The best way to get started with AWS QuickSight.
in this article, I will introduce Amazon QuickSight, and how to use it to
visualize data. Amazon QuickSight is a data visualization tool that Amazon
offers hosted on AWS and is available across multiple sites. According to the statement on the official website, it says: “Amazon QuickSight is a fast, cloud-powered business intelligence service that makes insights easy to deliver.
These dashboards can be consumed by many users and across multiple platforms like web or mobile apps. Users can also integrate these dashboards on web applications, websites, or various other portals as required. The dashboards are scalable and have an ability to interactively enable actions within the dashboards.
Sign Up for QuickSight on AWS Dashboard
In order to use QuickSight, you need to have a valid AWS account. If you do not have an AWS account, please go to http://console.aws.amazon.com/ and create an account first. Creating the AWS account is beyond the scope of this article, so I am not going to explain that in detail. Once you are logged into the AWS console, search for QuickSight and click the link as follows.
You’ll be navigated to the Amazon QuickSight site where you’ll be explicitly asked to sign up for the service. Tap on the QuickSight Signup button and continue. You’ll be given an choice to pick the version on the next page. I will pick the Standard Edition for the purpose of this tutorial, and click Continue.
In the next page, you’ll need to provide your QuickSight service with an account name and an email address where you can get updates. When you’re finished press End.
The process may take up to a few minutes and once it is completed, you will receive a message which says signup for Amazon QuickSight is successful.
Click on Go to Amazon QuickSight to start the service. You will be navigated to a different service in which you can see QuickSight being loaded with a few sample analyses already prepared.
Just to get an idea about the analysis of the samples, let’s go ahead and open the analysis of the Sales Pipeline that is available on the list. A new review will continue, as you can see, and you will be able to display the study as below.
As you can see in the above figure, this is the layout of an Amazon QuickSight analysis. On the left-hand pane, there are many icons that we can use to visualize the report which is active by default, apply filters to the existing report, create a story from an analysis, define parameters and use them as external filters, set actions to control how other visuals changed based on a fixed visual. You also have the option to define custom themes and color schemes so that the reports can be blended into the same theme as your web application or to give as a branded look of your organization.
In the second pane on the left, we have the Dataset pane, which displays a list of all the available fields that the selected data source has. The fields are categorized according to the data types, for example; string, numerical, etc. You can use these fields to create the various analyses as and when required. On the bottom, there is the Visual Types pane which enables us to select what type of visual we want to display on the reports. There are several visual types from which we can select a suitable one to use, the most common being the bar and the pies.
All the visuals will be created on the right pane where you can change the size of the visuals by simply dragging those visuals’ right handles. You may also render two or three visuals appear side by side by simply changing each of those visuals’ widths.
Creating an analysis from scratch
Now that we have some idea about the looks of an Amazon QuickSight study, let’s go ahead and build our own review. Let ‘s start by downloading a dataset into the QuickSight context. In the sake of this article, I will use the sample Superstore dataset which can be downloaded free of charge.
Select New Research for building one on the QuickSight homepage.
On the next page, you will see an option to choose from a list of available datasets for this analysis. Since we are going to use our own dataset for this analysis, let us go ahead and upload it. Click on New data set button to proceed.
On the next page, choose Upload a file to upload the CSV file from the local machine.
Choose the path on which the CSV is downloaded and select the file for upload. Once the file is uploaded, you can verify it by the following message on the screen.
To create an overview from this dataset, press Visualise. You can see that without any graphics on it a new analysis opens up. Rename the analysis as needed and go ahead with the visual development.
Now let’s go ahead and make our first picture. Let’s try to see the sales made on a bar chart by each state in the United States. Pick the visual form bar from the panel and drag and drop the State and Sales panel to the Y-axis and Value boxes respectively from the field list panel.
Choose the visual form as Line and drag and drop the Date of Order and Sales respectively to the X-axis and Value tab.
Such analyzes are automatically saved by default, which means you don’t need to specifically save them. This function can be toggled by selecting Autosave on or off as necessary.
Sharing the analyses
Now that we have finished the review we are able to publish it and share it with other users. Data can be done in two occasions. First, sharing the analysis by developing a read-only version, also known as a dashboard, and second, sharing the same analysis with others.
Let us go ahead and publish the analysis as a dashboard first. Click on the Share icon on the menu bar and select Publish dashboard.
Give the dashboard a relevant name and click on Publish dashboard.
You can choose which uses should have access to the dashboard by providing individual email addresses or selecting all users under the account. Once the dashboard is ready it will be visible to all the users under the account used.
