Monitoring Quicksight

Monitor Quicksight with Cloudwatch and custom metrics

In the realm of cloud computing, the prowess of effective monitoring cannot be overstated. Metal Toad, a frontrunner in cloud solutions, is well-versed in the art of ensuring seamless operations. This blog post delves into how Metal Toad harnesses Amazon CloudWatch to monitor AWS QuickSight and the broader data pipeline, offering insights into their approach and best practices.


Understanding AWS QuickSight:

AWS QuickSight, Amazon's cloud-based business intelligence (BI) service, is integral to organizations for scalable and cost-effective data analysis and visualization. Monitoring QuickSight for performance and errors is paramount as businesses increasingly rely on data-driven decision-making. For an in-depth comparison of BI tools like QuickSight and Power BI, check out Metal Toad's blog post on Choosing the Right BI Tool: A Comparison of Power BI and Amazon QuickSight, which can aid in making informed decisions regarding BI tool selection.


The Role of Amazon CloudWatch:

Amazon CloudWatch, a comprehensive monitoring and management service, becomes the linchpin for Metal Toad's monitoring strategy. It provides real-time insights into AWS resources, including QuickSight, and allows for proactive issue identification and resolution.

Monitoring Quicksight begins with the datapipe line. You need to ensure that data export, ETL Jobs, and Quicksight ingestion is running successfully. Key Metrics that Metal Toad monitors around this are:

  • Lambda Errors: Its common for Metal Toad to export data from sources using a lambda. By monitoring lambdas for errors we can catch pipeline problems early in the process
  • AWS Glue Job Runs: Cloudwatch allows you to monitor the number Glue jobs being Run. 
  • Cloudwatch Logs: Some metrics aren’t available natively via Cloudwatch For those we create alarms for Crawler and Glue job errors to alert. 
  • Quicksight Ingestion: Quicksight ingestion is another thing that doesn’t have a metric or log in Cloudwatch. To detect and alert on this, Metal Toad has a lambda run to monitor ingestion jobs and alert on success and errors as custom metrics. 

With the pipeline monitored Metal Toad will also be able to monitor Key Metrics of Quicksight it self.

  • Dataset Ingestion Row Count:

Monitoring the Row Count for ingestion allows you to alert on errors that are outside of a predicted band. 

  • Dashboard Load Times:

At its most basic Quicksight is a website. So ensuring that the dashboards load quickly to keep users engaged is critical. 

  • User Activity Metrics:

Tracking the number of views is an important metric to see engagement especially after changes to dashboards or features. 


Setting Up CloudWatch Alarms:

Metal Toad employs CloudWatch Alarms judiciously, setting up notifications when predefined thresholds are breached. For example:

An alarm triggers when the average Dashboard view load time in QuickSight exceeds a specified limit. Alarms are set for high error rates across the data pipeline to prompt immediate investigation.

Utilizing CloudWatch Dashboards:

CloudWatch Dashboards serve as a centralized hub for Metal Toad, providing a unified view of key metrics and alarms for QuickSight and the broader data pipeline. Customization enables teams to quickly identify trends, anomalies, and potential issues.



Metal Toad's strategic use of Amazon CloudWatch for monitoring AWS QuickSight and the broader data pipeline underscores their commitment to delivering reliable and efficient cloud solutions. By leveraging CloudWatch's robust features, Metal Toad ensures that QuickSight operates seamlessly, empowering organizations to harness the full potential of their data-driven insights. As businesses evolve in the cloud, Metal Toad's monitoring approach serves as a blueprint for achieving excellence in performance, reliability, and scalability across the entire data pipeline.

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