AWS customers are looking for an efficient tracking method of support cases raised with AWS Support across their multiple interconnected accounts. Having a unified view lets the cloud operations team derive actionable insights across the support cases raised by different business units and accounts. This helps ensure that the team has a comprehensive understanding of the state of existing support cases and can quickly identify and work with teams to resolve them. The team can also prioritize their responses based on the severity of impact of the issues and take action on cases that need acknowledgement or additional information. AWS Systems Manager is the operations hub for your AWS applications and resources and a secure end-to-end management solution for hybrid cloud environments that enables secure operations at scale. AWS Systems Manager Explorer provides a summary of support cases across your AWS accounts to help you get better visibility into the operational health of your AWS environment.
This post describes how Amazon QuickSight dashboards can help you visualize your support cases in a single pane of glass using data extracts from Systems Manager. QuickSight meets varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics, and natural language queries.
The following architecture diagram illustrates the use of Systems Manager to provide a summary of support cases across your AWS accounts. The solution automates the collection process using a Systems Manager Automation document, scheduling automations within a maintenance window. When the Systems Manager configuration is done, the automation extracts the all support cases across the organization and creates a CSV file in an Amazon Simple Storage Service (Amazon S3) bucket. From the S3 bucket, we integrate with Amazon Athena to create a table, and lastly we visualize all support cases in QuickSight. Note that for aggregating data across multiple accounts, they must reside within a single AWS Organization. Implementing the solution requires the following steps:
- Set up a Systems Manager maintenance window.
- Register an automation task in the maintenance window.
- Create a database in the AWS Glue Data Catalog.
- Create a custom classifier for an AWS Glue crawler.
- Create and run an AWS Glue crawler.
- Create views in Athena.
- Visualize AWS support cases in QuickSight.
Before you get started, complete the following prerequisites:
- Have a Business or Enterprise support plan for your AWS accounts.
- Enable and set up Athena.
- Enable QuickSight in your data collection account. For instructions, refer to Setting up for Amazon QuickSight.
- Create an S3 bucket where Systems Manager Automation will export support cases.
- Follow the steps in Centralized view of support cases opened from multiple AWS accounts using AWS Systems Manager to establish Systems Manager Explorer and create a resource data sync for data aggregation.
- Create an Amazon Simple Notification Service (SNS) topic. Use the following command to create an SNS topic named
SSM-supportcases-notificationand subscribe an email address:
You should see the following output:
For more information, refer to Creating an Amazon SNS topic.
- Have an AWS Identity and Access Manager (IAM) Systems Manager Explorer Exporting OpsData role. The role
AmazonSSMExplorerExportallows Explorer to export OpsData to a CSV file. For more information, refer to Exporting OpsData from Systems Manager Explorer.
- Have Systems Manager permissions for maintenance windows. For more information, refer to Use the console to configure permissions for maintenance windows.
After you have all the prerequisites in place, follow the step-by-step instructions in the rest of this post.
Set up a Systems Manager maintenance window
Maintenance windows, a capability of Systems Manager, help you define a schedule for AWS support cases to extract at a predefined schedule. For instructions on creating a maintenance window, see Create a maintenance window (console).
Register an automation task with a maintenance window
In this step, you add a task to a maintenance window. Tasks are the actions performed when a maintenance window runs. For instructions on registering an automation task to a maintenance window, see Schedule automations with maintenance windows.
- Provide a name for the maintenance task and choose the automation document
2. Enter the following details in the Input parameters section.
||(Required) The role ARN to assume during the automation run||The role you created as a prerequisite|
||(Optional) Filters for the
||(Optional) The name of the resource data sync||The sync name that you created as a prerequisite|
||(Optional) The result attribute for the
||(Optional) The column fields to write to the output file||“DisplayId”,”SourceAccountId”,”Subject”,”Status”,”ServiceCode”,”CategoryCode”,”SeverityCode”,”TimeCreated”|
||(Required) The S3 bucket where you want to download the output file||The S3 bucket that you created as a prerequisite|
||(Required) The SNS topic ARN to notify when the download is complete||The ARN for the SNS topic that you created as a prerequisite|
||(Optional) The message to send when a document is complete||Leave blank|
||(Optional) The fully qualified column fields to write to the output file||Leave blank|
||(Optional) The multiple result attributes for the
- Choose the IAM service role you created as a prerequisite.
- Choose Register Automation task.
After you successfully register the task, the automation will run, and you will see CSV files getting created in your S3 bucket. In our use case, we set the rate expression as 1 day. However, you can use a lesser frequency such as 1 hour or even 5 minutes to test the functionality.
Create a database in the AWS Glue Data Catalog
Before you can create an AWS Glue crawler, you need to create a database in the Data Catalog, which is a container that holds tables. You use databases to organize your tables into separate categories. In our use case, support cases data resides in an S3 bucket.
- On the AWS Glue console, create a new database.
- For Name, enter a name (for example,
- Add an optional location and description.
- Choose Create database.
For more information about AWS Glue databases, refer to Working with databases on the AWS Glue console.
Create a custom classifier
Crawlers invoke classifiers to infer the schema of your data. We need to create a custom classifier because when we extract the support cases, every column in a potential header parses as a string data type. When creating your classifier, choose Has headings and add the following:
For more information on classifiers, refer to Adding classifiers to a crawler in AWS Glue.
Create an AWS Glue crawler
To create a crawler that reads files stored on Amazon S3, complete the following steps:
- On the AWS Glue console, in the navigation pane, choose Crawlers.
- On the Crawlers page, choose Add crawler.
- For Crawler name, enter support cases extract, then choose Next.
- For the crawler source type, choose Data stores, then choose Next.
Now let’s point the crawler to your data.
- On the Add a data store page, choose the Amazon S3 data store.
- For Crawl data in, choose Specified path in this account.
- For Include path, enter the path where the crawler can find the support cases data, which is
s3://S3_BUCKET_PATH. After you enter the path, the title of this field changes to Include path.
- Choose Next.
The crawler also needs permissions to access the data store and create objects in the Data Catalog.
- To configure these permissions, choose Create an IAM role. The IAM role name starts with
AWSGlueServiceRole-; you enter the last part of the role name (for this post, we enter
- Choose Next.
Crawlers create tables in your Data Catalog. Tables are contained in a database in the Data Catalog.
- Choose Target database and select the database you created.
Now we create a schedule for the crawler.
- For Frequency, choose Daily
- Choose Next.
- Verify the choices you made. If you see any mistakes, you can choose Edit to return to previous pages and make changes.
- After you have reviewed the information, choose Finish to create the crawler.
For more information on creating an AWS Glue crawler, refer to Adding an AWS Glue crawler.
Create views in Athena
After the AWS Glue crawler is configured successfully, we query the data from the database and table created by the crawler and create views in Athena. The data source for the dashboard will be an Athena view of your existing
support_cases database. We create a view in Athena with a group by condition.
Create the view
case_summary_view by modifying the table name
support_cases from the following code and run the query in the Athena query editor:
Visualize AWS support cases in QuickSight
After we create the Athena view, we can create a dashboard in QuickSight. Before connecting QuickSight to Athena, make sure to grant QuickSight access to Athena and the associated S3 buckets in your account. For details, refer to Authorizing connections to Amazon Athena.
- On the QuickSight console, choose Datasets in the navigation pane.
- Choose New dataset.
- Choose Athena as your data source.
- For Data source name¸ enter
- Choose Create data source.
- For Database, choose the
aws_support_casesdatabase, which contains the views you created (refer to the Athena console if you are unsure which ones to select)
- For Tables, select the
case_summary_viewtable that we created as part of the steps in Athena.
- Choose Edit/Preview data.
- Select SPICE to change your query mode.
Now you can create the sheet
aws_support_cases in the analysis.
- Choose Publish & Visualize.
- Select the sheet type that you want (Interactive sheet or Paginated report). For this post, we select Interactive sheet.
- Choose Add.
Refer to Starting an analysis in Amazon QuickSight for more information about creating an analysis.
- In Sheet 1 of the newly created analysis, under Fields list, choose
- For Visual types, choose a clustered bar combo chart.
This type of visual returns the count of records by case category.
- To add more visuals to the workspace, choose Add, then Add visual.
In the second visual, we create a donut chart with the field
case_status to count the number of overall cases.
- Next, we create a word cloud to display how often AWS support cases have been raised by which AWS account.
The word cloud shows the top 100 accounts by default (if you have data for more than one account) and displays the account with the maximum number of entries in a higher font size. If you wanted to show just the top account, you would have to configure a top 1 filter.
- Next, we create a stacked bar combo chart to display cases with service type, using the fields
- Next, we create a table visual to display all case details in table format (select all available fields).
The following screenshot shows a visualization of all fields of support cases in tabular format.
19. Adjust the size and position of the visuals to fit the layout of your analysis.
The following screenshot shows our final dashboard for support cases.
You’ve now set up a fully functional AWS support cases dashboard at an organizational view. You can share the dashboard with your cloud platform and operations teams. For more information, refer to Sharing Amazon QuickSight dashboards.
When you don’t need this dashboard anymore, complete the following steps to delete the AWS resources you created to avoid ongoing charges to your account:
- Delete the Amazon S3 Bucket
- Delete the SNS topic.
- Delete the IAM roles.
- Cancel your QuickSight subscription. You should only delete your QuickSight account if you explicitly set it up to follow this post and are absolutely sure that it’s not being used by any other users.
This post outlined the steps and resources required to construct a customized analytics dashboard in QuickSight, empowering you to attain comprehensive visibility and valuable insights into support cases generated across multiple accounts within your organization. To learn more about how QuickSight can help your business with dashboards, reports, and more, visit Amazon QuickSight.
About the authors
Yash Bindlish is a Enterprise Support Manager at Amazon Web Services. He has more than 17 years of industry experience including roles in cloud architecture, systems engineering, and infrastructure. He works with Global Enterprise customers and help them build, scalable, modern and cost effective solutions on their growth journey with AWS. He loves solving complex problems with his solution-oriented approach.
Shivani Reddy is a Technical Account Manager (TAM) at AWS with over 12 years of IT experience. She has worked in a variety of roles, including application support engineer, Linux systems engineer, and administrator. In her current role, she works with global customers to help them build sustainable software solutions. She loves the customer management aspect of her job and enjoys working with customers to solve problems and find solutions that meet their specific needs.