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I am a cloud enthusiast with expertise in designing scalable and secure cloud solutions. I have hands-on experience in Docker for efficient containerization and application deployment. Passionate about simplifying complex technologies, I share my knowledge through informative blogs to inspire and educate others in the tech community.
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The ability to transform raw data into useful insights is critical in today's data-driven world. The secret to unlocking the potential in your data is data visualization. Amazon QuickSight is a cloud-based business intelligence (BI) service provided by Amazon Web Services (AWS) that specializes in data visualization. It enables organizations to create interactive dashboards, analyze data, and generate insights through visualizations. QuickSight is designed to be scalable, user-friendly, and cost-effective, making it suitable for businesses of all sizes.
In this blog, we’ll explore the powerful integration of Amazon QuickSight and Amazon S3, showcasing how this dynamic pair can transform your data into impactful insights and compelling visualizations.
Have an AWS account. If you don’t have one, sign up here free and enjoy the benefit of 12 month free tier account.
Download the datasets from Kaggle. In this demo, I use Netflix dataset from Kaggle and other manifest is used to define the data source and data import configuration in Quick sight.
Open AWS management Console and search S3.
Click on the Create Bucket.
Give the bucket name unique , leave all the default setting as it is and click on the create bucket.
Click on the bucket that looks like this:
Upload the dataset that we downloaded Earlier.
Now, we have created a bucket which contains our netflix.csv and manifest.json file and copy the netflix_titles.csv URI.
Open manifest.json file and change the file location URI with your S3 URI which is copied earlier and re-upload the manifest.json file in your s3 bucket.
Search for Amazon QuickSight in AWS management Console.
Select Sign up for QuickSight.
From the left hand navigation bar, select Datasets, then New dataset.
Select S3.
For the first field (data source name), enter netflix-data.
Ooo there's also a second field called manifest.json URL - does manifest.json found familiar?
Open a new tab to open your AWS Management Console again. Head back to your S3 bucket.
Select the checkbox next to manifest.json, then select Copy S3 URI.
Now we get to the creative part! With QuickSight, you can sort, filter, and customize your data to create visualizations. You can also experiment with different types of graphs like bar charts, pie charts, line graphs, etc.
We can see on the left hand panel that the dataset's fields are already imported.
Now let's create a new visual, select + ADD under the Visuals heading on our middle navigation bar, and we'll see another blank frame pop out.
Drag the release_year label into the Y Axis heading.
Next, drag the type label into the Group/Color heading.
Now, Let’s demonstrate the same thing on table format.
Finally, we can see our dashboard looks like this.
Double-click on the titles of all the charts you see in front of you, and the 'Edit Title' panel will pop up. You can give each chart a name which is clear and easy to understand.
Final step, On the top right hand corner, select Publish. This will make your dashboard public so you can share it with your team.
In this project, we explored the powerful capabilities of Amazon QuickSight to transform raw data into actionable insights through intuitive visualizations that enable better decision-making. Through this hands-on exploration of QuickSight, we demonstrated its potential to enhance data-driven decision-making within businesses by providing intuitive, real-time visual analytics. The flexibility, scalability, and cost-effectiveness of QuickSight make it a powerful tool for organizations looking to harness the value of their data.
Amazon S3: Amazon S3 - Cloud Object Storage - AWS
Amazon QuickSight: Business Intelligence Tools - Amazon QuickSight - AWS