Unleash the Power of Data: Visualizing Insights with AWS QuickSight

In today’s data-centric landscape, the ability to turn raw information into actionable insights is paramount. Data visualization is the key to unraveling the potential within your data. Enter AWS QuickSight, a beacon of brilliance in cloud-based analytics. With the seamless connection to your securely stored data on Amazon S3, AWS QuickSight becomes your personal data artist. In this blog, we’ll explore the captivating synergy of AWS QuickSight and Amazon S3, demonstrating how this dynamic duo can transform your data into insightful masterpieces. Whether you’re an entrepreneur, data analyst, or executive, join us on this journey as we unlock your data’s full potential and harness the power of AWS QuickSight’s artistry in visualizing data. Get ready to witness your data’s beauty come to life.

Prerequisites

1. Have an AWS account. If you don’t have one, sign up here and enjoy the benefits of the Free-Tier Account
2. Download the following 2 files: listeners.csv and listeners.json (The listeners.csv is the dataset and the listeners.json is the AWS QuickSight manifest file used to configure the data source on AWS QuickSight

Data Source

Let’s first get our data to work with. You can get your date from Kaggle. They offer plenty of datasets that you can use. I chose a dataset that contains Spotify data showing artists and the number of listers they have. You can choose any of the datasets they provide. You can find the Spotify Dataset here.

Here is a peak to the first 10 records of the data:

Creating an S3 Bucket and Uploading the Data

1. Log into your AWS account
2. On the search box, search for “S3” and click on the first option that appears.
3. Give the bucket a name (the bucket name should be unique compared to all other active buckets within the region from all AWS users) and select the region to have your S3-bucket

3. On the Amazon S3 interface that appears, click on “Create Bucket”

4. Give the bucket a name and select the region to have your S3-bucket. All other settings can be left as default (Feel free to go through them and edit where you deem fit)

5. Click on “Create bucket” at the bottom of the page

6. The bucket should then appear in your “buckets” list

7. Click on the bucket name

8. Let’s have a look at the manifest file:

{
   "fileLocations": [
       {
           "URIs": [
               "s3://BUCKET-NAME/listeners.csv"
           ]
       }
   ],
   "globalUploadSettings": {
       "format": "CSV",
       "delimiter": ",",
       "textqualifier": "\"",
       "containsHeader": "true"
   }
}

The manifest file is used by AWS QuickSight to get the data source. You can have a look at the description and structure of the manifest file here. Move to the URIs and edit it by replacing ‘BUCKET-NAME’ with the name of your bucket. Save the file

In our case, it will be as follows:

{
   "fileLocations": [
       {
           "URIs": [
               "s3://kev-quicksight-bucket/listeners.csv"
           ]
       }
   ],
   "globalUploadSettings": {
       "format": "CSV",
       "delimiter": ",",
       "textqualifier": "\"",
       "containsHeader": "true"
   }
}

8. Click on “Upload” (They are 2, any can do)

9. On the upload page, Click on “Add files” then navigate your local storage to the listeners.csv and listeners.json files that we downloaded. You may also drag and drop them onto the page.

10. Click on Upload at the bottom of the page

11. The files are uploaded and are now visible on S3

Setting up AWS QuickSight

1. On the Services search bar at the top, searck for ‘quicksight’ and select the first option that appears

2. In the event you have not signed up for AWS QuickSight on your account, Click on ‘Sign up for QuickSight”

3. Select the Enterprise edition to get a Trial period for 30 days (You can cancel the subscription before the 30 days are over to avoid unnecessary charges) Click Continue

4. Select your Authentication method. Select the region to host AWS QuickSight, select a username and an email address that you can use for notifications

5. Under “Allow access and autodiscovery for these resources”, Select Amazon S3 to allow access and select the bucket that you created with our data and manifest files. Then Click “Finish” at the bottom

6. Give QuickSight some time to create your account (Takes less than a minute).

Visualize your Data on AWS QuickSight

1. Once AWS QuickSight loads, it presents a dashboard. Click on datasets, the “New dataset”

2. On the Data sources, Select S3

3. Give the Data source name (You can choose any name you wish)

4. Give the url of the manifest file. To get the url of the manifest file, select “listeners.json” from your uploaded files on S3 and copy the S3 URI.

5. Paste the S3 URI of the manifest file then click ‘Connect’

6. Click on “Visualize”.

7. In some cases, AWS QuickSight may not access S3, if it fails, follow this guidelines on granting QuickSight access to S3

8. select a Tiled Layout then click on ‘Create’

9. AWS QuickSight presents its workarea showing the Fields (Data Columns), 1 visual (You may choose to add more visuals) and the Visual Types for the data

10. Let’s do a sample data visualization. We will create a visual of Artists against Listeners. Click on ‘Artists’ on the Fields list then click on Listeners. A visual of the data appears on the Visual space

11. The data was too much, let’s do a filter, say we want to view the Artists with more than 75,000,000 listeners.

12. Click on Filter, then ‘ADD FILTER’ then select ‘Listeners’ as this is the Field we want to filter by

13. Select Listeners

14. Set Aggregation to Sum, Filter condition to ‘Greater than’ and the Minimum value to ‘75000000’ (There are many more options that you can explore). Then click ‘APPLY’ at the bottom

15. The visual changes to show you the data that meets the criteria

16. You may also change the Visual type to show the data in different formats. The following are some examples.

Additional Practice

AWS QuickSight offers a wide range of features for data visualization and analysis. Here are some key features that you can practice and explore:

1. Data Source Connectivity: AWS QuickSight allows you to connect to various data sources, including Amazon S3, Amazon RDS, Redshift, SQL Server, and more. Practice connecting to different data sources to import data into QuickSight.
2. Data Preparation: You can perform data cleaning, transformation, and shaping operations within QuickSight, such as filtering, pivoting, and aggregating data. Practice cleaning and preparing your data to make it suitable for visualization.
3. Interactive Dashboards: Build interactive dashboards by combining multiple visualizations on a single canvas. Practice designing dashboards that convey insights and allow users to explore data interactively.
4. Custom Calculations: Use QuickSight’s formula editor to create custom calculations and calculated fields. Practice creating calculated fields to derive new insights from your data.
5. Usage and Performance Monitoring: Monitor usage and performance of your dashboards and analyses to optimize and scale your QuickSight deployment effectively.

By practicing these features, you’ll gain a deeper understanding of how AWS QuickSight can help you analyze and visualize data, turning it into valuable insights for your organization.

Well that’s it for this article. We’ve seen how AWS QuickSight works smoothly with Amazon S3 to help us analyze data easily. Using the power of the cloud, AWS offers lots of tools to help us do even more with data. I encourage you to keep exploring AWS and QuickSight to find more ways they can help you in your work or projects. Lastly, a big thank you to Lucy Wang from the Tech with Lucy YouTube Channel for the idea that got us started on this journey. It’s teamwork and ideas like these that make the AWS community so great. Together, we can keep learning and doing exciting things in the world of cloud computing.