S5 E3: Byte: Technologies that pair well ish with Tableau

Ravi we're back once more we didn't fail Our obligation to stop for another what nine months nine months yeah the gaps are getting shorter and that's what we're they are getting shorter the good thing about this Gap is we actually uh pulled through um you were traveling right so we we we actually couldn't record you're in a in a land far away where all internet is banned the internet's banned and everything is watched um no it's it's always fun to visit these places CU you realize I guess some of the pros and the cons so yeah that that was it was good fun um but yeah we missed our first recording date yeah um and then yeah we've been trying to find whereever since I think we we we were messaging about this episode on the 15th June a month ago exct literally a month ago yeah we were supposed to record this a month ago right before you went abroad that was the deadline and then I became m and uh sorry like kids like just life happened and we were pushing it like too close and then I just said ah no let's let's do it when you get back and then I didn't football didn't come home yeah football didn't come home yeah I was are you wearing a football shirt no you're not wearing it's Puma Puma I thought it might have been a football um yeah football did not come home for all the but it came home last it came home in 2020 it came home in 2021 when the women won so um do you know who won copper America I think that's going on time right Argentina there you go was Messi playing Messi was injured in the first half so he came off early coincidence okay right yeah and I think he's done for international football right oh I saw a picture of his ankle did not look good um but fortunately this isn't a football podcast good it is not no the rest is not football uh thank God yeah um so yeah no we thought we'd talk today about technologies that people use alongside Tableau obviously Tableau is are bread and butter it's our it's our home turf as it were to carry the football analogy on um but we're taking some trips to the away teams and we're going to talk about things that you might see paired with Tableau I think I will preface this by saying look we are not uh expert at these Technologies so we kind of wanted to have this open discussion as an initial Gambit um to just talk between us um understand what we perceive uh these Technologies to be but obviously we're going to touch a on what they actually do but we'd like to invite you the audience to um essentially get involved let us know in the comments I love my comment section people are so passionate I the the one thing I love about the YouTube channel is the people who are so polite like this fills me with so much joy they they open a sentence was like Tim I absolutely love this video but and then they put a time stamp like put like St St seconds and I'm I'm always like man you watch till the end man you you got my heart and then they cut into me they're like you got this wrong and this wrong and this wrong and at the end they're like but I really love what you're doing man thank you so much keep doing what you're doing and I love that I love that it is that stuff that keeps me going so look if that is you um please engage with us on this podcast um we are going to try and keep to a time limit because we've got a long list here and yeah I know Ravi and I are fantastic are just talking endlessly so we which is why we started the podcast in the first place right exactly exactly so what we're going to try and do i' got I've got my iPhone here I'm just going to kind of pull up the timer app on my um yeah on my phone and we're going to set a timer I think four minutes maybe five minutes is more um we can be flexible with that as as Tim said I think you know to that point that's there's that stanus bar think anything before the word but is meaningless um so so the sandwiches you're getting sent to you on on your comments but no genuinely like if if um old Trix Adam or DBT Dave is out there um ready to start a YouTube channel to Aral Damon yeah exactly a Col of mine a colleague of mine at endpoint did come up with data Brick Mason I was like that's very good that's very good data Brick Mason they should they should definitely get on the that um but talking of data breaks that is going to be our first one so let me set a timer here five minutes um right data bricks what is data bricks um I I kind of describe data bricks as a data platform rather than a database would that be fair um yeah but it can do database things I think this is where people get struggle with right you can have Delta lakes and um lake houses within your data data bricks platform but I think it's the biggest benefit is you can do a lot of things with it and probably the most important is it's built on spark so Pat spark is just allows for Mass parallelization yeah yeah that gives you speed it's kind of like I always think of it as they they started their whole platform at the sweet time when Ai and machine learning and data science was actually sort of coming along will come to snowflake later snowflake started too early to really capitalize on that sort of core core core structure maybe that's an opinion by the way it's not it's the applied side I think that that's what data brick wrote they wrote area of like being able to actually apply and give you examples of where it can solve your problem okay this is a problem that you have this is how this solves it and this is how it gets around it um and you know there's there's actually quite a common meme going around the social media platforms right now of like what does Salesforce do like can someone explain what Salesforce is and what it does yeah and this is almost what you felt with with a platform like SL we'll come back to SL obviously yeah but these platforms and I think as you as said the timing of data bricks to come almost emerge onto the scene and say we're a platform but more importantly we're a deaggregated platform you can host us anywhere we can connect into anything and we can connect out into anything we're just the people in the middle that allow you to do things quite cleanly um and they kind of realize the role of the warehouse in that right they kind of they kind of I think what's the word they reimagine the warehouse in a in a very slightly different way to snowflake um same sort of Direction slightly different execution leads to much much sort of deeper integration and I think it's definitely got sort of the hearts and minds of um a lot of companies because of that slightly more integrated more sort of disaggregated sort of nature you talk about so um super interesting um have you used the Ravi yeah yeah I've used it it's um I think the nice thing is you can write notebooks where you start off in Python do a bit of SQL tack on a bit of R and then execute it all as part of a wider Warehouse query um like a data Factory query or you can call on the notebook via a API so would you say the learning curve is steep because of all those prerequisites you talked about R python if you've not really sort of dabbled in let's say I'm going to say data science generally but also if you've not been a multidisiplinary m i can't say that multidisiplinary mulinari that's the one m multi-disciplinarian in your approach to learning analytics it could be quite a steep learning C because you've got a lot of I would say front-facing things to learn and principles as well it's not just each of those things it's how do they all come together right I'd say that's that's me right like I I don't I don't see myself as a data scientist and I don't I I didn't understand how a I can't do a v look up or an X lookup and I don't understand how pivot tables work until some AI is coming for your job mate exactly um but I didn't really understand it and then I think being able to explain in you know the fact that this thing does this this thing does this this thing does this is quite clear but yeah I think I agree with you um there's a lot that you can do with a platform like data bricks that you don't really get into like you can just use surface level here's my data Lake I want to query it and I'm going to get out but you can go really deep into it as well with orchestration apis it's it's a technology that I I feel like I I need to find a good use case for in the future I don't feel like I've for a hobbyist right like this is purely Enterprise scalable things that just have to be constantly running and updating there's no Tableau public for data breaks here like no that's called just it's just python or Jupiter yeah exactly exactly so I I think that's um a pretty good touch on the data bricks I think if we were to talk more about its integration with Tableau they announced a new connector Delta connectors um literally live as of a couple of days ago so that's a really good way of connecting to um to data bricks I think the Vantage there is that it it it it should make that easier because previously was quite hard right yeah it makes it easier for sure and like it's an exciting capability that you can tap into but in order to set up you need a bunch of there's my timer um in order to set up you need a bunch of like the relevant information and key so you might need to actually reach out to your uh administrator which if you're a you know data bricks user within a large Enterprise environment you might not know who that is um so this is where you get end up in that weird moment where you know Tabo used to live of being Shadow bi and maybe you end up with Shadow it to try and make your Delta L connect to work the the other one that I was kindly reminded of uh when speaking to him at the Tableau conference by Thomas and Han was uh the table extension which we talked about really early on we've got a previous part on this actually but we'll come back to that yeah yeah and and this can link into and call on data bricks models so you have a model in data bricks you can call on it and extend your data tables um and again that is a super powerful the iceberg for data Bri is is Big yeah I I think that's sort of one of the challenges with data brecks potentially it breaks away from the Tableau mold and might be slightly tougher because it does it does require you to look beyond the the dragon drop like the the the sort of UT hope you that Tableau presents you this this is not Mak you think yeah you have to get deep into that this is not for the like drag and drop sort of Enthusiast it's not for the you know window Cal dabler this is this is like a hardcore tool a little bit yeah but when you if you got a great data engineering team um they're probably using this yeah they they will be able to like understand your query and then help you translate it so you just deal with very pretty yeah uh Del tables that you just connect to and refresh as you normally would and decent magic we we then end up living in a Rosy environment right the timer went off a minute ago we went over so we clearly have a passion for data brecks let me reset the timer started again let's go to snowflake because we brought it up again um as we were talking about that um snowflake I feel was right at the curve and I just want to call out you pointed out snowflake to me I don't know I can't literally at this point like TC 17 TC 17 at the tobacco dock you actually took me to their stand and we watched the demo and I stood there and I was like I just don't get it what is it like I don't get it and you explained to me really really well and what it what it what it shows to me is that I just wasn't far along in my Learning Journey to really appreciate it for what it was at the time so that that was the second time I think I I I took you to their store twice the first time I was like I I've heard this thing and I think it does this let's go ask the guy and I think we were at TC in Vegas and we went there again I think we both left that being I still don't know why I should use it over a normal database yeah right I get it but I just don't I think there's a really small niche of people that this is really useful for and that's just people that don't yeah there's just a small a niche at the time but I'll also say this since then they've massively built on their platform in a way that you know having also being the one who's like I don't get it and now have a course on LinkedIn introducing people to it so they've massively built on the platform massively in a way that I think they do come in competition with data bricks for a lot of what I would say the same type of workloads with data not specifically database workloads necessarily but the same types of work the same types of computations um but they have built they really like the way I think of data bricks is that data bricks have built um a platform you deploy it how you want where you want snowflake they built a software they're running it on a platform AWS and deploy it for you comes in multi flavors multi flavors but it has to be on a a web platform so it's very much like a SAS vers this is a bad description don't don't come after me but it's very much a sass version of what data bricks is trying to do right like it's kind of the a lay that abstracts the hard work of doing that but really people just want to get like um close to the to the metal as it were so anyway um how quickly can I find this book in my library when I want it and access it read it quickly and then put it back take this out do stuff put it back and then it's like at point of query or point of access you'll only getting that doing something with it and then you it could then disappears like I don't know if you watch um Rick and Morty but there's a thing called a MI6 box and you press press a button a little thing appears you then say I want to do this it does its thing and then it disappears and that's exactly what it does that's what it does like Siri the data my auto or one of those Auto AI like Auto GPT Auto GPT where it like spins up micro anyway sorry we're going off on tangent you triggered my Siri as well and I've just triggered it again Jesus I need to there needs to be a way of disabling sir for a period of time this is this is ridiculous I'll stop saying the word so um it is interesting I think that um hold on let me just stop a second because this thing is just still recording what I'm saying this technology man I just hope Apple intelligence saves sir because that is ridiculous but okay um back to the back to snowflake thanks sir for that Interruption back to snowflake I think the other thing they've done is they've massively leaned into B2B workflows as well right like the ability the the data clear rooms the ability to share doing that at scale I'm defin like I I've come across instances of Industries centralizing on snowflake which is really good because you kind of get the economies of scale of doing all of that stuff um and then the more recent Partnerships over the last couple of years with Nvidia to start doing containers and compute clusters in the cloud as well trying to really lean into simar kind of spaces data brick coming at it from a different angle um really powerful I is there I always wonder with snif like if I I always wonder if they were too early just a tan too early if that makes sense right like I you know the world we're living in today and and and I can't really say this because if if you sort of tweak it a year or two here then they actually just to the right time but I I definitely didn't come across data bricks at the same time I came across snowflake but the rise to um oh there's our timer for snow the rise to like let's say being well known for data briak seems to be in shorter is that is that fair to say like I feel like data bricks has had like a fiveyear runup where a snowflake really or a great marketing team okay fair fair fair like shade on the snowflake team but yeah no yeah I think um the thing is when snowflake came about their competitive was exol like exol was comp for about them jeez yeah yeah exactly ex right yeah where did they so you had EX in there you had metrica metrica HP the HP one and then you still had like the fing around vertica not metrica metrica is a football company and [Music] this um fair fair yeah so a vertica yeah sap and then ex like so you're coming to a space where everything is actually about speed of query rather than like storage versus compute which is what they focus everyone focus on the like you know classic you know gigahertz megahertz race and suddenly data breaks comes along and so actually it doesn't matter as much well it does but really all these other if you just take out the process part the transform and extract bit and then just say but you just need this right go get that then and let us do this yeah pretty yeah pretty deep integration with Tableau um I know I think Tableau use SL fake Kon right really for their for their own um marketing data and uh uh clusters I keep hearing product developers talk about like obviously they keep their data in lots of different places but snowflake is their like big um the big big thing they' talked about it conferences so I'm not I'm not hopefully I'm not saying something I shouldn't be saying uh I'm referencing conferences just as like a standard point because I like if I could find it in a conference talk which I can yeah then it's public knowledge um but anyway um super interesting I I don't think I've seen as many use cases with snowflake as I thought I would have seen if that makes sense and by that I mean you know how with um data breaks you got this you know you talked about table extensions with snowflake I don't know if it's just because it's mostly within Enterprise or mostly within certain sectors like fast moving consumer goods but all these you know data clean rooms all of this stuff I haven't seen that in the Tableau world as much I don't know if it's just because I've I'm not that deeply embedded with like the pro use case for this data masking as an example like that's a really good idea for something like you know with Tableau where we come across these sort of common privacy questions all the time imagine being able to just do like a parameterized um masking operation right right inside of the query so that you're just connecting live and depending on who the user is with user um user filters or I forget the name of the role um not roles I forget the name of the feature but anyway user user attributes that's the feature so with user attributes being able to dynamically mask certain bits of information because you're able to look at the user attributes and just modify the query all that stuff look us be to do this look with lookl yeah I think lookl allowed you to parameterize and do D like which is which is hilarious right they sell them say as yeah visualization tool but the thing that you everyone always talks to you about is look ML and the fact that you can do loads of like parameterized like data filtering anyway um I think the the reason for that I would possibly argue again having less knowledge of snowflake than you is probably that deaggregation point like if you think about platforms as aggregators right like we had the period between 2014 to 2019 where like we can do everything we are a platform Just One Stop Shop and snowflake almost became that but then dats were like yeah know we're like a Christmas cracker you can open us at both ends and we'll just stay in the middle you want us over here we'll go over there if you want to go over here we'll go over there um but I don't if snowflake can do that therefore extensibility in the same way we talked about with table extensions for example might not be the case however it might snowflake sneeve sneeve snowflake Steve get in touch let let's have a chat with you on the podcast ah absolutely absolutely right let's um let's take a detour let's go to alrix very briefly very briefly well what is that to say um I'll I'll start I'll start because on then I use orri every day so um I think if you let me let rip I'll I'll go for too long but yeah I've not used Al Trix for three years now right um possibly longer um I but back in 2016 2017 if someone I think someone did ask me like if you've got to pick one of these two to back to go long is it ultr or tablet I would have got cuz there's a lot more use cases and it's a lot less crowded the field and it's really easy to use but the price point the limitation over the engine and the fact that code like almost AI would have killed it like destroyed it completely right because suddenly no code isn't that interesting or Dragon drop isn't as interesting because you can find you can have ai assistant supporting you on your code data Brick's AI assistant jumps at you when you know need it but it does solve your problem for example yeah but it's really fast and I think the inmemory for someone doing stuff locally for themselves to do let me just do this this this and this and just as I'm thinking I can drag something and drop it get the answer okay this is the scheme I want now I'm going to show this to my data engineer can I build this go productionize it but for that fast iteration incredible for the ability to implement code and automate and do batched ma growth jobs fantastic systemic jobs amazing like if you want to generate and burst PDFs and run some code and then bring it all back together yeah yes we can do this spatial what it he lives breeds and eats um but but it just didn't evolve um. next altrix server alrix cloud whatever you want to call it was just never the thing s times yeah yeah like I I add to what you said at the beginning I add to what you said at the beginning which is you know if you chose something 2016 to go long it would have been all tricks to this day if I only had one tool I could use as a data analyst like one tool I had to pick one tool I couldn't open any other data product still be all tricks because I can muscle my way through any problem in that at all and they're like maybe because I've used it for so long I've just have like I've got I'm in tuned to wrong because like data densification kills you in some parts of Tableau there times you hit the wall of you need to Pivot your data and you cannot do this unless you pivot your data and the other thing is you just you just pivot it and carry on we'll come on to this in a second because DBT is on the list right but I'll say this I have never met a tool that allows for um accelerated ability to solve Last Mile problems right like you you you you've got the data you've got it so close but there's you just come across a use case something you know all the data is right in front of you it's just not in the right format it's not in the right shape it's not in the right it's not in the right place to do the thing you need to do yeah you don't want to go back to the data team it's still alricks and DBT and all these other tools don't solve that because what they do is they put up a new hurdle a new language a new way of processing whereas alrix allows you to stay in that sort of visual frame of the data like rows and columns right and you could just get the data to way you needed to still to this day the best tool at that I know there's other players prep we'll talk about Enos as well later on but it's just it's just such a loss this this is such classic like like you know when you're just like ah but it's so good and I love it so much but it's not the one and you know it's it's hard go that is my heart talking that's my heart talk not you it's me I can fix him it's so true it's so true and we said we wouldn't talk long about this and here I am basically writing a love letter to AIT um but nonetheless listen they've not trapped up at the cloud they missed their killed them pricing stru killed pricing still kills them today still kills them today um and there there is there's just a lot of very um I think difficult difficult things for people to get if they've not used it tool before like if you're sort of introducing someone new to alrix uh that philosophy in alrix is quite hard to understand it's sort of derived from years ago but in today's world it just it just really greats against that way people think about data but I still stand by it if all they did I know it sounds simple to say if all they did was they took their core product or trick designer push it into this modern world put it in a browser for goodness sake put it on a Mac for goodness sake like bare minimum put it on a Mac bring it to data scientists in a fresh way right bring it to data analysts in a fresh way make it play nice alongside the things they already do Tableau powerbi just make it a companion into those things doesn't have to be a on own I've got I've got a killer there right imagine imagine the Simplicity of old tricks and then once you're done you can output it into a Jupiter notebook and it will do everything for you you can then push it into R it's done it for you you want it in Python here you go you want to do this all in SQL this is the the things you can do they have it in in database is literally that you do your data prep you do your thing and then there's a little thing you're saying oh would you like me to write the sequel for you D it's literally there like just lots of people describe it as a Swiss army knife just lean into that man and build the platform the cloud platform that's deserving of designer rather than trying to make the platform its own separate product anyway we need to still sponsoring McLaren are they are they still sponsoring McLaren yes absolutely a great sponsor I keep saying Lando Norris well alrs are getting great value for money because they bought that when McLaren was nowhere and I think I believe that McLaren not even in the Midfield yeah exactly probably the first two years of that sponsorship that was probably looking sketchy and then in the last year it's come through real good like the amount of times Lando Norris has been on on the podium is unbelievable and I keep looking at the jacket the sponsorship position moves the sponsor position moves it did used to be like quite prominent then it's now on the sides like in the long list of names sometimes it's on the front nose of the car so it's not got a permanent spot but it does move around so you know I'm still waiting for that race where it's like right on the front nose and like it's a race or it's a piece of debris that's flown off into the distance and you just see alter fly across the screen that is that is the slowo moment I'm waiting for something to happen and then just just see the ultra in that SLO sh brilliant um right let's go on to DBT DBT is next DBT is next um gosh I think DBT started off with an open source tool for anyone who's wondering what is DB I didn't even describe all Trix um I'm really sorry we won't go back to that Dragon drop tool makes it Dragon drop visual analys visual data cleansing prep Wizardry predictive Wizardry mapping you kind of did actually so let's just carry on DBT um I kind of the way I describe DBT is Imagine sequel but with smarts on top basically that's that's basically how I describe DBT and the big problem they have is I think they built a great open source product everyone was like this is amazing and then they tried to build the company on top of that and everything went to their Cloud platform and I think that's been like a difficult transition for people to understand right um but there's a lot of good principles like you know there's you know I think if anyone ever talks you about semantic Lair they're really ultimately talking about some of the capabilities in D DBT I know other tools do the same thing but nonetheless yeah they're talking about DBT so I mean do you use it in your work like in what you do interesting it's it's it's another one of those things that you hear the name and then you ask your trusted circle of data NS being like should I care about DBT and then they tell you yeah or no or I don't know I keep hearing it as well tell me when you find out more tell me when you find out when it's good I've used it for a project I can't talk about the project but what I I I came into this project Midway through so I like if it was my choice I wouldn't have built this on DBT because I would have built it in orrix and then I would have struggled with orrix of course I do everything in orrix there's a theme here but what was interesting about experiencing DBT and I experienced two flavors of DBT DBT core which is the open source version and DBT Cloud which is the paid version runs in the cloud my experience of cloud was fantastic really good like the lineage was H chef's kiss I was like damn this is nice like where is this in alrix like I know they had a DAT or in any other data product to have that clear version control cicd you this this this model here this python thing you've got yeah it goes here and then goes here this take a step back here's everything exactly yeah like beautiful and then the thing I said earlier on if you recall last man analytics killed me like the amount of times I got into the workbook and I was like God damn it need to go back into my model and like fiddle with this and Fiddle with that that's my wife calling me it's fine um fiddle with this fiddle with that and then I'm like oh God how did to build the whole model like Jesus I could have just written a nice beautiful like LOD right here and just solve this but no we're doing things a DBT way um uh a a a product developer we both know well who happens to work on calculations described it as uh uh communism for data sources which I which when he said it I didn't get it but like when I think about it I'm like actually nailed it absolutely nailed it so so true like I'm I'm just looking at the docs now um and any docs that automatically goes into dark mode you know the people that are using it fine they thought about it they' thought about you know what like the people who are going to be deep in these docks want dark mode immediately they don't want to start off with know this white white stuff um but no I think I think the from the website and from what I've heard and the reason I sort of always asked was exactly that the the fact that you can deploy as you can deploy many things and then follow it almost like this meta layer um so you can do the NIT ortic but you also get the met but you're not Nimble because it's again designed for Enterprise not for your mom and pop doing your stra analytics like you're not do stra analytics in here right same same with data bre but snowflake you might right you might use a community version of yeah snowflake to host your your personal data yeah you can just start up a new trial every every month with a new email that's that's how you get prey snowlake um but if I go back to DBT I think the bit I really struggle with is DBT core and man DBT core that is painful because you don't get all the same niceties um it's a bit it's a bit like that sort of rough experience you get with um with no Explorer no match no semantic layer DVT um I think it's still quite promising I this is I'm going to sound nasty is it a product or is it a feature Jesus Christ it sounds like you're saying DBT is going to get bought out by data bricks or snowflake or someone and absorbed into their product it is or the principles or or or do you think that the principle shouldn't it be that surely the way DBT works is exactly that tell me that it doesn't belong inside of an analytics platform that's decoupled is agnostic to you know where it's come from it's it's the thing you put on top of your platform to orchestrate it doesn't matter what it is it's a feature surely yeah trying to think I'm trying to think of another product that that that almost does that it's usually freemium stuff right like it's like oh yeah yeah correct maybe explains why people struggle with using it because you're just like well I just do this myself with my own little store procedures and Sequel and all this stuff right like so yeah a brutal ending there is it a product or is it a feature we'll come back to that later um Eno Eno Mak nice the new kid on the Block yes interestingly allows me to go back to my love alrix because it's got many of the same technical team right the very very heart of the product Adam Riley Steve Harding I think as well yeah Steve Harding the original creator of the ultra James dun yeah James exactly exactly this is like the greatest hits of let me even go further into this is that era harded Harding sorry Ned Harding now I go back to like 20 is it 2015 Ravi you've got Libby you've got Ned you've got um uh Adam Riley crew macros there was just a sweet spot there where alrick was like use a British term I've got a greatest IDE here so um go for it go for it I think it was it was it was one of the ultra conferences in London and will Griffith's good friend of mine former data School consultant yeah um I think it was him or Ben Moss created a m ultrix macro called the Scrambler because oh that yeah the Y XMD file the ultrix um file that you used to open up workflows is just XML so obviously if you can find out the X and Y coordinates of everything on your canvas and then just randomize it what you'll get at the end of it is just all of your tools everywhere when you just put it through the randomizer the Scrambler but it still works but it still works you can still run it it's just in a horrible order um so Ned's Ned's in London and we we're talking to him Adam Riley James duny and Ben's like show show Ned the Scrambler um so we go through it show him explain it to him and Ned's words were when when I you know wrote built designed Al back in the '90s didn't see this as one of the use [Laughter] cases love it love it love it so yeah Eno um Cloud native uh I think it brings dare I say it the philosophy of orrix but to a modern to a modern to modern world and I'm not going to go as far as saying it is alrix I'm not going to go as far as saying um you know you know they're just foring alri because I don't think they are I've seen some posts that um uh Adam Riley his name is so hard I just think of him as Mr crew matros that's in my head I just want to say the guy who built crew macros always forget yeah um I always forget the name Adam Riley Adam Riley Adam Riley I went forget it now um every time I see Adam Riley do Dam on LinkedIn there's always a little bit more thought that goes into some of the simpler tools that we we we've never seen an alrix and it goes all the way into ux and UI and it actually reminds me this is this is what the core problem is you know RAV you have not used alrix in five years let me let you into something not much has changed you're not messing and it's ult gr Prix and I'm like that's the same same it's literally the same interface again you can the same argum about Tableau what's changing Tableau desktop him what Everything Jesus don't get me started don't get me started don't get me started don't get me started it's not for the dashboard designers anymore to to the core to the core h no no I've been on a roller coaster ride about that we'll say that for another podcast but going back to enay it just feels like some of those initial places it's very clear the product team did want to go did want to go and understand and discover and reimagine and re Envision they're getting the opportunity to do that in nay um and I think you know if you'll listen to this go check out Eno and don't come at it from an alter perspective go with a sort of a neutral mind a clean mind as it were evaluate it for the product it is and I think you'll find um something quite surprisingly nice um so it's quite good I think again the the the benefits of Tableau and alteris have always been you can think and do in the same flow right like you end up in a flow state it looks like Eno almost taking this yeah from a flow State just looking at the um the documentation uh there in the getting started there's reading a CSV passing selecting commments how to use Eno documentation the fourth one is your journey from to Eno brackets from alter oh yeah they're going for it hard right and they're not the only ones um we'll touch on data IQ Maybe a bit um uh also going for the altrix customer very very hard I think everyone's just looking at alrix and go yeah we'll have a bit of that um who is it um five Tran I kind of think feel like had their moment taking a bit of 's lunch um I love the philosophy behind Five Tran which is oh to hell with the freaking macros and the fls we'll just tell you the answer like we'll just do the work for you and just give you beautiful tables and by the way you want DBT here we've even built these DBT models for you you know off you go like I am still shocked by how easy it was to go from not a five Tran customer to a paying five TR customer who within 30 minutes had exported my entire YouTube analytics for the last six months to snowflake in 30 minutes now I know speed or ease both was it just doing the work for you just both just both I literally turned up to snowflakes 30 minutes later I looked at the tables I looked at the architecture I looked at the models had done and I was like perfect I I've like I've got what I need I I don't even need to think about I just literally open Tableau and a problem that would have know taken quite a lot of considerable effort in time was just solved now was the cost okay I know five TR is quite expensive airb is cheaper does the same thing of course but I also wonder like why didn't alrix just lean in some of that as well in the past right anyway keep going back to a keep going can you tell that I love the product but I hate where it's gone you can just play a violin in the background I'm TBL T and I'm out here crying like soul out for all tricks I will you you know this better than anyone R it's probably because of the amount of time I've spent in that product solving stuff for customers right like those fmcg projects Tim those are recently Insurance 90% of my time was in altrix solving huge problems 10% of my time was in table because I solved it all in in NRI so anyway we've moved on Eno check it out DBT check it out data IQ check them out as well we've got the cloud platforms so I'm going to Rattle them off there's only really three you need to aware of as your AWS Google Cloud platform even gcp can almost like start to be like yeah exactly because like I I don't know any I I don't know many positive people when they use gcp it's fine it's hard to use it's all Cloud but in the AI world where you going to run yourap where you going to run your uh um um AI compute um God I can't get my words out what is the word where are you going to run your Transformer models that's what I was looking for um so Google Cloud platform Transformer models this is where you run them I I think this this is about to change if you talk about azur Amazon gcp azur is the only place you can run chat GPT models fine lots of people are going there um Google is the only other person I think a lot of people want there to be other competitors to Google so I think gcp might have some benefit in being the only other place but Amazon is not really part of that right Amazon's not part of that sort of let's say dialogue other than it's still the place you go to run compute to prepare your data to be able to run it through Ari models so they they are like a transitory platform for all of that stuff and but yeah sort of a so so so my my view is that the reason I don't see much featuring in gcp or like um you know sorry not much fure I think it it be used but it's just it is just always used less what I'm what I'm why I think AWS and as your sort of live longer is because they have bigger customers that use them for everything and the scale of it like in the coverage they have is just they're kind of baked in aren't they yeah they're baked into and like for example in in order to have aure any like anything with aure be it you just need the platform and once you have the platform you may as well get Microsoft SQL Server you may as well start using power automate you may as well start going through all of the different things you have um that are available for um for Microsoft and Ed is the same like it's really easy and really it's in really nice platform to use and it's like when you and me both did the certification around the same time right yeah like it's it's such a nice platform to learn it makes sense completely and you can you can build from that and again all three of these to an extent work quite nicely with Tableau I think gcp is the only one that doesn't because big query is very difficult to connect up to Tableau in my opinion I've never had I've never had a positive experience with big query in tableau but go Google Gregory gcp Gregory will come on this podcast and talk to I said George gcp George there we go gcp George will come on Greg or George one of the two yeah I I don't know I still I think gcp still lives on Purely because it's Google and Google probably run a ton of their own stuff off gcp as well I mean and like and then Apple run a ton of stuff of of gcp and Amazon to be fair and then a lot of like what I would call Spotify run off gcp and so if I start to list the companies that run off gcp a lot of the companies that do that purely do that because they want to be able to have something against Amazon because Amazon is like a big a big big share of the cloud so gcp lives because Amazon exists right it's not you know it will never die because you always need something that isn't as your against Amazon I mean again I I feel like of the of the three if you talk talking about the database to connect to their data I have had no joy with big query I've had very little Joy with red shift and Athena and the sequel server has been all right yeah um so of the three if you're connecting data I'm as your man fair to start with and and it's going to be hard for you to move out of that even if it was to Amazon or something like that right because it's the Enterprise yeah yeah so if we talk about Tableau and we talk about these Technologies right a well in the Aur you've got Microsoft SQL Server you've got obviously powerbi we bring that up it's kind of part of the ecosystem um what else have you got in AO that really works well with a Windows Active Directory uh All That Jazz you've got Microsoft Suite obviously I these are not in as your but they are intrinsic in your contract with Microsoft I to have Office 365 azour and everything else kind I don't know how the commercials whatever work but it's just levels isn't it I'm sure it's levels yeah yeah yeah whereas Amazon apart from the compute and some of their database techology which is pretty good red shift never really took off like it was supposed to right red shift had its sort of snowflake moment and then fell flat in my opinion it never really sort of lit the fire of the industry because the Amazon pricing model just meant that the more you used it the Richer they got and you know you know it didn't beon obice didn't quite level off and actually we're seeing a trend to move away back to on premise now with a lot of companies called bare metal yeah exactly I love that phrase bare metal like I just think like the the the the the the hardware Engineers needed something cool they like yeah Bare Metal Man anyway um the cloud P are super interesting gcp doesn't have something that hooks you in that's ultimately the problem there right yeah and from a tableau perspective all you're really doing connectors potentially some of the data sources in there um you've got uh is it Firebase which is the really commonly used gcp um dat I think gcp let Google this [Music] um of course with Tableau I for I do forget it's not really gcp you got your connectors to um bitco query Google Sheets Google analytics as well right those are like of big things but Google analytics and Google Sheets don't need Google Cloud platform I know they probably run off the same architecture but I don't think you really need them um trying to look at see what else they've got not really much fire Bas is real time they've got mongod DB computer engine VM then download okay andal platform for your entire team so you can put TBL server on their computer on their VMS that's probably the only other use if tablet server is not going to die death slowly but uh yeah like that's about it right um yeah run run run your server and containers or whatever in in in Google that's pretty much the end of that good um honorable mentions before we call it a day uh we touch on powerbi don't really use it with table but I kind of think like if you're a day trist today and you're only learning Tableau you're kind of not really selling yourself to the market you've got to be learning powerbi and I say that in the nicest possible way someone who hates power behind will not be made to learn power behind properly unless I really have to I've done a good enough job talking about Tableau to avoid that um but yeah like come at us yeah yeah and there's actually you know bars power there there quite a few powerbi channels none of them went with my naming moner maybe that's a hint I should have taken yeah um so that's good Sigma comes out from time to time competitor of Tableau don't have to use it with tablet but if you're were going to learn something super super bleeding engine modern it would probably be Sigma where I think Sigma falls down have you used it I've just seen some guerilla marketing that's all yeah so okay on the marketing I'm just not a fan of Sigma marketing just misses the misses the whole point of the tool in my opinion was too much energy focusing on Tableau when they should just focus on the thing they're really good at and that is enabling people to build analytical workflows end off not workflows in the sense of orrix but workflows in the sense of dashboards that should be apps Sigma goes that one level further and lets you build that app right within the dashboard let you go a little bit further than your typical thing it's got right back natively built in but here here's the kicker like want to connect to Fat files not in Sigma so it's also very very modern like it's so modern it might break some of the large like you know companies that we come across where unfortunately there is that one Excel file somewhere in the organization that everything is relying on yeah exactly everything is in the cloud this is in that and then you go to you go talk the country manager and you go oh where's this look up table oh it's in my one drive and you're like you have that moment um you know was that Chris Lawrence in Bad Boys you know he has that Meme where like gets real and his eyes get bright open you're like where like where are you keeping that in one drive yeah happens in every situation Sigma is not the to for that but I'm enjoying their sort of content I'm enjoying what they're sort of saying I love Luke stanes and um Luke stocks um you know history of uh analytics uh that they keep doing on the sigma Channel they keep doing these sort of short videos giving giving their obviously it's quite biased to word Sigma positively but yeah um interesting to see where they go I stop focusing on tabl just just just just do your job thing yeah exactly and it's doing its job well it's just just too focused on Tableau the the other problem with that is tablet is an actual platform like in the nicest possible way if I say Tableau Sigma powerbi you just immediately discount Sigma to any customer they've never heard of Sigma and so they' just got this really long path to building name recognition matters right like name recognition really matters and it's that agel saying noever no one ever got fired for you know buying IBM right like in analytics no one ever got fired for buying Tableau powerbi yeah that is where those incumbent tools are and so yeah anything we missed any other honorable mentions things I think I mean yes there are many that we have missed um but I don't think there's anything major that we want to talk about that we said we would that we want to talk about is a very fair way to put it I'm sure there's other tools and if we've missed your tool and you love your tool we're really sorry mongod DB like you can you can keep reeling off all these things you can do the game of Tech Tool or Pokemon um which I think is still that that that website is still live um but no it's I think it's useful to just go through and talk about these things and I think is this will this episode be as dry as that hyper episode we did maybe for some people um but I think I think it's you've not seen my editing skills wait till I get these into Tik toks and shorts exactly yeah the edits and the cuts will be so fast they'll be filthy no I think it's um I think we've covered a lot there um but no generally if if you have dear listener any thoughts anything that you think we should we should dig a bit deeper into or you want to come on and discuss something with us I think you know one of the reasons I think we wanted to do this is almost to open the arms of like tell us we're wrong yeah exactly come talk to us about it so we can Grill you on um and learn learn at the same time like I think yeah Enzo data bricks maybe even some product manager somewh alri who's like clinging on being like please don't go um yeah oh again right um next episode we should talk about 24.2 um we should talk about um some other stuff it's tabl dying let's go back to the core let's go back to the core bam 24.2 is Tableau dying the Salesforce care let's cover it all let's let's deal with it hot takes to come hot takes to come absolutely there's a there's a great topics we will generally cover them I guest but um you know I was in touch with someone on Reddit who posted something is it even worth learning Tableau anymore blah blah blah and I was like that's the wrong question the question should be asked which parts of Tableau are worth digging deeper into exactly VI extensions exactly service API yeah if it's the same thing that you know everyone's blogging about still from the last five years no don't like it's not about building beautiful stuff in Tabo public to become like you know top data analy like the way companies are going people are moving further into the stack so we'll talk about that next week and where I think like The Sweet Spot in terms of skills is and also I'll say something controversial it doesn't all have to be about tablet like people shouldn't be learning anything wedded to a tool that should be learning agnostic skills it's easy to pick up the tools once you're in front of them it's hard to know the core skills whilst you're in front of the tool so learn those F anyway more next week to be continued take care mate super take care br

2024-08-06 23:26

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