0:00Hey, it's Tim here. In today's video we're
0:01going to be covering a topic that has taken
0:03me a while to understand
0:04But it's gonna be VDS, visQL data service
0:07The concept of decoupling and this term
0:09that was mentioned called headless BI and
0:12then we're also going to touch on
0:13Some sort of small concepts around layers.
0:15Anyway, that's a lot to cram in. Let's get
0:18stuck in. Okay
0:19So at the Tableau conference keynote Pedro
0:21announced something called a visQL data
0:23service and he threw a few terms in for
0:26example
0:27Headless BI decoupling and so what I wanted
0:29to do is sort of really dig into that
0:32But I really can make a video immediately
0:33because fundamentally I didn't actually
0:35know what Tableau meant by the term
0:37Of course, you can go out on the internet
0:39and look at these things and sort of come
0:40to some sort of conclusion
0:42But I was actually able to get a bit of
0:43time with a few people
0:44internally at Tableau who knew a bit more
0:46about how the service has come about and
0:48Some of the thinking that's gonna come out
0:50and so I have to sort of lead with this and
0:52say that look this service has
0:54Not been announced. It's not been released.
0:56It's not in public. It won't be probably
0:58until next year
0:59But because it was in the keynote we can
1:01speculate a little bit about it
1:03So let's find out then let me show you what
1:05I think
1:05This service is going to be like let's hop
1:08into Xcalidraw where we can go through a
1:10diagram that I've prepared for you
1:12Okay, so I want to start off by setting the
1:15scene here
1:16What I want to do is essentially take you
1:18through a diagram
1:19So I've hidden the diagram and on the left.
1:22I've got two arrows one is green and one is
1:25purple now
1:25These are conceptual arrows
1:28I just want you to be absolutely clear that
1:29what you're about to see is not technically
1:32correct that sort of it's conceptually
1:34correct
1:34But it's not technically correct and so on
1:36the left here
1:37We have the journey and we have the
1:39analytical flow which starts from the
1:40bottom and goes up and then on the right
1:42you have
1:43The consumption workflow which goes from
1:45the top and goes down
1:46essentially what I'm trying to show here is
1:48that if you're an analyst building some of
1:50these assets whether it's a Tableau pulse
1:52view or
1:52Dashboard or metric whatever you're
1:54building you start from the bottom of this
1:57flow going upwards
1:58And then when you're actually using these
2:00tools when you're actually consuming this
2:02data consuming this analysis
2:03You actually go the other way around you go
2:06from the top down and that's an important
2:07sort of journey to understand
2:09And but it also means that the journeys are
2:11actually two different things consumption
2:13and creation are completely separate
2:16They don't sort of always marry up, but in
2:18the Tableau platform of course
2:19They can actually overlap in lots of
2:21different ways, and that's why this sort of
2:23blue purple thing is a little bit confusing
2:25But let's take a look at this diagram. Let
2:28's select this and move this across now in
2:30today's world
2:31And this diagram on the left is sort of
2:33accurate it really what you should be
2:35looking at is a Tableau architecture
2:37diagram
2:37This gives you an accurate view of where
2:40all the services are sat, but it doesn't
2:42help me with my explanation
2:44So this is what I've made instead so if we
2:46go from the bottom up
2:47and let's look at the analytical flow when
2:50you're building a
2:51Dashboard or metric or something for an end
2:53user to consume you'd start here with your
2:55data sources at the bottom
2:56And it could be in Google Drive. It could
3:00be in the cloud
3:00It could be wherever you want it the data
3:02sources can come from a multitude of
3:04sources including flat files on your
3:06machine
3:07Once you have that once you've connected to
3:10your data source you see the next step
3:12I go to here is server and cloud now
3:14I've done this because in a world where we
3:16're bordering is a predominant experience
3:18and in a world where data sources mostly
3:21live on
3:21Tableau server and cloud this is
3:23technically true
3:25But if I was if I was being strict where
3:27you'd actually start from you know an
3:30analytical workflow is you'd you'd start
3:32off in
3:32Test stop then go down to your data sources
3:35connect to your data sources
3:36And then once you've connected to your data
3:38sources you then start building dashboards
3:41Once you've built your dashboards you then
3:43publish them back up to server
3:44And then now you've published them to
3:47server they can start to do all the other
3:48things for example
3:49You can use the embedded data source within
3:51the workbook to build a new view you could
3:53even publish the data source within that
3:55dashboard
3:55To tableau server and cloud and then use
3:58those in other dashboards and other metrics
4:01so this diagram starts to get a bit
4:02confusing
4:03Then once you've got your metrics or your
4:05dashboard sets up from those you can then
4:08go and do a bunch of things one of
4:09those is do an embedded experience where
4:11you put it inside of an application or you
4:12put it in a web page or
4:14You go on the left hand side, and you send
4:17it out to a client the client could be
4:19Tableau desktop or Tableau prep
4:20This is in essence me connecting back to a
4:23data source
4:23That's hosted in server or cloud if you're
4:25using desktop or prep if you're on mobile
4:28Then this is essentially just the Tableau
4:30server Tableau cloud app on
4:31Mobile consuming dashboards and metrics if
4:34you're on the web
4:35Then you're browsing the Tableau server
4:37Tableau cloud portal browsing the explore
4:40tab looking for
4:41Visualizations and using them and of course
4:43at the very end you've got your end users
4:44here at the top
4:45They're the ones who are fundamentally just
4:48you know users. They could be explorers
4:50viewers whatever they are
4:51They are the analysts coming in to look at
4:53some data and so in this flow
4:55This is sort of broadly the worldview today
4:58, and this is how it works again
4:59This is not a hundred percent accurate, but
5:01just bear with me as I try and explain this
5:03so that's the world today
5:05And Tableau through this term called head
5:08less BI and what I was trying to sort of
5:10represent
5:10What do they really mean well in that
5:12strict flow where you're starting from
5:15desktop?
5:15You're going to your data source, then you
5:18're going up to your dashboard you publish
5:20it to server and cloud
5:21Then you go up to one of these experiences
5:24and these experiences all require
5:27Metrics or dashboards to work so you can't
5:30for example
5:31You can't and if I sort of bring my
5:33annotation tools back you can't
5:36really
5:38Give a user any sort of experience unless
5:40it has a metric or a dashboard
5:43They have to start that even if you just
5:45wanted the data source
5:46You'd still have to create a dashboard that
5:48the JavaScript API could query or some
5:51other API could query and then from there
5:54You could go on and build your experience
5:56They need a dashboard and that's
5:57essentially not headless headless BI is
6:00essentially this concept where you don't
6:02need a dashboard
6:03you're essentially decoupling which is the
6:05term tableau used and
6:06The infrastructure so you can choose at
6:09what point you're taking part in the
6:11journey
6:11I'll come to a diagram a little later that
6:13explains this in more detail, but this is
6:15sort of the world today
6:17Let's take a look at the world in the
6:18future and for this I've added Tableau GPT
6:21and
6:22Tableau pulse into this matrix so you can
6:24get a better view of sort of where
6:26everything might sit again might sit
6:28because I don't
6:29Know we haven't seen the features. So let's
6:30go ahead and remove this. So
6:32This is a new diagram and what we can see
6:35here is that actually is very similar
6:37The the the way end users and the way
6:39people work is exactly the same
6:41However, if you go down here to the server
6:43and cloud section, it's a little bit busier
6:45. And what you can see here is that
6:47fundamentally and you now have where before
6:51you had
6:52Metrics and dashboards and you now have
6:54essentially three options you have Tableau
6:56pulse, which is just over here
6:58You have visQL data service
7:00Which is this there and you have your dash
7:02boards and the way to think of this is these
7:05three are equal citizens
7:06To the data source. They all have the same
7:09access to the same data sources. So here's
7:11the first big change
7:13This means you could build three separate
7:15experiences that are all pulling from the
7:17same data source
7:19That is either published to Tableau server
7:20or cloud or is going through a virtual
7:22connection or anything
7:23Any of those things sit on top of the
7:25connection layer. And so the way to think
7:28of this is it's actually layers
7:30It's actually a really good way of thinking
7:31about it
7:32so if I go back here and strictly speaking
7:35we think of these as like a
7:36Tower you start with your data source you
7:38go and connect to your data source again
7:40You'd be doing this in desktop, but just
7:42bear with me. Let's start from the bottom.
7:44You start from your data source
7:45That's just your you know, raw data source,
7:47whether it's Tableau server Tableau cloud,
7:49whatever it is you start there
7:51once you've done that you publish up your
7:54server and you publish up your connection
7:56to server or cloud and
7:57Once the data is there, you now have three
8:00options you can either
8:01Build a metric go and define a metric and
8:04then have it live inside of Tableau pulse
8:06You can ask an analyst to build something
8:09very bespoke. Maybe a dashboard maybe
8:11something nice
8:11maybe something that even uses Tableau
8:14pulse on the metric built in Tableau pulse
8:16or
8:16You could ask your developers to use vis
8:19cure data service to query the same data
8:22source
8:22To then go off and build an application
8:24interface that uses d3 in a web browser
8:27somewhere else
8:28okay, so those are your three options and
8:31so and you can kind of look at vis cure
8:33data service in this instance at
8:36Primarily being for embedded services and
8:38applications essentially this cure data
8:39service the only real reason you need to
8:41use this is if you want
8:43To build something external somewhere else
8:44right that that's the only real reason it
8:46makes sense
8:46Otherwise you just use the front end and
8:49set up the tablet give you through its own
8:51product
8:52Which just comes out of the box ready to go
8:54and is seamless
8:55You don't have to sort of rebuild or put
8:57any dev time behind it
8:58But for companies who do want to build a
9:00more tailored experience like Tableau
9:01showed during the keynote
9:02This this would be one of the ways of doing
9:05that. Okay
9:06Now the other thing to bear in mind here is
9:08that Tableau GPT is sort of sitting around
9:11These data sources so Tableau GPT was
9:13described by Tableau as being and while
9:15Tableau's own efforts are generative
9:18generative generative AI can't say this I'm
9:21having another like
9:22What is it? I just can't say these words
9:26are so confusing anyway
9:27these
9:31Technologies this technology is going to
9:33sit around this and the way Tableau showed
9:35it was side by side
9:36But really strictly speaking Tableau GPT
9:38needs access to all of these things to be
9:40able to enhance your experience
9:42And then server and cloud remains what it
9:44is today the secure sort of you know
9:47governed space that IT
9:48admins can use to control and manage
9:50everyone and
9:51Everything runs on already the
9:54infrastructure that's there for server and
9:55cloud
9:56So it's all set up nice and it's sort of
9:57nice and easy to work with
9:59And so that's sort of how this starts to
10:02work and so you can hopefully start to
10:03realize that look this actually changes
10:05In a very meaningful way a couple of things
10:08not just how users consume
10:10information and data as Tableau showed but
10:13actually also workflows and so what I did
10:14is I tried to make it a
10:15Much simpler diagram just here on the
10:17bottom. So let's go ahead and move this out
10:20of the way
10:21so if I just move this out of the way and
10:24Just look at today in the future you see on
10:26the left you have today's world. So you
10:28have your end user, okay, and
10:30They can choose their window into a data
10:32source. They can either use Tableau metrics
10:35. They can either go to a dashboard
10:36And they can essentially just you know
10:39browse their their setup how they want but
10:42it's actually quite narrow
10:43Everything has to originate from the
10:45dashboard pretty much
10:46There is no real way around it
10:49The other way the other thing you could do
10:51is you could you know have have your data
10:54sources going into Tableau
10:55Available through other platforms and other
10:57systems, but you'd have to build those
10:59outside of Tableau. That's obviously always
11:01been possible
11:01but yeah, it's a very sort of different
11:04setup to the one we have here on the right
11:06which is
11:07Really the ability to choose the window
11:09into your data source
11:10You can choose three ways of looking at the
11:12same thing
11:12And there is an interface to define the
11:14metrics that is decoupled from the dash
11:16boarding experience
11:18Previously it was very much part of the
11:20dashboarding experience
11:21And then the other thing is you can request
11:24data without having to put anything in
11:26front of it
11:27You don't have to have any sort of show
11:29showroom for your data
11:30You can go ahead and pulp pump it into
11:32whatever other service or API you have and
11:35for the record, you know
11:36There are many services and API's that have
11:38ready-to-go systems out of the box that you
11:40can do this with
11:41So this is a this is a diagram. I'm still
11:44working on I'm still not sort of super
11:46happy with it
11:47There's a couple of things that are wrong
11:49And you know in a way there's never going
11:51to be a right version because depending on
11:53what you're talking about
11:54This diagram will need to change
11:56It's kind of like a 4d chess where you've
11:58got to look at it different dimensions for
12:00it to make sense
12:01and but hopefully this hopefully starts to
12:04explain sort of all these concepts and so
12:06I think the big change for analysts if you
12:08're a data analyst today and you kind of
12:10wonder well
12:10How is this going to change my workflow? I
12:12think this this this is fundamentally it
12:14you're gonna need to actually start
12:15thinking about
12:16I don't think visco update service will be
12:18one of the big things unless you're a
12:20developer an API
12:21That will be pretty much a standalone thing
12:23and
12:24but
12:25you'll need to think whether you want to
12:26define metrics so that they're available in
12:28tableau pass with all the goodness that
12:30comes there or
12:31You're gonna build a dashboard that does
12:33the same thing, but we'll probably not get
12:35the same love and attention
12:37long-term as
12:39Something like tablet pass get you know
12:41that tablet pass is gonna get a whole bunch
12:43of suite of features
12:44They're gonna hook into newer technologies,
12:46of course, obviously running in tablet
12:48cloud
12:48My hunches tablet pulse is a hundred
12:50percent going to be a cloud only feature. I
12:53can't imagine
12:55you know
12:57tablet putting the kind of work required or
13:00even admins necessarily and
13:02Being willing to beef up their infrastruct
13:04ures to the level that can let something
13:06like tablet pulse run
13:08Um, if you really want to know and for
13:11tableau to sort of train an LLM to do this
13:14kind of work
13:15it's a serious investment if you look at
13:17the likes of chat GPT and
13:18You know open AI there and they're
13:22investing. We're literally talking
13:23thousands of
13:25hardware thousands of GPUs thousands of
13:29computing hardware a
13:30Ton of time it takes a really long time and
13:34whilst all of that is happening
13:35It costs money and electricity and that is
13:38just an investment and that's just stage
13:40one
13:40There are multiple stages to training an LL
13:43M
13:43It can take months on end using all this
13:45hardware with the right expertise and at
13:47the end of it
13:48You have something close to what chat GPT
13:51has and there's something called an ELO
13:53score
13:53Elo and essentially that rates how good
13:56LLM model is and so it's going to be
14:01interesting to see how tableau GPT
14:05sort of embraces this where that sort of in
14:07that that sort of
14:08Investment takes them and sort of what kind
14:11of capability are we seeing?
14:13With a technology like this versus the
14:15things we are seeing more generally through
14:17companies like Microsoft open AI and Google
14:20Anyway, we're getting off off track here
14:23I just wanted to describe what's going on
14:26with viscos data service using this diagram
14:28So hopefully at some point next year when
14:30it comes out we can dig a little bit deeper
14:32and find out more about it
14:34Anyway, thanks for watching and I'll catch
14:35you in the next one
14:36You
14:46[ Silence ]
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