Amazon (AWS) QuickSight – Getting Started
Working with modern Business Intelligence tools is exciting. Although the market offers a broad variety of tools, you may not have found the tool that meets all your requirements yet. This course might change that!
In this course, you will learn how to use one of the latest Business Intelligence tools released to the market: Amazon QuickSight, a tool which allows you to easily analyze and visualize data. But what makes QuickSight special? QuickSight is a cloud solution and completely integrated into Amazon Web Services (AWS). With that, it can be easily connected to a broad variety of services and sources which make QuickSight a highly scalable, easy-to-use and very flexible data analysis tool.
This course will give you a first overview of QuickSight including the following topics:
- How to use QuickSight and its different functions
- Understand the workflow of QuickSight
- How to connect QuickSight to different data sources within and outside of AWS
- How to prepare your data in QuickSight, for example by adding filters and calculated fields
- How to easily create your analysis by building multiple visuals
- How to create dashboards and stories
- Share your project results with people within and outside your organization
- How to use the iOS mobile app
- Understand the user management of QuickSight
- And more!
These topics will be covered throughout this course, but is this your course?
- … never worked with QuickSight and want to get started with it
- … are looking for a cloud based Business Intelligence tool to quickly analyze your data
- …have worked with other Business Intelligence tools but want to take a look at new tools
- … already worked with AWS and now want to understand how to analyze and visualize your data using a service within the AWS universe
- …are generally interested into data analysis
…then this course is made for you!
I would be really happy to welcome you in this course!
Welcome & First Steps
Welcome, great to have you on board! Let's take a quick look at the content of this course.
Let's understand what we can do with QuickSight and take a look at its specific components.
We now have a basic understanding of QuickSight. But was is AWS and why do we need it to use QuickSight?
We know what we need to start our first project, so let's create our AWS and our QuickSight accounts!
We created our accounts, now it's time to connect QuickSight to our source file and to prepare our data set.
After finishing our data set, let's now take a look at the analysis and see how easy it is to create our first chart!
Let's take a more detailed look at the structure of this course and at the different topics we will cover.
QuickSight - Starting with the Basics
QuickSight is a relatively new business intelligence tool. Let's take a look at the development of the last months and also see how we can ensure to always be up-to-date regarding new features and functions.
When working with data, structure is a really important topic. Therefore, understanding the workflow of QuickSight will help us to keep track of our project.
Let's take a closer look at the interface now to make sure that we always find our way when working in QuickSight.
SPICE is an important part of QuickSight. Let's understand why this is the case and what SPICE is actually doing.
The availability of different editions and the pricing can be confusing when starting to work with a tool. Let's avoid that confusion and take a look at the Standard and the Enterprise edition of QuickSight.
Preparing our Data
Time to dive deeper into our course project. But before we start: Where are we now in the workflow of QuickSight?
Generating great results is highly dependent on the quality of the source data. What data preparation steps can be done in QuickSight and where might additional tools be required?
We can connect QuickSight to a broad range of data sources. Let's learn why the data source connection type has an impact on our data preparation steps.
QuickSight is a part of AWS, so why not using AWS' cloud storage to upload our project data?
Time to learn how to create a bucket in S3 and to upload our project file to this bucket. After our data is in S3 we need to import it to QuickSight. Let's understand how all this works.
We now loaded our source data into QuickSight. Time to understand the interface of the data preparation section.
Let's learn how to work on columns and why we need fields in our data set.
We already have data in our source file, but what if we need to add additional calculations? Let's understand how to do this using calculated fields.
We understood how to create simple calculated fields. But what additional functions do we have in QuickSight and how can we categorize these?
Let's apply different string functions to our project right now!
We already worked with string functions. Time to understand how to extract specific information out of strings.
Time to add another calculated field to our project - This time we will add a conditional function and combine it with an operator.
Let's take another look at a very important conditional function, the IF-Function, and use it in our project.
We understood various functions, but we didn't work with numeric values so far. Time to change that now!
We have a lot of data in our data set. Let's focus on the important information and understand the different filter types in QuickSight.
Everything worked fine in our project - but what if we run into problems? Let's understand some general error sources and how we can avoid them.
We finished the data preparation, created our own data set and are now ready to start the analysis. Really great, but let's first summarize what we learned.
Analyzing and Visualizing our Data
Before we start our analysis: What did we achieve so far and what is the goal of this module?
Let's understand why the data preparation and the data analysis are two separate steps in our project.
Until now we only worked on our data set. Time to change that by creating our analysis!
Time to understand the interface of the analysis section. Let's take this chance and also create our first visual!
When creating our visual we saw that we have two different field types. Let's understand the differences between a dimension and a measure and the role of these items in our charts.
So far we worked on our course project data set. Let's now understand how we can add an additional data set to our analysis.
After creating our first visuals we will dive deeper now. Time to take a closer look at formatting, aggregation and granularity to improve the quality of our visuals!
We understand how to define what should be visualized. Let's now see how we can work on the formatting of our visuals.
Our data has different aggregation levels. Let's add a drill-down to our visual to be able to display different levels of detail in a single visual.
After finishing the first part of our analysis, it's time to start our story. Let's understand what stories are and how we can create them.
Let's add a treemap to our analysis - another great visual!
We already talked about filters in the last module. Let's understand the differences between a filter in the data preparation module and in this module and see how we can apply filters to our analysis.
Time to take a look at pivot tables, a special visual. Let's understand how we can create it and what calculations we can apply to it.
Our project keeps growing, time to add another scene to our story.
Let's add a heat map to our analysis and understand why this visual can enable great insights into our analysis.
Time to create our last visual. Let's understand KPI's and take a look at the different analyses we can perform using this visual.
We created our last visual, so let's also finish our story now.
We finished the analysis and created a lot of great visuals. Time to summarize what we learned in this module.
Refreshing, Exporting and Sharing our Project Data
We finished the work in our project, so what now?
Depending on our goals, we have different options now. Let's understand how we can continue at this stage.
Let's take a look at refreshing to ensure our data set is always based on the most recent source data.
We created visuals, but what if we only need the raw data behind that visuals? Let's find out how we can export this data.
We want to share our project with other users in QuickSight. To do this, we need to add users to our QuickSight account first. Time to understand how we can manage users and their different roles in QuickSight.
We understood the theory behind the user management - Time to apply our knowledge in the project now.
We added the user, but this is it not enough. Let's learn how we can invite this user now to our data set.
After giving the user access to our data set, we want to share more. Time to understand how to share our analysis.
Sharing of data sets and analyses is clear now, but what are dashboards? Let's take a closer look at these and also understand how we can share dashboards with other users.
After learning how to export and share our data, it's time to take a closer look at the specific features behind our QuickSight account.
We now know how to share our data on the computer. Let's now understand how the iOS app works.
Data export, data sharing and account management is clear for us now. Let's recap what we learned about these topics.
Databases as Data Sources
Let me introduce you to this module and what you're going to learn in it!
In order to connect to a database, we of course need one! Don't have one? Let's set one up together!
Want to learn more about AWS RDS? This lecture is for you!
We got a database but of course we also need data for that to be of any use. Time to add such data!
With a database (with data) being available, this lecture will now teach you how to connect Quicksight to a (relational) database.
Need more information about connecting to databases (from Quicksight)? This lecture helps!
We set up a connection to the database, now the question is how to get the data into Quicksight. There are two options, let's start with the SPICE option in this lecture.
After importing data into SPICE, let's now take a closer look at option number 2 - direct queries.
Direct queries got one other "special thing". Learn which one that is in this lecture.
So far, we only considered SQL databases. What about NoSQL though? This lecture explores this question.
Let me wrap this module up and summarize what we learned thus far.
You finished this course, well done! Let's now recap what we learned and how we can continue with this knowledge.