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Amazon (AWS) QuickSight – Getting Started

Get started with Amazon QuickSight - AWS' Business Intelligence (BI) answer to Tableau and Power BI
Instructor:
Manuel Lorenz
2,195 students enrolled
English [Auto-generated]
At the end of this course students will be able to connect QuickSight to different data sources and create their own analyses
Students will understand the basic concepts behind QuickSight and its positioning within AWS
Students will be able to dive deeper into QuickSight and feel comfortable in working with the tool and its different functions

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?

If you…

  • … 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!

Manuel

Welcome & First Steps

1
Welcome to This Course

Welcome, great to have you on board! Let's take a quick look at the content of this course.

2
What is QuickSight?

Let's understand what we can do with QuickSight and take a look at its specific components.

3
What is AWS?

We now have a basic understanding of QuickSight. But was is AWS and why do we need it to use QuickSight?

4
AWS – A Closer Look (Optional)
5
Let's Start: Creating our AWS and QuickSight Accounts

We know what we need to start our first project, so let's create our AWS and our QuickSight accounts!

6
Starting Our First Project

We created our accounts, now it's time to connect QuickSight to our source file and to prepare our data set.

7
Creating our First Visual

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!

8
Course Outline

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

1
The Development of QuickSight

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.

2
Understanding the Workflow

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.

3
Looking at the Interface

Let's take a closer look at the interface now to make sure that we always find our way when working in QuickSight.

4
SPICE - What it that actually?

SPICE is an important part of QuickSight. Let's understand why this is the case and what SPICE is actually doing.

5
Pricing and Editions of QuickSight

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

1
Module Introduction

Time to dive deeper into our course project. But before we start: Where are we now in the workflow of QuickSight?

2
Before we Start: Understanding Data Preparation in 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?

3
Loading Data: SPICE vs. Direct Query

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.

4
Introducing AWS S3

QuickSight is a part of AWS, so why not using AWS' cloud storage to upload our project data? 

5
Uploading Data and Importing Data to QuickSight

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.

6
A Closer Look at the Interface

We now loaded our source data into QuickSight. Time to understand the interface of the data preparation section.

7
Working with Columns and Fields

Let's learn how to work on columns and why we need fields in our data set.

8
Taking a First Look at Calculated Fields

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.

9
Understanding Functions and Operators

We understood how to create simple calculated fields. But what additional functions do we have in QuickSight and how can we categorize these?

10
Adding Calculated Fields using Strings to our Project

Let's apply different string functions to our project right now!

11
Extracting Information out of Strings

We already worked with string functions. Time to understand how to extract specific information out of strings.

12
Working with Conditional Functions

Time to add another calculated field to our project - This time we will add a conditional function and combine it with an operator.

13
Another Look at the IF-Function

Let's take another look at a very important conditional function, the IF-Function, and use it in our project.

14
Creating Calculated Fields with Numeric Values

We understood various functions, but we didn't work with numeric values so far. Time to change that now!

15
Adding Different Filters to our Project

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.

16
A Little Helper: Dealing with Skipped Rows

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.

17
Module Summary

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.

18
Time to Practice: Data Preparation

Analyzing and Visualizing our Data

1
Module Introduction

Before we start our analysis: What did we achieve so far and what is the goal of this module?

2
Preparing Data vs. Analyzing Data - Understanding the Differences

Let's understand why the data preparation and the data analysis are two separate steps in our project.

3
Creating the Analysis

Until now we only worked on our data set. Time to change that by creating our analysis!

4
Understanding the Interface and Creating our First Visual

Time to understand the interface of the analysis section. Let's take this chance and also create our first visual!

5
Understanding Dimensions and Measures

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.

6
Adding Additional Data Sets to our Analysis

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.

7
Understanding Field Formatting, Aggregation and Granularity

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!

8
Formatting 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.

9
Adding Drill-Down

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.

10
Creating our First Story

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.

11
Creating a Treemap

Let's add a treemap to our analysis - another great visual!

12
Applying Filters

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.

13
Understanding Pivot Tables

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.

14
Continuing our Story

Our project keeps growing, time to add another scene to our story.

15
Understanding Heat Maps

Let's add a heat map to our analysis and understand why this visual can enable great insights into our analysis.

16
Adding a KPI visual

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.

17
Adding the Final Scene to our Story

We created our last visual, so let's also finish our story now.

18
Module Summary

We finished the analysis and created a lot of great visuals. Time to summarize what we learned in this module.

19
Time to Practice: Analysis and Visualization

Refreshing, Exporting and Sharing our Project Data

1
Module Introduction

We finished the work in our project, so what now? 

2
Our Ways to Continue

Depending on our goals, we have different options now. Let's understand how we can continue at this stage.

3
Understanding Refresh and Schedule Refresh

Let's take a look at refreshing to ensure our data set is always based on the most recent source data.

4
Exporting our Project Data as .csv Files

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.

5
Adding Users to our Account - Some Theory

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.

6
AWS - More about IAM (Optional)
7
Adding a User to our Account - Continuing with the Project

We understood the theory behind the user management - Time to apply our knowledge in the project now.

8
Sharing our Data Set

We added the user, but this is it not enough. Let's learn how we can invite this user now to our data set.

9
Sharing our Analysis

After giving the user access to our data set, we want to share more. Time to understand how to share our analysis.

10
Creating and Sharing Dashboards

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.

11
Managing Capacity and Understanding Subscriptions

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.

12
Taking a Quick Look at the Mobile App

We now know how to share our data on the computer. Let's now understand how the iOS app works.

13
Module Summary

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

1
Module Introduction

Let me introduce you to this module and what you're going to learn in it!

2
Setting up a Database (Optional)

In order to connect to a database, we of course need one! Don't have one? Let's set one up together!

3
More Information about AWS RDS

Want to learn more about AWS RDS? This lecture is for you!

4
Preparing Dummy Data (Optional)

We got a database but of course we also need data for that to be of any use. Time to add such data!

5
Connecting Quicksight to a Database

With a database (with data) being available, this lecture will now teach you how to connect Quicksight to a (relational) database.

6
More Resources about Connecting Quicksight to Databases

Need more information about connecting to databases (from Quicksight)? This lecture helps!

7
Importing Data into SPICE

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.

8
Importing Data as a Query

After importing data into SPICE, let's now take a closer look at option number 2 - direct queries.

9
Calculated Fields & Query Imports

Direct queries got one other "special thing". Learn which one that is in this lecture.

10
What about NoSQL Databases?

So far, we only considered SQL databases. What about NoSQL though? This lecture explores this question.

11
Wrap Up

Let me wrap this module up and summarize what we learned thus far.

Course Roundup

1
Congratulations

You finished this course, well done! Let's now recap what we learned and how we can continue with this knowledge.

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