Your team is working on an IoT application.
The data generated from the application is saved in S3 buckets and a Redshift data warehouse.
You need to design interactive dashboards to analyze the data and share with other team members every day.
With the dashboards, the team can explore the data further and get more insights.
You have a limited budget on this assignment.
Which of the below solutions is the most cost-effective?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer - C.
There are two requirements in the question.
One is creating interactive dashboards.
The other one is being cost-effective.
AWS QuickSight is an ideal service to achieve these two requirements.
Check the introductions of QuickSight in https://aws.amazon.com/quicksight/.
Option A is incorrect: Tableau is a powerful business intelligence (BI) tool and has more features.
However, it is not a free tool and this solution is not cost-effective.
Option B is incorrect: Because CloudWatch metrics/dashboards cannot be used to generate data insights from S3 or Redshift.
Option C is CORRECT: QuickSight is a BI service which can generate rich and interactive dashboards.
It also has a pay-per-session pricing and is a cost-effective solution.
Option D is incorrect: Because Kinesis cannot be used to create personal dashboards for data analysis.
There is no service called Kinesis dashboards.
Among the given options, the most cost-effective solution for designing interactive dashboards to analyze the data generated by an IoT application, which is stored in S3 buckets and a Redshift data warehouse is Option A - Configure free third party tools such as Tableau. Design customized dashboards to help analyze the data using graphs, numbers, and charts.
Option A is the most cost-effective solution as it involves using free third-party tools such as Tableau, which provides customized dashboards for analyzing data using graphs, numbers, and charts. Tableau has a range of features that allow users to create interactive dashboards and visualizations that can be shared with other team members. Additionally, Tableau can connect to various data sources, including S3 buckets and Redshift data warehouses, which makes it an ideal choice for analyzing the data generated by the IoT application.
Option B involves generating CloudWatch metrics from the data stored in S3 buckets and Redshift and designing CloudFormation templates to create CloudWatch dashboards for analyzing the metrics. While this option is suitable for monitoring and analyzing metrics in real-time, it may not be the best option for creating interactive dashboards that can provide insights into the data generated by the IoT application.
Option C involves enabling QuickSight in AWS and using it to analyze the data, visualize the data, and publish interactive dashboards that include machine learning insights. While QuickSight is a powerful tool for analyzing data and generating interactive dashboards, it may not be the most cost-effective solution for a limited budget as it requires a subscription fee.
Option D involves configuring Kinesis Data Firehose and Data Analytics to collect and process the data in real-time and generating personalized content in Kinesis Dashboards for analysis. While this option is suitable for processing and analyzing real-time data, it may not be the most cost-effective solution for analyzing the data generated by the IoT application.
In conclusion, Option A - configuring free third-party tools such as Tableau to design customized dashboards to analyze the data using graphs, numbers, and charts is the most cost-effective solution for designing interactive dashboards to analyze the data generated by an IoT application that is stored in S3 buckets and a Redshift data warehouse.