Data Visualization - Amazon Quicksight


Big Data - Data Visualization
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In this course, we learn how to determine the appropriate techniques for delivering the results/output of a query or analysis. We examine how to design and create a visualization using AWS services, and how to optimize visualization services to present results in an effective and accessible manner. We introduce and outline the core AWS analysis tools and then work through how to integrate and output data to enable business decisions using QuickSight.

Amazon QuickSight makes it easy to build visualizations, perform ad hoc analysis, and quickly get business insides from your data. It has a number of pre-configured reports which take out the undifferentiated heavy lifting of creating visual reports. A benefit of QuickSight is that it's integrated into our AWS dashboard and our AWS account, and this is where reports and graphs can be viewed by team members, staff, etc.

QuickSight also makes it easy for business teams to create and share interactive graphs and reports as stories, and if we have any additional data sources added in the future, those can just simply be added as Amazon QuickSight database sources. When we create visuals using QuickSight, the style and format of graphs are automatically selected by the QuickSight engine, which saves time and improves the quality of reports and visuals.

Learning objectives

  • Recognize and explain how to determine the appropriate techniques for delivering the results/output of a query or analysis
  • Recognize and explain how to design and create data visualizations
  • Recognize and explain the operational characteristics to gain simple and timely results from Amazon QuickSight


Big Data - Collection 

Big Data - Processing


Right, let's create a visualization from a data source. Now, we can import our own data using the new dataset import wizard, as we've said already. We can use one of the demo sets that are provided by QuickSight. So we'll use our sales pipeline dataset and we'll import that and create a new visualization from it. So, the wizard has stepped us through that, we've had 100% success with the import. That tells us the rows that we've added, what was skipped, et cetera. What we can do at this point is we can edit the dataset, so if we're looking at changing some of the field names or adding some further descriptions or even a calculated field, then this provides us with an opportunity to do that. So, we might change this to be, just, lead name, for example. And we apply that, and that will change our dataset records for us. So, it's very easy to work and manipulate the data before we do anything. So, we'll save this and go forward and create a visualization. 

Now, a key benefit of QuickSight is that it uses its rendering engine to best render the graph that suits the data that you've selected. So, if we leave this button here chosen, that option, the auto-graph feature will do its best to format the graph in the most efficient way for us, based on the data that we select. Now, down here, we've got our field types, and, as an example, let's just select 'Region', and the auto-graph feature has broken this down into a bar graph for us to show the three regions. We can alter and edit, though. I'm gonna open up the 'Field Wells' section here at the top. From here, we can add and change our dimensions. So, let's say we want to add, 'Opportunity Stage'. In here, we can edit, and auto-graph feature has broken that down to a bar graph for us, and given us a key to everything, which is fantastic. 

Now, we're also able to drill down in any of these areas by adding another dimension to any of these group wells at the top here. So, under 'Region', we might add 'Salesperson'. as a drill down, let's do that. So, there's two options here when you add it to a field well, you can either replace the current value, or add it as a drill-down layer. You see that difference, just showing up in the menu there? So if we add it as a drill-down, that means that when we click into any of these options, we'll get the additional option of a drill-down. As an example, if I go into here, see how for our APAC region, it allows me to drill down to the 'Salesperson' label, which is this other dimension, which we added to the field well. Okay, so we could just do that now. And so, straight away, that gives us a very, very powerful tool for rendering dynamic data. We may want to change this to be a vertical graph, like, it might be a bit easier to read there. So we've got the salesperson's name here, then we've got a breakdown of their activity in this form. 

Could we go deeper? Yes, we could add another layer into this if we wished. So, what about target close? Why don't we add that? Now, you'll see here, it'll tell you if it's not an acceptable or compatible drill-down type. If we add in it there ... We now have a target close count earlier on the left-hand axis here. Okay, so if I click back into this, I can drill back up to the region detail, and we've changed our format to be a bar graph. Again, once we've changed or altered the layout to a specific level, it will stay with that. We can always go back to the auto-format, just by clicking that feature. We can now change the layout of this to suit better, so for one thing we will do is just change the format. So, in here, we can format a visual, and this allows us to set the legend type where the layers, sorry, where the labels are, and what the label will say, and it also allows us to add a range of values if we want them. 

Now, the best way of doing that type of filtering is actually from the wells themselves. So, here's an example. We've got 'Region' here, why don't we change or add date value so we'll just get out here, by clicking this arrow here, this box here, that takes us back to the main fields, and I'm going to add a date, into here, or add a drill down there, and a date for target close. Alright, now if I drill down into here, it's defaulted to months, and it gives me a breakdown of values again, I can drill down to week. The date field is, by default, broken out into various dimensions, like year, month, week and day. You can add those dimensions just simply by doing, the same process with other, non-date fields. And we can also change the layout type, so if we want to format this to be a little different, we go back into our format menu here, and we go 'Format Visual'. In here, we can do some pretty clever things, like we can change, if this was a date field ... Now at the moment we've got this graph, so what we can do straight away is just, make this a bit smaller, and then we'll add a new one. So that's given us a great quick view, of opportunities by close, and by region. So let's create a new one by doing that, we just click here, 'Add Visual', and we can format this to be whatever way we like, and for this, let's go 'Date', and 'Salesperson'. Okay, so the order graph feature has given us a line graph, which quickly, and easily let's us view, where each of the sales people is, on these dimensions of year. 

Well let's change this to be, a little more granular, so under our 'Field Wells', under 'Date', we'll drill down here, and we'll make the aggregate month. So this is going give us a lot more detail, with granularity on the report. And, straight away, we can see we've got a different view, we also have the slide bar here, which allows us to scroll in between months. So, again, just really, really, simple things that just take out all the undifferentiated heavy lifting, with creating graphs and reports. These can all be done very, very easily. Okay, so we make this a little smaller just by dragging the corner of our display box here. We can move it 'round; let's put it up next to the other report there. And, as you can ... No doubt, since it's very easy to create a dashboard report in this way, which is going to be dynamic. So, we've got account of records by salesperson and date, we've got the salesperson, we've got our target close. 

Let's do one more. We'll add a visual here, and for this one, we'll do it by segment. So first off, we'll add that as a variable. Let's add amount into our y-axis. We'll add this into here, as a drop down layer. And if I make this a pie chart, when I drill into this, I can drill down to the forecast of monthly revenue for our start up. So just great functionality there, and if I wanna move back, I get drill back up to segment. So, we can change a lot of these things, let's just change this layout slightly, we'll make it, what about a scatter point. Here we are, that's quite cool. We can see it's not really the relevant data for a scatter point graph but there's a lot of different options, you have in here, which is really, really cool. It can make it very, very easy to visualize your results, in a number of creative ways. If I go back to the 'Wizard', it's gonna give me just the straight bar graph. That's fine. I've already got a drill down here, to show my revenue. We've revenue value on this y-axis here. And, it is possible for us to change this. It's defaulted to give us a revenue value. Now we have the sum of weighted revenue by segment. 

So that's a sum value, we can change that if we wish. We can make it an average, we can make it the count, a number of different varieties we can add here, so if we do make it ... Well sum is best for the report, so we'll leave it at that. We can drill down into, month forecast, and monthly revenue on top of that. So that's another great report we have right there, we can minimize this to fit into our dashboard. We will do one more, just to show us a cumulative revenue, I think. So we'll go 'Edit Visual'. So let's start there again, so the first thing we'll add is date. And the field wells populate immediately, and we'll add forecasted revenue here, against our date. Alright, so that's given us a line graph, that shows us our forecasted revenue summer. And, why don't we just add weighted revenue in there. There's another dimension. There, very simple. Now we can add another drill down of course, so if we want to make it on a data point we can add a drill down for that. We change the colors and the layout, we can add titles, all are easy to do. So we'll just minimize this. Yes, that fits on a page. Great tool. You know, you can just save yourself so much time, using Quick Sight. 

Here we are, so we've got quite a nice little, dashboard summary there, that's interactive and dynamic. So we can now save this as a story, if we go 'Story', we add the capture button, up here. Okay, and that captures our story board as it stands and calls it a scene. So we'll call this, 'Monthly Sales Report' and we'll go, play that out. So our dynamic dashboard looks like this, we can drill into any of these areas, and it shows us a full report, which is really, really cool. Okay, so we'll stop the story, and save it, and then we can share it. 

So if we wanted to share it, we'll create a dashboard first. So, 'The Monthly Sales Report', create that dashboard, and then we can send it out to our team. Now, how easy is that? And we'll choose me, and share it. Yeah, very cool. Alright, so I'm going to get a link this, it's going to be a dynamic page, I'm able to download it, dive into, and drill down into each of these variables, as we set them up to do. It's a super powerful tool, super powerful, each of these is dynamic, as you can see here. So it's possible for people to drill down and do whatever they like with the data, drill back up to region. Fantastic! How cool is that? And, you know, we can go a bit further, with the stories as well, in that, you know, we can create an animation basically, so we can animate the diagram, we can do a walkthrough and save that, and then send that animation out, just to save people time. Most people, I think, prefer just being able to interact with the graphs themselves, and most people, I think, have that basic skill. 

About the Author
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Andrew is fanatical about helping business teams gain the maximum ROI possible from adopting, using, and optimizing Public Cloud Services. Having built  70+ Cloud Academy courses, Andrew has helped over 50,000 students master cloud computing by sharing the skills and experiences he gained during 20+  years leading digital teams in code and consulting. Before joining Cloud Academy, Andrew worked for AWS and for AWS technology partners Ooyala and Adobe.