You've done strategy work. You've been around software and data your entire career. And we're seeing the collision of software, data, cloud, machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or data executive at an organization, Jay, what's your north star? Where are you trying to take your company from a data and analytics point of view? Yeah, I mean, you know, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust, creating large volumes of data. Storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organizations, you know, drowning in data, but somehow still starving for insights. And so I think, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization and really start to democratize the analytics and, you know, let the business users and the whole organization get value out of all that data they have. We're going to dig into that throughout this program. Data, I like to say, is plentiful. Insights, not always so much. Tell us about your launch today, Jay, and thinking about the trends that you just highlighted, the direction that your customers want to go and the problems that you're solving. What role does the cloud play and what is what you're launching? How does that fit in? Yeah, we're really excited today. We're launching the Altrix Analytics Cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud-native. And to take advantage of things like browser-based access so that it's really easy to give anyone access, including folks on a Mac. It also lets you take advantage of elastic compute so that you can do in-database processing and cloud-native solutions that are going to scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud. We've got Altrix Machine Learning, which helps upskill regular old analysts with advanced machine learning capabilities. We've got Auto Insights, which brings business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest addition, which is Trifacta. That helps data engineers do data pipelining and really create a lot of the underlying data sets that are used in some of this downstream analytics. So let's dig into some of those roles if we could a little bit. I mean, traditionally, Altrix has served the business analysts, and that's what designer cloud is fit for, I believe. And you've kind of expanded that scope into the business user with Hyperana. And in a moment, we're going to talk to Adam Wilson and Suresh about Trifacta and that recent acquisition. It takes you, as you said, into the data engineering space and IT. But in thinking about the business analyst role, what's unique about designer cloud and how does it help these individuals?  You know, really, as they look to take the next step, they're trying to figure out, how do I give access to that, those types of analytics to thousands of people within the organization. And designer cloud is really great for that. You've got the browser-based interface. So if folks are on a Mac, they can really easily just, you know, pop open the browser and get access to all of those prep and blend capabilities to a lot of the analysis we're doing. It's a great way to scale up access to the analytics and start to put it in the hands of really anyone in the organization, not just those highly skilled power users. Okay, great. So now then you add in the Hyperana acquisition. So now you're targeting the business user. Trifacta comes into the mix, that deeper IT angle that we talked about. How does this all fit together? How should we be thinking about the new Altrix portfolio? Yeah, I mean, I think it's pretty exciting. You know, when you think about democratizing analytics and providing access to all these different groups of people, you've not been able to do it through one platform before. You know, it's not going to be one interface that meets the needs of all these different groups within the organization. You really do need purpose-built, specialized capabilities for each group. And finally today with the announcement of the Altrix Analytics Cloud, we've brought together all of those different capabilities, all of those different interfaces into a single end-to-end application. So really finally delivering on the promise of providing analytics to all. How much of this have you been able to share with your customers and maybe your partners? I mean, I know all of this is fairly new, but have you been able to get any feedback from them? What are they saying about it? I mean, it's pretty amazing. We ran an early access and limited availability program. That led us put a lot of this technology in the hands of over 600 customers over the last few months. So we have gotten a lot of feedback.  I think organizations are really excited to unlock the insights that have been hidden in all this data they've got. They're excited to be able to use analytics in every decision that they're making so that the decisions they have are more informed and produce better business outcomes. And this idea that they're going to move from dozens to hundreds or thousands of people who have access to these kinds of capabilities I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. That's good. Those are good numbers for preview mode. Let's talk a little bit about vision. So if democratizing data is the ultimate goal, which frankly has been elusive for most organizations. Over time, how's your cloud going to address the challenges of putting data to work across the entire enterprise? Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes. And these are really kind of enduring themes that you're going to see us making investments in over the next few years. The first is having cloud centricity. The data gravity has been moving to the cloud. We need to be able to provide access to be able to ingest and manipulate that data to be able to write back to it to provide cloud solutions. So the first one is really around cloud centricity. The second is around big data fluency. Once you have all of that data, you need to be able to manipulate it in a performant manner. So having the elastic cloud infrastructure and in database processing is so important. The third is around making AI a strategic advantage. So getting everyone involved in accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. And then the fourth thing is really providing access across the entire organization, IT and data engineers, as well as business owners and analysts. So cloud centricity, big data fluency, AI is a strategic advantage, and personas across the organization are really the four big themes you're going to see us working on over the next few months and coming years. That's good. Thank you for that. So on a related question, how do you see the data organizations evolving? I mean, traditionally, you've had monolithic organizations very specialized, or I might even say hyper-specialized roles. And your mission, of course, is the customer, you and your customers, they want to democratize the data. And so it seems logical that domain leaders are going to take more responsibility for data life cycles, data ownership, low code, becomes more important. And perhaps this kind of challenges the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving and what role will altrix play? Yeah. You know, I think we'll see sort of a more federated system start to emerge. Those centralized groups are going to continue to exist. But they're going to start to empower in a much more decentralized way the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized, highly skilled teams work on problems that are of higher value to the organization. There are kinds of problems where one or 2% lift in the model result in millions of dollars a day for the business. And then by pushing some of the analytics out to closer to the edge and closer to the business, you'll be able to apply those analytics in every single decision. So we're going to see both the decentralized and centralized models start to work in harmony in a little bit more of a almost a federated sort of way. And I think the exciting thing for us at Altrix is we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better and drive business outcomes with the analytics they're using. I mean I think my take on it, I wonder if you could comment, is to me the technology should be an operational detail. And it has been the dog that wags the tail or maybe the other way around. You mentioned digital exhaust before. I mean essentially it's digital exhaust coming out of operational systems that then somehow eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, those line of business experts get more access. That's your goal. And then even go beyond analytics, start to build data products that could be monetized. And maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications and to enable somebody who's an analyst or business user to create an application on top of the data and analytics layers that they have. Really to help democratize the analytics, to help prepackage some of the analytics that can drive more insight. So I think that's definitely a trend we're going to see more of. And to answer your point, if you confederate the governance and automate that, then that can happen. I mean that's a key part of it obviously. All right Jay, we have to leave it there. Up next we take a deep dive into the Altrix recent acquisition of Trifactor with Adam Wilson, who led Trifactor for more than seven years and Suresh Vitao as the Chief Product Officer at Altrix to explain the rationale behind the acquisition and how it's going to impact customers. Keep it right there. You're watching the Cube.  It's go time. Get ready to accelerate your data analytics journey with a unified cloud native platform that's accessible for everyone on the go. From home to office and everywhere in between. Effortless analytics to help you go from ideas to outcomes in no time. It's your time to shine. It's Altrix Analytics Cloud Time. Okay, we're here with Suresh Vitao, who's the Chief Product Officer at Altrix and Adam Wilson, the CEO of Trifactor, now of course a part of Altrix. Just close this quarter. Gentlemen, welcome. Great to be here. Okay, Suresh, let me start with you. In my opening remarks I talked about Altrix's traditional position serving business analysts and how the Hyperana acquisition brought you deeper into the business user space. What does Trifactor bring to your portfolio and why did you buy the company? Thank you for the question. We see a massive opportunity of helping brands democratize the use of analytics across their business. Every knowledge worker, every individual in the company should have access to analytics. It's no longer optional as they navigate their businesses. With that in mind, we know designer and the products that Altrix has been selling the past decade or so do a really great job addressing the business analysts. With Hyperana now kind of renamed Altrix Auto Insights, we even speak with the business owner, the line of business owner, who's looking for insights that aren't revealed in traditional dashboards and so on. But we see this opportunity of really helping the data engineering teams and IT organizations to also make better use of analytics. And that's where Trifactor comes in for us. Trifactor has the best data engineering cloud in the planet. They have an established track record of working across multiple cloud platforms and helping data engineers do better data pipelining and work better with this massive kind of cloud transformation that's happening in every business. And so Trifactor made so much sense for us. Yeah, thank you for that. I mean, look, you could have built it yourself would have taken, you know, who knows how long. So definitely a great time to market move. Adam, I wonder if we could dig into Trifactor some more. I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well on the Cube. Coming at the problem of taking data from raw refined to an experienced point of view. And Joe in the early days talked about flipping the model and starting with data visualization, something Jeff Heller was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you maybe and some others changed that model with, you know, EL and then T explain how Trifactor really changed the data engineering game. Yeah, that's exactly right, Dave. And it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research at Berkeley and Stanford that really broke Trifactor was, you know, why is it that the people who know the data best can't do the work? You know, why has this become the exclusive purview of the highly technical? And, you know, can we rethink this and make this a user experience problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those. So a broader set of users can really see for themselves and help themselves. And I think that there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data driven. And then the whole time they're saying, well, then give me the data, you know, in the shape that I can use it with the right level of quality and I'm happy to be, but don't tell me to be more data driven and not empower me to get in there and to actually start to work with the data in meaningful ways.  And I think as we saw over the course of the last five, six, seven years that, you know, a real excitement to embrace this idea of trying to think about data engineering differently, trying to democratize the ETL process and to also leverage all these exciting new engines and platforms that are out there that allow for, you know, processing, you know, ever more diverse data sets, ever larger data sets in new and interesting ways. And that's where a lot of the push down or the ELT approaches that, you know, I think have really won the day. And that for us was a hallmark of the solution from the very beginning. Yeah, this is a huge point that you're making. This is, first of all, this is a large business. It's probably about a hundred billion dollar TAM. And the point you're making, we've, look, we've contextualized most of our operational systems, but the big data pipeline hasn't gotten there. But maybe we could talk about that a little bit because democratizing data is nirvana, but it's been historically very difficult. You've got the number of companies that's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altrix as you bring these puzzle pieces together? How is this going to impact your customers? Who would like to take that one? Yeah, maybe I'll take a crack at it and Adam will add on. You know, there hasn't been a single platform for analytics automation in the enterprise, right? People have relied on different products to solve kind of smaller problems across this analytics automation, data transformation, and domain. And I think uniquely Altrix has that opportunity. We've got 7,000 plus customers who rely on analytics for data management, for analytics, for AI and ML, for transformations, for reporting and visualization, for automated insights and so on. And so by bringing TriFactor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where analytics gets applied, and serve multiple personas. And we just talked about the data engineers. They are really a growing stakeholder in this transformation of data analytics. Yeah, good. Maybe we can stay on this for a minute because you're right. You're right. You're bringing together now at least three personas, the business analyst, the end user, business user, and now the data engineer, which is really, Adam, an IT role in a lot of companies. And you've used this term, the data engineering cloud. What is that? How is it going to integrate with or support these other personas? And how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? Yeah, that's great. I think for us, we really looked at this and said, we want to build an open and interactive cloud platform for data engineers to collaboratively profile, pipeline, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is a big reason why this combination is so magic, because ultimately, if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that, what I would describe as collaborative curation of the data together so that you're starting to see increasing returns to scale as this rolls out. I just think that is an incredibly powerful combination. And frankly, something that the market has not cracked the code on yet. And so I think when I sat down with Suresh and with Mark and the team at Altrix, that was really part of the big idea, the big vision that was painted and got us really energized about the acquisition and about the potential of the combination. Yeah, and you're really, you're obviously riding the cloud and the cloud native wave. And but specifically, we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, Snowflakes doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right? It's simplified, get there fast, don't necessarily have to go through the central organization to share data. And but it's really all about simplification, right? Isn't that really what the democratization comes down to? Yeah, it's simplification and collaboration, right? I don't want to, I want to kind of just what Adam said, resonates with me deeply. Analytics is one of those massive disciplines inside an enterprise that's really had the weakest of tools and weakest of interfaces to collaborate with. And I think truly this was Altrix's end of superpower was helping the analysts get more out of their data, get more out of the analytics. Like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, understanding data models better. I think curating those insights, I'm boring Adam's phrase again. I think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. So how should we think about designer cloud, which is from Altrix, it's really been the on-prem and the server desktop offering. And of course trifecta is about cloud, cloud data warehouses, right? How should we think about those two products? Yeah, I think you should think about them and as very complimentary, right? Designer cloud really shares a lot of DNA and heritage with designer desktop. The low-code tooling and the interface that really appeals to the business analysts and gets a lot of the things that they do well. We've also built it with interoperability in mind, right? So if you started building your workflows in designer desktop, you want to share that with designer cloud, we want to make it super easy for you to do that. And I think over time now we're only a week into this alliance with trifecta. I think we have to get deeper inside to think about what does the data engineer really need, what business analysts really need, and how to design a cloud and trifecta really support both of those requirements while we can continue to build on the trifecta, on the amazing trifecta cloud platform. I was just going to say, I think that's one of the things that creates a lot of opportunity as we go forward because ultimately, trifecta took a platform first mentality to everything that we built. So thinking about openness and extensibility and how over time people could build things on top of trifecta that are a variety of analytic tool chain or analytic applications. And so when you think about Altrix now starting to move some of its capabilities or to provide additional capabilities in the cloud, trifecta becomes a platform that can accelerate all of that work and create a cohesive set of cloud-based services that share a common platform and that maintains independence because both companies have been fiercely independent in really giving people choice. So making sure that whether you're picking one cloud platform or another, whether you're running things on the desktop, whether you're running in hybrid environments that no matter what your decision, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse, transform, shape, structure that data and ultimately to deliver the analytics that you need. And so I think in that sense, this again is another reason why the combination fits so well together, giving people the choice and as they think about their analytics strategy and their platform strategy going forward. You know, I make you chuckle, but one of the reasons I always liked Altrix is because you kind of did a little end run on IT. IT can be a blocker sometimes, but that created problems. Right, because organizations said, wow, this big data stuff has taken off, but we need security. We need governance. And it was interesting because you got, you know, ETL has been complex, whereas the visualization tools, they really weren't great at governance and security, it took some time there. So that's not their heritage. You're bringing those worlds together. And I'm interested. You guys just had your sales kickoff. You know, what was their reaction like? Maybe Suresh, you could start off and maybe Adam, you could bring us home. Yeah, thanks for asking about our sales kickoff. So we met for the first time in kind of two years, right? As it is for many of us. In person, which I think was a real breakthrough as Altrix has been on its transformation journey. We had a trifecta to the party such as it were and getting all of our sales teams and product organizations to meet in person in one location. I thought that was very powerful for us as a company. But then I tell you, the reception for trifecta was beyond anything I could have imagined. We were working, Adam and I were working so hard on on the deal and the core hypotheses and so on. And then you step back and you kind of share the vision with the field organization. And it blows you away the energy that it creates among our sellers, our partners. I'm sure Adam and his team were mocked every single day with questions and opportunities to bring them in. But Adam, maybe you should share. Yeah, no, it was through the roof. I mean, the amount of energy, certainly how welcoming everybody was. I think the story makes so much sense together. I think culturally the companies are very aligned. And it was a real capstone moment to be able to complete the acquisition and to close and announce at the kickoff event. And I think for us, when we really thought about it, and the story that we told was just you have this opportunity to really cater to what the end users care about, which is a lot about interactivity and self-service. And at the same time, and that's a lot of the goodness that Altrix has brought through years and years of building a very vibrant community of hundreds of thousands of users. And on the other side, Trifactive bringing in this data engineering focus that's really about the governance things that you mentioned and the openness that IT cares deeply about. And all of a sudden now you have a chance to put that together into a complete story where the data engineering cloud and analytics automation come together. And I just think the lights went on for people instantaneously. I think the market is really hungry for and certainly the reception we got from the broader team at kickoff was a great indication of that. Well, I think the story hangs together really well. One of the better ones I've seen in this space and you guys coming off a really strong quarter. So congratulations on that. Jens, we have to leave it there. Really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the Cube, your leader in enterprise tech coverage. This is your data housed neatly and securely in the Snowflake data cloud. And all of it has potential, the potential to solve complex business problems, deliver personalized financial offerings, protect supply chains from disruption, cut costs, forecast, grow and innovate. All you need to do is put your data in the hands of the right people and give it an opportunity. Luckily for you, that's the easy part because Snowflake works with Alteryx and Alteryx turns data into breakthroughs. With just a click, your organization can automate analytics with drag-and-drop building blocks. Easily access Snowflake data with both SQL and NoSQL options. Share insights powered by Alteryx data science. And push processing to Snowflake for lightning fast performance. You get answers you can put to work and your teams get repeatable processes they can share. And that's exciting because not only is your data no longer sitting around in silos, it's also mobilized for the next opportunity. Turn your data into a breakthrough. Alteryx and Snowflake. Okay, we're back here in the Cube focusing on the business promise of the cloud democratizing data, making it accessible and enabling everyone to get value from analytics, insights and data. We're now moving into the ecosystem segment. The power of many versus the resources of one and we're pleased to welcome Barb Hulskamp who's the senior vice president, partners in alliances at Alteryx and a special guest, Tarek DeWeek, head of technology alliances at Snowflake. Folks, welcome. Good to see you. Thank you. Thanks for having me. Good to see you, Dave. Great to see you guys. So cloud migration, it's one of the hottest topics. It's the top, one of the top initiatives of senior technology leaders. We have survey data with our partner ETR. It's number two behind security and just ahead of analytics. So we're hovering around all the hot topics here. Barb, what are you seeing with respect to customer, you know, cloud migration momentum and how does the Alteryx partner strategy fit? Yeah, sure. Partners are central company strategy. They always have been. We recognize that our partners have deep customer relationships. And when you connect that with their domain expertise, they're really helping customers on their cloud and business transformation journey. We've been helping customers achieve their desired outcomes with our partner community for quite some time and our partner base has been growing an average of 30% year over year. That partner community and strategy now addresses several kinds of partner spanning solution providers to global SIs and technology partners such as Snowflake. And together we help our customers realize the business promise of their journey to the cloud. Snowflake provides a scalable storage system. Alteryx provides the business user friendly front end. So for example, IT departments depend on Snowflake to consolidate data across systems into one data cloud. With Alteryx business users can easily unlock that data in Snowflake solving real business outcomes. Our GSI and solution provider partners are instrumental in providing that end-to-end benefit of a modern analytics stack in the cloud, providing platform guidance, deployment support and other professional services. Great, let's get a little bit more into the relationship between Alteryx and Snowflake, the partnership, maybe a little bit about the history. One of the critical aspects that we should really focus on, Barb, maybe you could start an enteric kindly way in as well. Yeah, so the relationship started in 2020 and Alteryx made a big battle deep with Snowflake co-innovating and optimizing cloud use cases. Together we are supporting customers who are looking for that modern analytics stack to replace an old one or to implement their first analytic strategy. And our joint customers want to self-serve with data-driven analytics leveraging all the benefits of the cloud, scalability, accessibility, governance and optimizing their costs. Alteryx proudly achieves Snowflake's highest elite tier in their partner program last year. And to do that, we completed a rigorous third-party testing process, which also helped us make some recommended improvements to our joint stack. We wanted customers to have confidence. They would benefit from high quality and performance in their investment with us. Then, to help customers get the most value out of this joint solution, we developed two great assets. One is the Alteryx starter kit for Snowflake and we co-authored a joint best practices guide. The starter kit contains documentation, business workflows and videos helping customers to get going more easily with an Alteryx and Snowflake solution. And the best practices guide is more of a technical document bringing together experiences and guidance on how Alteryx and Snowflake can be deployed together. Internally, we also built a full-enablement catalog of resources. We wanted to provide our account executives more about the value of the Snowflake relationship, how to engage and some best practices. And now we have hundreds of joint customers such as Juniper and Sainsbury who are actively using our joint solution solving big business problems much faster. Cool. Tara, can you give us your perspective on the partnership? Yeah, definitely Dave. So, as Bart mentioned, we've got this long-standing, very successful partnership going back years with hundreds of happy joint customers. And when I look at the beginning, Alteryx has helped pioneer the concept of self-service analytics, especially with use cases that we worked on for data prep for BI users like Tableau. And as Alteryx has evolved from data prep to now becoming a full end-to-end data science platform, it's really opened up a lot more opportunities for a partnership. Alteryx has invested heavily over the last two years in areas of deep integration for customers to fully be able to expand their investment in both technologies. And those investments include things like a large-scale space push-down, right? So customers can leverage that elastic platform, that being the Snowflake Data Cloud, with Alteryx orchestrating the end-to-end machine learning workflows. Alteryx also invested heavily in Snowpark, a feature we released last year around this concept of data programmability. So all users, regardless of their business analysts, regardless of their data scientists, can use their tools of choice in order to consume and get at data. Alteryx Cloud, we think it's going to open up even more opportunities. It's going to be a big year for the partnership.  And Cloud Data Warehouse changed all that because you had virtually infinite resources. So that's obviously one of the problems that you guys solved early on. But what are some of the common challenges or patterns or trends that you see in Snowflake customers? And where does Alteryx come in? Sure, Dave, there's a handful that I can come up with today that are big challenges or trends for us. And Alteryx really helps us across all of them. There are three particular ones I'm going to talk about. The first one being self-service analytics. If we think about it, every organization is trying to democratize data. Every organization wants to empower all their users, business users, the technology users, but the business users. I think every organization has realized that if everyone has access to data and everyone can do something with data, it's going to make them competitively, give them a competitive advantage. With Alteryx and Snowflake, we share that vision of putting that power in the hands of everyday users, regardless of the skill sets. So with self-service analytics, with Alteryx Designer, they started out with self-service analytics as the forefront. And we're just scratching the surface. I think there was an analyst report that shows that less than 20% of organizations are truly getting self-service analytics to their end users. Now with Alteryx going to Alteryx Cloud, we think that's going to be a huge opportunity for us. And then that opens up the second challenge, which is machine learning and AI. Every organization is trying to get predictive analytics into every application that they have in order to be competitive. And with Alteryx creating this platform so they can cater to both the everyday business user, the quote-unquote citizen data scientist, and making it code friendly for data scientists to be able to get at their notebooks and all the different tools that they want to use, they fully integrated in our Snowpark platform, which I talked about before, so that now we get an end-to-end solution catering to all lines of business. And then finally, this concept of data marketplaces. We created Snowflake from the ground up to be able to solve the data sharing problem, the big data problem, the data sharing problem. And Alteryx, if we look at mobilizing your data, getting access to third-party data sets to enrich with your own data sets, to enrich with your suppliers and with your partner's data sets, that's what all customers are trying to do in order to get a more comprehensive 360 view within their data applications. And so with Alteryx, we're working on third-party data sets and marketplaces for quite some time. Now we're working on how do we integrate what Alteryx is providing with the Snowflake data marketplace so that we can enrich these workflows, these great, great workflows that Alteryx already provides. Now we can add third-party data into that workflow. So that opens up a ton of opportunities, Dave. So those are three I see easily that we're going to be able to solve a lot of customer challenges with. Excellent. Thank you for that, Tarek. So let's stay on cloud a little bit. I mean, Alteryx is undergoing a major transformation, big focus on the cloud. How does this cloud launch impact the partnership Tarek from Snowflake's perspective? And then, Barb, maybe please add some color. Yeah, sure, Dave. Snowflake started as a cloud data platform. We saw, our founders really saw the challenges that customers were having with becoming data-driven. And the biggest challenge was the complexity of having a managing infrastructure to even be able to get applications off the ground. And so we created Snowflake to be cloud-nated. We created to be a SaaS managed service. So now that Alteryx is moving to the same model, right? A cloud platform, a SaaS managed service, we're just removing more of the friction. So we're going to be able to start to package these end-to-end solutions that are SaaS based, that are fully managed so customers can go faster. They don't have to worry about all of the underlying complexities of stitching things together, right? So that's what's exciting from my viewpoint. Yeah, and I'll follow up. So as you said, we're investing heavily in the cloud. A year ago, we had two private desktop products, and today we have four cloud products. With cloud, we can provide our users with more flexibility. We want to make it easier for the users to leverage their Snowflake data in the Alteryx platform, whether they're using our beloved on-premise solution or the new cloud products. We're committed to that continued investment in the cloud, enabling our joint partner solutions to meet customer requirements wherever they store their data. And working with Snowflake, we're doing just that. So as customers look for a modern analytic stack, they expect that data to be easily accessible within a fast, secure, and scalable platform. And the launch of our cloud strategy is a huge leap forward in making Alteryx more widely accessible to all users in all types of roles. Our GSI and our solution provider partners have asked for these cloud capabilities at scale, and they're excited to better support our customers' cloud and analytic ambitions. Bob, how about your go-to-market strategy? How would you describe your joint go-to-market strategy with Snowflake? Sure. It's simple. We've got to work backwards from our customers' challenges, right? Driving transformation to solve problems, game chancies, or help them save money. So whether it's with Snowflake or other GSI's, other prototypes, we've outlined a joint journey together from recruit, solution development, activation, and enablement, and then strengthening our go-to-market strategies to optimize our results together. We launched an updated partner program, and within that framework, we've created new benefits for our partners around opportunity registration, new role-based enablement and training, basically extending everything we do internally for our own go-to-market teams to our partners. We're offering partner marketing resources and funding to reach new customers together. And as a matter of fact, we recently launched a fantastic video with Snowflake. I love this video. That very simply describes the path to insight starting with your Snowflake data, right? We do joint customer webinars. We're working on joint hands-on labs and have a wonderful landing page with a lot of assets for our customers. Once we have an interested customer, we engage our respective account managers, collaborating through discovery questions, proof of concepts, really showcasing the desired outcome. And when you combine that with our partner's technology or domain expertise, it's quite powerful. Tariq, how do you see it, your go-to-market strategy? Yeah, Dave, we've... So we initially started selling, we initially sold Snowflake as technology, right? Looking at positioning the architectural differentiators and the scale and concurrency. And we noticed as we got up into the larger enterprise customers, we were starting to see how do they solve their business problems using the technology, as well as them coming to us and saying, look, we want to also know how to continue to map back to the specific prescriptive business problems we're having. And so we shifted to an industry focus last year. And this is an area where Altrix has been mature for probably since their inception selling to the line of business, right? Having prescriptive use cases that are particular to an industry like financial services, like retail, like healthcare and life sciences. And so Barb talked about these starter kits where it's prescriptive. You've got a demo and a way that customers can get off the ground and running, right? Because we want to be able to shrink that time to market, that time to value that customers can launch these applications. And we want to be able to tell them specifically how we can map back to their business initiatives. So I see a huge opportunity to align on these industry solutions. As Barb mentioned, we're already doing that where we've released a few around financial services. We're working on healthcare and retail as well. So that is going to be a way for us to allow customers to go even faster and start to map to the lines of business with Altrix. Thanks, Tarek. Barb, what can we expect if we're observing this relationship? What should we look for in the coming year? A lot. Specifically with Snowflake, we'll continue to invest in the partnership. We're co-innovators in this journey, including Snowpark Extensibility Efforts, which Tarek will tell you more about shortly. We're also launching these great new strategic solution blueprints and extending that at no charge to our partners. With Snowflake, we're already collaborating with their retail and CPG team for industry blueprints. We're working with their data marketplace team to highlight solutions working with that data in their marketplace. More broadly, as I mentioned, we're relaunching the Altrix Partner Program, designed to really better support the unique partner types in our global ecosystem, introducing new benefits so that with every partner achievement or investment with Altrix, we're providing our partners with earlier access to benefits. I could talk about our program for 30 minutes. I know we don't have that kind of time, but the key message here, Altrix is investing in our partner community across the business, recognizing the incredible value that they bring to our customers every day. Great. Tarek, we'll give you the last word. What should we be looking for from this part? Thanks, Dave. As Barb mentioned, Altrix has been the forefront of innovating with us. They've been integrating into making sure, again, that customers get the full investment out of Snowflake, things like in-database pushdown that I talked about before. But extensibility is really what we're excited about. The ability for Altrix to plug in to this extensibility framework that we call Snowpark. And to be able to extend out ways that the end users can consume Snowflake through SQL, which has traditionally been the way that you consume Snowflake, as well as Java and Scala and now Python. So we're excited about those capabilities. And then we're also excited about the ability to plug into the data marketplace to provide third-party datasets, right? Third-party datasets in financial services, third-party datasets in retail. So now customers can build their data applications from end to end using Altrix on Snowflake with a comprehensive 360 view of their customers, of their partners, of even their employees, right? I think it's exciting to see what we're going to be able to do together with these upcoming innovations. Great stuff. Barb, Tarek, thanks so much for coming on the program. We've got to leave it right there. In a moment, I'll be back with some closing thoughts in a summary. Don't go away. 1,200 hours of wind tunnel testing. 30 million race simulations. 2.4-second pit stops. Make that 2.3. Sector times out the wazoo. Way too much of this. Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Altrix. Altrix, analytics automation. OK, let's summarize and wrap up the session. We can pretty much agree that data is plentiful, but organizations continue to struggle to get maximum value out of their data investments. The ROI has been elusive. There are many reasons for that, complexity, data trust, silos, lack of talent and the like. But the opportunity to transform data operations and drive tangible value is immense. Collaboration across various roles and disciplines is part of the answer as is democratizing data. This means putting data in the hands of those domain experts that are closest to the customer and really understand where the opportunity exists and how to best address them. We heard from Jay Henderson that we have all this data exhaust and cheap storage. It allows us to keep it for a long time. It's true, but as he pointed out, that doesn't solve the fundamental problem. Data is spewing out from our operational systems, but much of it lacks business context for the data teams chartered with analyzing that data. So we heard about the trend toward low code development and federating data access. The reason this is important is because the business lines have the context and the more responsibility they take for data, the more quickly and effectively organizations are going to be able to put data to work. We also talked about the harmonization between centralized teams and enabling decentralized data flows. I mean, after all, data by its very nature is distributed. And importantly, as we heard from Adam Wilson and Suresh Vital, to support this model, you have to have strong governance and service the needs of IT and engineering teams. And that's where the trifecta acquisition fits into the equation. Finally, we heard about a key partnership between Altrix and Snowflake and how the migration to cloud data warehouses is evolving into a global data cloud. This enables data sharing across teams and ecosystems and vertical markets at massive scale, all while maintaining the governance required to protect the organizations and individuals alike. This is a new and emerging business model that is very exciting and points the way to the next generation of data innovation in the coming decade. Where decentralized domain teams get more facile access to data, self-service take more responsibility for quality, value, and data innovation. While at the same time, the governance, security, and privacy edicts of an organization are centralized and programmatically enforced throughout an enterprise and an external ecosystem. This is Dave Vellante. Remember, all these videos are available on demand at thecube.net and altrix.com. Thanks for watching Accelerating Automated Analytics in the Cloud, made possible by Altrix. And thanks for watching The Cube, your leader in enterprise tech coverage. We'll see you next time.