The only way today’s Finance teams can deliver the insightful information and recommendations business leaders need is by quickly analyzing complex financial and operational data in real-time. But this much-needed, sophisticated kind of decision-making is out of reach unless you’ve built a truly agile data pipeline — and that’s exactly what Incorta does best.
In this live, interactive workshop, we show you how Incorta can help you:
- Connect quickly and easily to any complex data, like Oracle EBS
- Rapidly develop and re-develop data models
- Build virtual star schemas
- Develop dashboards
- Define data sharing and ownership
*You need your own Incorta Cloud Free Trial account to follow along with this session — if you don’t already have one, sign up here.
Joe Miller: Good morning, everyone. I see people are joining the session. We're just gonna go ahead and give it one minute before we get started, and then we'll be off and running so hang type. We'll get started shortly.
Joe Miller: I do see we've added a few more yet, so we are just going to wait. About forty five more seconds, and we'll get started.
Joe Miller: All right. Let's go ahead and get this session kicked off here. Welcome everybody. My name is Joe Miller I'm. The senior director of Community and customer education here at
Joe Miller: Today's session is going to be a virtual Hands-on lab, and we're going to be focused on delivering financial and operational analytics through a truly agile data pipeline.
Joe Miller: We're going to talk a little bit about what that means, and how in quota differentiates itself in this particular space.
Joe Miller: Just so, you know a few housekeeping items by default. Everyone is muted for this session just to make sure that we can hear each other, and we have no interruptions about the duration of the session.
Joe Miller: But just because everyone's muted doesn't mean we don't want to hear from you. So if you have a question throughout the duration of the session, go ahead and put it into the Q. And a window or the chat. The venerable grant. Joseph is here with me on this call here today to also support me. If any questions are happening throughout the duration of this presentation that they get answered,
Joe Miller: Okay. And we also have aspen
Joe Miller: joining us here under the in court of admin pseudonym. Um. Supporting us and making sure that we get all the materials, and so thank you to both of you for joining me today, and let's go ahead and get this session started. So the first thing i'll make note of is a virtual Hands-on lab by nature is really intended for you to get your hands on in quota and try it out for yourself.
Joe Miller: Um. Well, the concepts may be interesting to you just to watch what we to watch. We think it's going to be even more interesting if you get the hands on the product. We have a few files to share over to you. Um! That will help you participate in this session.
Joe Miller: I'll also make note that the games of instruction throughout the session will be assuming that you are doing some of the work that i'm doing so. The cadence of the session will be a little bit slower, and step by step, to make sure that you are getting the support that you need
Joe Miller: to get access to encoded. If you haven't had access to it already, you can go to encoder dot com onencorded Com. If you come up here into the top right corner
Joe Miller: you'll see an option that says, start for free.
Joe Miller: Okay, all that option will do is launch this dialog here, letting you know that your cluster is getting ready. I think you have to provide a business email, but once you're off and running, it will prompt you to add username, et cetera, right? And you might also want to check your inbox just in case um the confirmation lands in there as well That shouldn't take you
Joe Miller: about five minutes to spin up, so go ahead and get that started now, and we are going to cover off on a little bit about the introduction of why in court it and the value of
Joe Miller: Okay. So the first value in cord. It is kind of best understood by kind of understanding the landscape that we're in right. And I think you know, I don't want to belabor this diagram that you've probably seen time and time again,
Joe Miller: but a quick overview would be effectively a lot of organizations from a modern data architecture connecting to a myriad of source systems that are using Etl pipelines to extract that into some landing zone.
Joe Miller: They're modeling it. They're refining it. They're aggregating it. They're packing it in a way that it's accessible, variable and understandable for their business users. And through that nature of that process of grooming, Probably about a twenty. Five percent of the data remains right, and seventy five percent has been eliminated, or the detail of that data has been lost right.
Joe Miller: And finally, if you progress a further step yet for those who are at the analyst here or on the front lines of intelligence and recording within organizations.
Joe Miller: It gets aggregated much further yet, maybe even down to ten percent of the data that exists there. And this process is years right for organizations to build out along the way. So I think there's kind of two mindsets that I tell people to think about, one of which is the mindset of
Joe Miller: building this
Joe Miller: canon of data that people can refer to at any point in time. And then there's also just the on demand nature of analytics, right? So it's not unusual for us. If we had a a Cfo come to us, or someone who's leading a finance organization
Joe Miller: building reports are using reports that have been built off an architecture like this to come back and say, Hey, we want to adjust our kpis, or we want to be able to actually drill down to a detail level within our ledger or sub ledger to really understand what's going on underneath the hood right? And
Joe Miller: we know that companies need to be more agile than ever to accompany our to
Joe Miller: to acquiesce some of these requests that are coming in from leadership
Joe Miller: right, and unfortunately the rigidity of an architecture like this doesn't Adapt? Well, right. It's really well for building. What I said is kind of the hotted canon of data, right?
Joe Miller: But the reality is is that you know the request from a Cfo comes down it gets down to the analyst. The analyst has to go shopping and find the data where it exists within the solar system.
Joe Miller: They have only a ten percent people at the data that exist within their little ecosystem and effectively they have to keep going back and finding where that source data is. They might have to go to the data model, or they might have to go to the data engineer and ask where it is. And all of a sudden you get a super user involved,
Joe Miller: and that super user is able to support the data, build it in surface it back up to leadership. And then they say, this looks fantastic. We want an operationalized, and we want this report
Joe Miller: right? And all of a sudden. You're back into refactoring this into kind of a formalized pipeline. So you can kind of see these two worlds, this really formalized process, and on demand and process happening at the same time. And that's really where kind of the value of encoder comes in. Is it connects the two spaces together right? The space of
Joe Miller: long-term durable data development on demand data development.
Joe Miller: So we have source systems that we can connect to you know some of the bigger ones that we like to mention quite a bit as oracle sap salesforce work day. I could mention two hundred, two hundred and fifty
Joe Miller: more different data sources, plus some custom ones, if you so desired, but effectively in cordon, we'll take these source systems and ingest them into a platform at their most granular and detailed model. Right. So we will make sure that we get access to that data, and that data is not
Joe Miller: aggregated lost throughout this process, right? And the question I get a lot of times is, how is that possible?
Joe Miller: Right? How can that happen?
Joe Miller: Well, previously we've been driven into data warehousing and data modeling for a number of different reasons that could be performance-based. We didn't want queries to take a long time so we built star schemas to remove data or build the relationships of data in a certain way to perform right. Or we've removed a certain granularity because we don't think people need to understand data that way,
Joe Miller: Right?
Joe Miller: Mit ctl, and with incorporate data is ingested from these source systems in its native structure. Right? So if you're connecting to Oracle ebs, and i'll show you an example of this today, right? You can actually ingest the data model as it sits within it's within its application right?
Joe Miller: And the nice thing is is that previously such a large, robust data model used to scare people into saying, Hey, we can't have people traversing through this whole data model from a query perspective, or also have a huge performance in right, thanks to commoner storage in in cortex,
Joe Miller: reduce costs and memory and storage and new query, optimized routing even brought to you by important. You Don't, really have to worry about traversing these models anymore. Right? You don't have to worry about reorganizing and restructuring that you can take it in its native structure, Use it within Cora, and still get that sub-second query performance and report it right
Joe Miller: and on the back end of this, I think i'll make a mention that in cord up while it does have reporting built in, and we're going to expand a little bit of in quarter reporting today. That's not the end. I'll be all right, and court is intended to work with your or incumbent tool of choice, whether you're be in one
Joe Miller: table or power bi for excel for your reporting solutions right? Or you have applications that you want to redirect some of this data that's been ingested and recorded to like black line work day, right? Those are available. Um in the encoder system. So I don't want you to think of a cord as simply as a Vi tool, but a tool that's really going to make your data
Joe Miller: available, understandable,
Joe Miller: queriable. So there's never a point in time where you're lost in finding what you need when the agility of the business is required.
Joe Miller: I think you can think of probably a few situations within your business where this might happen. I know Today we have a lot of things happening within our political climate. We've got,
Joe Miller: you know, inflation rising by points over the past few months right? The way that things are getting taxed is is happening very differently. And now we need to reorganize how our skews are happening with inside the business right? And so really thinking, how do we adapt our pipeline in a truly agile matter? Manner is going to be more critical than it has ever.
Joe Miller: So we're going to get to the point where we actually jump into the inquiry and experience this for ourselves, right. And before we get in i'd like to talk a little bit about the flow,
Joe Miller: right, the first of which is what you'd expect. We connect to whichever source systems you need. Today we're going to connect to oracle. Okay, specifically, an oracle Ebs instance,
Joe Miller: the next of which is schema design. The next step that we will take in the process is selecting which tables within the source system we'd like to replicate into incorporate right. And I said, replicate or mirror. Um, we are not changing the source system. We are actually bringing in that data directly and ingesting it to
Joe Miller: right. So we have to choose which tables we want, how those tables are going to be related to each other, and then we load data into them.
Joe Miller: Try,
Joe Miller: and then finally, we will showcase this within in court today, as we'll actually jump into our recorder report, and we'll actually drag and drop fields from across this small data model that we build and show how cortex smartly queries through that data model very quickly to get the results of needs to create insights. One:
Joe Miller: Okay,
Joe Miller: this little middle layer that I have business schemas. Okay, this little dotted cylinder that sits in the middle of this diagram. This is something that I just say is,
Joe Miller: I call it optional right? It is a semantic layer that sits over this human design a great analogy that I like to use as websites
Joe Miller: right when I go to Google Com. Right, i'm actually querying for a web server, and that web server actually has an Ip address. In fact, you can Google Ip. Or go to the Google's website by just typing in the Ip address, right?
Joe Miller: But the reality is is, no one could remember
Joe Miller: an eight ten digit number right just to type into their bar to get to Google right. So Google effectively applied a domain name, Google Dot Com easy to remember, and it will redirect you to the right Ip address. For that search engine sits right, and I think of Schema's business. Steam as is the right way. Same thing, right?
Joe Miller: The data still lives in the schema designer with the physical schema of Encoder. The business scheme up provides a semantic layer just to make sure that the analysts that are working within in Corda are getting routed to the correct data within the skiing model.
Joe Miller: Right? So that's one thing. I showcase as a layer that can be added. We won't touch on this much today, but it's a huge value When you've got these large data models with four hundred plus tables with each table having four hundred plus comm. It can be hard for people to pick and choose exactly what they need, and that's exactly what their business scheme is designed to.
Joe Miller: So um let's go ahead and jump into the product. What we are going to do today is we're actually going to have a pretty quick session today. We've been trying to shorten these just to make sure that they're impactful, and we're making the best use of your time.
Joe Miller: What we're going to start off with is a schema that's got four tables. We're actually more specifically these through our tables here within this model, and we're just going to add two more to it
Joe Miller: right. We will just kind of emulate that experience of Hey, a leader came in. It made a requirement. They like our dashboard, but they want a new chart added within that dashboard. Let's add more to our data model from our source system.
Joe Miller: That's really all we're going to do today. It's pretty simple and straightforward.
Joe Miller: Okay,
Joe Miller: So i'm going to go ahead and jump over into our cloud instance, and my hope is that everyone has had ample time to spin up there and cord a cloud, and can start to follow along with me.
Joe Miller: Okay.
Joe Miller: So the first thing that we're going to do is I'm: just going to showcase this navigation bar right to left really high level, the first of which is actually a very new tab that just came out a month ago. Is our marketplace right?
Joe Miller: I made this statement early in the session, that you can easily build this pipeline. Excuse me, What if it was built for you? Right? And this is what the
Joe Miller: the marketplace is all about right. Do you need to do order management from oracle Ebs? We will ingest all the tables that are related to that within a go from Oracle. You'll build a business scheme on top of it. We will build the dashboards for you all deployed within a minute, Right?
Joe Miller: Of course you might want to add some tuning to that. But that's really just going to get you to a quick start. If you're interested in this, I highly recommend that anyone who has the time after the session go ahead and connect to this
Joe Miller: mit ctl. And second of which is security you could add users to in quota. Obviously you can add users to groups. We have group based permissions. So you can apply permissions to those groups. Okay, one hundred and fifty.
Joe Miller: That's a fairly admin topic. We're just going to be working with content within in Corda here today.
Joe Miller: Next we have the data Tab. This is where we make connections.
Joe Miller: Schema is where we define the tables that we want from those data connections we
Joe Miller: and then finally, content is the place where we go to build dashboards today.
Joe Miller: Cats?
Joe Miller: No, we do have business schemas again. That's that semantic layer that can sit between the schema and the dashboarding layer, and we also have a scheduler, so you can schedule loads, dashboard, delivery,
Joe Miller: Okay. So let's go ahead and get started, the first of which is we are going to connect to data and to do so. You'll want to make sure that you're in the data tab, and you're going to go to the top right here to this new button. Okay, and let's go ahead and select
Joe Miller: a data source.
Joe Miller: Just a quick, cursory look can show you a few of the options that we have at our disposal. Right? You could filter down based on some of these buttons here if you want to see a databases, or perhaps some data lakes, or perhaps some custom connectors. You can go to see data and pick up the connector from there
Joe Miller: all that's available here in total. I think it's somewhere over two hundred and fifty that we account for today that work with you.
Joe Miller: Today we are going to connect to oracle,
Joe Miller: and we're going to go ahead and name
Joe Miller: our data source orders. Okay.
Joe Miller: So go ahead and drop that name in there,
Joe Miller: and the Username and password.
Joe Miller: It's going to be apps. And you're going to show this twice.
Joe Miller: and you'll want to apply this connection string
Joe Miller: which I am going to drop
Joe Miller: into the chat window for everyone to copy and paste into their instance.
Joe Miller: I will make note that every once in a while I see someone say that their connection failed every single time that that's come up has really been. There's been some hidden white space somewhere in that connection. String right?
Joe Miller: Either at the end of this connection string, or right at the beginning, or maybe even somewhere in the middle. If webex chatting broke a new line for that string that I wrote out that
Joe Miller: you need to make sure that the white space has been removed.
Joe Miller: It's
Joe Miller: so. The test connection was successful. Again. Username password is Apps and apps. I'm gonna drop that into the chat just in case anyone catching up with that
Joe Miller: you are not select. Okay,
Joe Miller: Again, nothing's been created. We just simply had pinked the database in this particular case, just to make sure that the connection is up. It's working, and we can actually start building a schema on top.
Joe Miller: It's
Joe Miller: So Here comes the part of this particular training where I'm going to give you some starting files.
Joe Miller: Okay, we're actually going to do a quick start just to make sure that we get up and going as quickly as possible. Okay, So we're going to go over to Schema and we could create a new one from scratch. But i'm actually going to get us started with one
Joe Miller: aspen. If you could copy the link to the schema, zip, file, and chat that would be appreciated.
Joe Miller: It's
Joe Miller: what we will effectively do is bring in just a small data model that we can explore a little bit. We'll load, and then we'll actually add some some more tables to
Joe Miller: so schema or sorry Aspen has just dropped the link into chat.
Joe Miller: You can see it's downloading here. Once you click it, it'll just drop down into your download. It should take no more than a few seconds.
Joe Miller: Let's go ahead and add this schema.
Joe Miller: Okay, and to do so you'll select new.
Joe Miller: I've seen him,
Joe Miller: and you can either drag the scheme in here, or you can double click and open your finder.
Joe Miller: The
Joe Miller: I'm going to go ahead. You should have a new schema called orders. Let's go ahead and go into this, and we're going to start loading this schema right now, and while it's loading we're going to explore around kind of what's all available there. So i'm going to go ahead and select load from the top right one hundred
Joe Miller: and just falling, though. That's right.
Joe Miller: We have four tables within the schema, and it's just going to take data from our source system and populate it here.
Joe Miller: Okay,
Joe Miller: Now let's just to around and see what's at our disposal. When we were looking at a scheme right by default. When you imported this, it imported effectively the meadow name, right? What are the tables? What are the relationships between tables? What are the columns? Common types combinates, et cetera, et cetera?
Joe Miller: You want to explore everything at a very high level at thirty thousand feet. I highly recommend you. First go to this diagram.
Joe Miller: This diagram view is going to show you exactly what tables exist and how they're related to each other. Right? Um within quarter. There's relational modeling. So you you have to define what the child table. And what's a parent table? Um, child table typically being the table with many records to the one record of the parent
Joe Miller: right? And that will all be shown here. So the child in this case is Oh, we order lines all, and the parent is our material systems.
Joe Miller: And if you want more details on that. You can go ahead and click in to any of these tables and get a really high level, like not only an understanding of what it's joining to,
Joe Miller: but how it's being joined on in one of the key fields,
Joe Miller: if you want to go further, even for Sorry if you want to go deeper even further. Yet. You can select this little drop in button here and actually zoom into what's happening within
Joe Miller: um. Within this table you can see all the columns that are there labels, the types functions. They're all available for you to update. And you can actually see that I added an additional field called order amount via formula
Joe Miller: right. You can create formulas within the schema to build new fields that didn't previously exist,
Joe Miller: and all the details that you saw in the previous diagram available here. You can see what data set it came from our data source the joints that exist there. You can edit, add, et cetera. You can see I've got three different joins, one of which is compound one.
Joe Miller: Then any filters that sit on the table that may be moving data as it moves into.
Joe Miller: No, it's.
Joe Miller: The other thing that you can do is you can kind of explore the data, and this button isn't available until the load is complete, but it must have completed while I've been doing my tour here. So you can select this preview data button and see exactly what's going on. Usually you get a first one hundred records to see what's within the schema.
Joe Miller: It's.
Joe Miller: I'm going to show you how you can go even deeper yet,
Joe Miller: so i'm just going to go back to my order Schema here
Joe Miller: and show you this button here, called Explore. It was previously grayed out, but now that our last load has been successful.
Joe Miller: We can explore.
Joe Miller: I'll just make a quick detour here, just so you can see the load status.
Joe Miller: You can see the job that ran up here all the tables that were within that job, and the duration of that job itself Right? How many records were loaded, and the time that took so roughly thirty, four seconds on that particular job to load these four tables.
Joe Miller: It's
Joe Miller: now
Joe Miller: back to seeing more data right. If you want to see more than one hundred preview. And just look at what's happening within your schema. You go ahead and click on this explore button, and it will launch what we call our analyzer experience. Right now.
Joe Miller: Our as an analyzer experience is what you use to drag and drop different columns into an insight to build a visualization.
Joe Miller: Right? So in this case I could drag in.
Joe Miller: I'm not really building anything compelling, but just dragging in a few columns into my measure field, and you can start to see the data is being populated there right. And you've got access to just kind of peruse all the data that lives with it.
Joe Miller: I'm going to.
Joe Miller: So that's just a little bit on exploration.
Joe Miller: It's
Joe Miller: Let's jump over to the Content Tab, and we're going to import a dashboard now. So, aspen, if you could actually provide the other link for the dashboard, that would be great.
Joe Miller: Um! What we are going to do here.
Joe Miller: Oh, I think I already came in as I'm going to go ahead and download that zip
Joe Miller: Come back over into my recorder. Tab here,
Joe Miller: and we're going to go ahead and import that dashboard.
Joe Miller: It's
Joe Miller: so within the Content tab. Once more we're going to slow
Joe Miller: new and port folder and dashboard
Joe Miller: and dragging this default dashboard. So all of a sudden I've got this orders overview
Joe Miller: populated within my content view.
Joe Miller: So just a quick view around the content itself, for many of you who worked with other V. I. Tools really shouldn't be surprising how this experience lays out. I'm going to go ahead and edit one of my insects here.
Joe Miller: So within this insight you can actually see that I've brought in organization, names, and order amounts just to see the
Joe Miller: which warehouses are serving. The most orders for my organization.
Joe Miller: Pretty simple analysis.
Joe Miller: What I do like to show people is kind of what's happening underneath the hood, right? Even though I just drug and drop a few different columns in here. If I go to the measure, pill and select query plan,
Joe Miller: you can see that without defining it. At the dashboard level.
Joe Miller: The query path has been
Joe Miller: built within in quota
Joe Miller: right, and you would see, as I would drag and drop different columns into this insight.
Joe Miller: It's
Joe Miller: query plan begins to change right, and it's really dynamic. So when in core loads this data, it actually makes a note and makes a mapping to make sure it understands what type of data is everywhere, where it sits and kind of what the expected current pass will be to optimize on the runtime within these analytic service,
Joe Miller: so you can kind of see everything that's happening as a result of that that process
Joe Miller: mit ctl. And now, if we zoom back out to our dashboard. This is something that's a very simple dashboard that we'll just say situationally, we've provided to our executives right? And they say, this is great. We see. Understand? You know what to being one hundred and fifty,
Joe Miller: what our top manufactured items are, what our warehouse performance is, what our performance is, but what we don't have a good grasp around Is
Joe Miller: our customers right? You know we actually are looking at all these invoices. We don't understand how many have been ordered versus how have been shipped right on a customer, and
Joe Miller: and we need that available right. My first inclination as an analyst is, I would come in here and say, Oh, easy. I'll just add some information about customers class. So table level. Just so you get a look at it.
Joe Miller: I'll search around. See a customer. I don't see anything around customer name. Okay. And the reason is, we actually haven't ingested it right with our traditional Etl process. This may have been left out. I know this is a kind of a blatant one to leave out, but it's something that's just an example of something that i'll need to go shopping for
Joe Miller: once more, right? So we want to be sure that we handle this in an agile manner.
Joe Miller: And for us, you know, I know that from my source system I actually have two more tables to connecting to my schema that's going to allow me to once more bring in my customer information now.
Joe Miller: So that's exactly what we're going to do. Is we're going to go update our schema to make sure that we get customer data, and then in turn be able to understand how much has been ordered versus ships.
Joe Miller: Then build the insight into our dashboard, and that will show kind of the iteration. Right?
Joe Miller: So let's go over our schema here.
Joe Miller: We're going to go back to orders.
Joe Miller: We're going to bring in two new tables.
Joe Miller: Make sure I get to the right space here. Okay.
Joe Miller: So i'm going to go ahead and select you.
Joe Miller: I'm going to show you two experiences right, the first of which i'm not going to show you, because it's actually very easy intuitive, and you can do it at any time, the first of which is called the Scheme Wizard.
Joe Miller: Right. Um, I could connect to our data source. And once I've connected that data source, I would have access to all the schemas that are within my source system. All the tables that are within that schema select the tables that I want to bring into my schema and import them right Now
Joe Miller: for this training. We've kept this data model very small. But the reality is this: Most people are going to go to the source system and select everything and bring it all in right now,
Joe Miller: and that's where it becomes truly agile when that query plan just dynamically changes based on whatever you want, because the most granular level of detail has been preserved in this high fidelity data.
Joe Miller: But in this case we're actually going to say, Hey, maybe we didn't do our due diligence. We didn't bring it all in. So let's even just go back to the source system and update our whole typewriter and show you how. But where this goes right.
Joe Miller: So we're going to go back here and select new.
Joe Miller: And again we're going to add two different tables, the first of which is going to be coming from the sequel database, which is from oracle right? And we're going to select our orders data source. So
Joe Miller: we're going to write a super simple query, just to bring in
Joe Miller: a table right? I'll drop this in the chat for everyone we're going to bring in our customer accounts table.
Joe Miller: You can give it a quick test. Make sure it works. I believe this effectively. Run the top one hundred.
Joe Miller: It was like done.
Joe Miller: the one thing to be aware of is once all of these columns are added into in Corda.
Joe Miller: We've got to make sure that we give it a name,
Joe Miller: so i'm going to go ahead and name it as it was in the source system.
Joe Miller: H. C. Cust
Joe Miller: It's like done,
Joe Miller: you know.
Joe Miller: This table has not been loaded yet, Right? We simply have just queried it, and so far as to bring in the common names, and to in quota the loading will come in a second step. Here,
Joe Miller: let's go ahead and add one more table. Let's go ahead
Joe Miller: and select Sql database.
Joe Miller: Our orders data, Source:
Joe Miller: Query: Our accounts receivable Customer's table.
Joe Miller: Yeah. I'm: going to go ahead and name that the same.
Joe Miller: They are underscore customer accounts receivable,
Joe Miller: and we're gonna take a look at our diagram. Okay, So if I come over here. Well, actually, before we do that, make sure you save the changes.
Joe Miller: And we're going to pop over to our diagram.
Joe Miller: Okay, in this diagram you can see that the two new tables have been added, but no relationships have been defined for them. We need to make sure that we have a way to get my shift in ordered quantity, which sits all the way in my aura lines, all taken
Joe Miller: to be able to query through our agency customer accounts table, and in twelve accounts receive them at the table. Right? So we need to build that relationship right
Joe Miller: with an understanding of the system. No, it's house and go right. I'm going to actually connect order headers all
Joe Miller: two Hd customer account,
Joe Miller: and i'm going to say soul to org I you.
Joe Miller: It's going to be the key of my child. Table Order, Edward,
Joe Miller: order, that is all,
Joe Miller: and my customer account Id is going to be.
Joe Miller: I I copy that in the chat. Just so everyone
Joe Miller: see that
Joe Miller: and let's go ahead and join our customer Accounts table over to our accounts for sea level customers, right? And we're going to connect this on the key of customer account. Id
Joe Miller: to customer I did
Joe Miller: drop that once more
Joe Miller: While you are doing that I do want to make the call out right is that we are piecemeal building this model, right? We are cherry picking the tables from our source system.
Joe Miller: Reality is is that for many people like I mentioned earlier within the schema was, our experience will choose to ingest the whole thing,
Joe Miller: and during that process, when you actually go through that steam or wizard process?
Joe Miller: There is a primary key, foreign key detection mechanism to detect how the tables are related within the source system.
Joe Miller: So for many people, and you can experience this if you try the online store of data. Um, that's built in within a quota is you can simply select all those tables, and as you bring it in the cordo, actually build on those relationships on your behalf. Right? It's smart enough to detect them from within the source system.
Joe Miller: So what I'm showing here is really just a true built from scratch. If you had,
Joe Miller: you know, put it in the grunt work to get it in. This would be the place that you would go right, just making sure that you're enabled at the deepest level with, an
Joe Miller: so I have my updated model. Now I can actually query all the way from our airlines table to our customer table, and really understand
Joe Miller: what's being shipped versus what's being born in?
Joe Miller: We're going to go ahead and save those changes to my schema. And you'll see that I am greed with this warning right? A warning, saying that a load is required for these two.
Joe Miller: You have two choices, and you can choose to do a full load of the data as we did before on the top right. Or if you click on this little ellipses here on the top or the right side of the each table, you can choose to load it right, and I believe these tables are relatively small. You can expect that they would take about
Joe Miller: twenty to thirty seconds on it
Joe Miller: So the first one is loaded There,
Joe Miller: go ahead and look the second one.
Joe Miller: Oh, it's out of
Joe Miller: it is a member's table
Joe Miller: We just had to come over here to load status once more,
Joe Miller: and just show you the most recent jobs. When it took eight seconds, one took six seconds, right?
Joe Miller: And you can pan through each of these jobs to look at more details as to what's happening within each of them.
Joe Miller: Okay,
Joe Miller: So we're at the final face. We've gone grocery shopping. We got our data. We brought it in. We've modeled it in, and now we can go back to our content
Joe Miller: an update are orders dashboard.
Joe Miller: So let's go ahead and create a new insight around ordered versus shipped quantity.
Joe Miller: Okay, So let's go ahead and select this plus sign up here on the top, right
Joe Miller: and start building our dashboard.
Joe Miller: Okay, The first thing that we're going to do is we're going to add a new insight. In this case the insight type is tornado.
Joe Miller: If you have trouble finding it, you can use that search bar, but you can select tornado
Joe Miller: the two things that we want to compare against each other within this tornado chart is how many have been shipped versus how we've been ordered. So let's go ahead and find ordered quantity
Joe Miller: within our orderlines table.
Joe Miller: I'll bring that in as a measure,
Joe Miller: and i'm going to bring in. Shipped quantity from the over the lines table,
Joe Miller: drop those in the chat.
Joe Miller: And then, finally we needed that customer name.
Joe Miller: The one thing I forgot to say
Joe Miller: when we've updated the schema we need to make sure that the analyzer is still looking at the updated schema Right now.
Joe Miller: If you come up here to manage data set, you can see that it's still referring to the four original tables.
Joe Miller: Are you
Joe Miller: sorry? I just saw that my microphone one out there so hopefully that's better.
Joe Miller: Um! I'll let you do
Joe Miller: is select my two tables
Joe Miller: within these orders manage dataset,
Joe Miller: and now, when I search for custom, name that table should show up,
Joe Miller: so let's go ahead and bring in customer name.
Joe Miller: Now that you see that all the required trays have pills dropped within them, We're going to be rendering our first insight,
Joe Miller: it's.
Joe Miller: We can maybe provide a few tweets to this right. But the first thing that I want to show you is if I go and look at these measures and go back to this for your plan.
Joe Miller: You can see now it's smartly, knowing that it can actually query through this query: Plan to my customer account. And the nice thing is that anytime anyone goes in after this right, they will always get customer data right? So as you always build and build this data model
Joe Miller: access will still be persistent. There, Right? You won't have to re add customers at a later time.
Joe Miller: Okay, So let's go ahead and make this more legible, knowing that we have so many customers. Maybe we just get the top fifteen
Joe Miller: right?
Joe Miller: So the thing that i'm going to do is effectively. Order, order our customer names on who's ordered the most,
Joe Miller: and then sort, maybe by the top fifteen. That sounds like a good number.
Joe Miller: So i'm going to go ahead and select on our customer name, and you'll see that over here there's a configuration panel.
Joe Miller: Okay, you can actually sort these customers on a certain piece of logic
Joe Miller: our case. We're going to go to that order quantity
Joe Miller: and drag it into our salvation.
Joe Miller: So now you can actually see everything looks to be sorted,
Joe Miller: and all we need to do is limit by the top fifteen customers right? And then to do that in the quota they'll come over here to this cog, and let's just say top fifteen.
Joe Miller: It's.
Joe Miller: Still
Joe Miller: we give it a quick title.
Joe Miller: It's here.
Joe Miller: There are a few options, so we've gone our executives have come in and made a suggestion of what we need. The question is, how do we make sure that it gets back out to them right, and there's a few different options that you have at your disposal,
Joe Miller: the first of which is sharing the dashboard directly within Corda. If you're building the dashboard. You may have other bi tools that you're using, that you're using
Joe Miller: um. In that case you would use the sharing mechanisms within there.
Joe Miller: If you're doing it with any corda
Joe Miller: you can select the share, button, share access,
Joe Miller: choose a name, or a group of people that you want to share it with. I'll just say, super admin right, and choose. If they can view it, they can share it, or even edit that dashboard,
Joe Miller: the second of which is, you could actually send or schedule a report.
Joe Miller: He's.
Joe Miller: So I can come in here and say on a recurring basis, maybe every day. I'm going to send the first tab of this report into the into a certain inbox within my organization. Right?
Joe Miller: It could be an html file of the actual dashboard itself, or if you have tables built into that desk when you actually connect for the Csvs that underlie some of those insights within that.
Joe Miller: So an interesting ad as well.
Joe Miller: So that's just a few basic ways that you can share. You can even actually go a little deeper If someone has access to this dashboard they could come in, and first of all, download the direct insight if they wanted to add it to some sort of presentation,
Joe Miller: or they could quickly convert it over to kind of its raw data form
Joe Miller: and download that as an exceptional or csu. Right, you take that home with them.
Joe Miller: So in a high level. That's what I You know what we say when we talk about the agile data pipeline within in quota.
Joe Miller: Now, if you're someone who's used in quota for a while, or you're just getting started. The recommendation that I would have is start here. Go to community, dot com
Joe Miller: There you can see the discussions that are happening around quota the common questions, the ideas that people want to see coming within the platform. Also, while you're experiencing this whether you want to deploy a data app for the first time that we show it in the marketplace early on, or maybe connect to a specific source
Joe Miller: you can access our knowledge base, which is going to help you get started within the court of trial.
Joe Miller: With that I do want to thank everyone who's joined us here today? I'm going to hang on the line for just a minute and make sure that there's no questions that come in the chat before we close the session, so I am just going to hold
Joe Miller: thirty seconds to make sure that no one has anything else to ask
Joe Miller: great. No further questions. After this event you will be redirected to a survey. We'd love to hear your feedback as to how we can make this more valuable or useful of your time. But with that we want to thank you, and we'll look forward to seeing you at the next virtual hands on Lab. Thank you all.
Sr. Director, Community & Customer Enablement