Netflix Movies Analysis
Netflix Movies Analysis
A brief visual-analysis of the Netflix Movies using Amazon Quicksight
Embedded Files
How I used Amazon Quicksight to Visualize Data?
What Were the Learnings ?
Milestone 1
Milestone 1
S3 Bucket Creating
Milestone 2
Milestone 2
Data Upload
Milestone 3
Milestone 3
Data Visualization
Milestone 2
Milestone 2
Data Presenting
I. Introducing Amazon S3
What it does ? How is it useful ?
What it does ? How is it useful ?
How I am using in my project?
How I am using in my project?
II. S3 Bucket Creating & Access Control
Some common configuration steps to consider :
The bucket’s Region: I choose AWS East which is nearby to my location which cause low latency
Access Control Lists: I chose bucket policies for general purpose
Bucket versioning: It allows to store multiple versions of the bucket, I left default.
Public Access: I set as publicly accessed for bucket, which means it can be accessed from anywhere with right request.
III. Data Structure
Following csv data and table structure was used for analysis purpose.
IV. Bucket Contents
Following data & support file has been uploaded:
netflix_titles.csv
Source: Kaggle
manifest.json: it was updated with the appropriate URI so that connection to dataset would go to the proper address.
V. Amazon Quicksight
Quicksight, need to be created as an individual application and connected using updated manifest file with the previously uploaded dataset. This is where the edit of manifest file is significant.
Quicksight had to access to S3 for connection purpose. It did not costs at the moment but had to delete the resource use after activities were done to avoid costing.
VI. Visualization
To initiate visualization, columns were dragged into AutoGraph space by choosing the appropriate visualizations type and configuring its settings to modify as per the need of analytics.
VII. Data Preparation for Time-Bound Analytics
For Time-bound Analysis filters were used here to subset from the data columns excluding irrelevant data. Here, only 5 categories were filtered for the period of 2015 and onwards.
No. of Releases
Observe the no. of Movies and TV Shows release at the initial and last 10 years. Initially, Movies were the most, but finally TV Shows were the only production.
VIII. Dashboard Preparation & Publishing
Titles of the graphs were updated for appropriate title.
Exporting features of either of pdf or image formats were observed.
Finally the dashboard was allowed to publish and similarly links for sharing was also available.
IX. Key Learning
Setting up Amazon S3 was straightforward, while the initial configuration for Amazon QuickSight felt a bit confusing. However, the default setup proved to be highly effective for standard use cases.
Amazon QuickSight is an excellent, interactive analytics tool. Creating visualizations was easier than expected, thanks to its user-friendly interface and helpful guided prompts.
The variety of visualization types and the ability to apply filters and calculated measures stood out as game-changing features, making data analysis more insightful and efficient.
Features like dashboard collaboration and exporting options further enhance its usability, making it a fantastic tool for both individual and team analytics.
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