QuickSight doesn't support exporting data from more than a single visualization at a time. To export data from additional visuals in the same analysis or dashboard, repeat this process for each visual. To export all the data from a dashboard or analysis, you need to connect to the original data source using valid credentials and a tool that you can use to extract data.
Visual Dashboard for AWS to See your AWS Architecture, Services
When creating a job in AWS Glue Studio, you can choose from a variety of data sources that are stored in AWS services. You can quickly prepare that data for analysis in data warehouses and data lakes. AWS Glue Studio also offers tools to monitor ETL workflows and validate that they are operating as intended. You can preview the dataset for each node. This helps you to debug your ETL jobs by displaying a sample of the data at each step of the job.
AWS Glue Studio also has a script editor for writing or customizing the extract-transform-and-load (ETL) code for your jobs. You can use the visual editor in AWS Glue Studio to quickly design your ETL job and then edit the generated script to write code for the unique components of your job.
AWS Glue Studio provides a comprehensive run dashboard for your ETL jobs. The dashboard displays information about job runs from a specific time frame. The information displayed on the dashboard includes:
Use AWS Glue Studio for easier job management. AWS Glue Studio provides you with job and job run management interfaces that make it clear how jobs relate to each other, and give an overall picture of your job runs. The job management page makes it easy to do bulk operations on jobs (previously difficult to do in the AWS Glue console). All job runs are available in a single interface where you can search and filter. This gives you a constantly updated view of your ETL operations and the resources you use. You can use the real-time dashboard in AWS Glue Studio to monitor your job runs and validate that they are operating as intended.
When using AWS Glue Studio, you are charged for data previews. After you specify an IAM role for the job, the visual editor starts an Apache Spark session for sampling your source data and executing transformations. This session runs for 30 minutes and then turns off automatically. AWS charges you for 2 DPUs at the development endpoint rate (DEVED-DPU-Hour), typically resulting in a charge of $0.44 for each 30 minute session. The rate might vary for each region. At the end of the 30 minute session, you can choose Retry on the Data preview tab for any node or reload the visual editor page to start a new 30 minute session at the same rates.
If you use AWS Organizations, you can view AWS Health events centrally across your organization. Organizational view allows you to consolidate those events and consistently retrieve them across hundreds of AWS accounts in an organization. For example, it helps you to programmatically receive information about AWS service degradations, resource maintenance events, AWS accounts and resources impacted by service events, and so on. With this feature, you can identify potential impacts from AWS Health events and quickly remediate if a global or account-specific event affects your resources. If you prefer to use an out-of-box solution to visualize organizational health data, consider AWS Personal Health Dashboard Organizational View.
In this post, I will show you how to deploy a customizable dashboard for your AWS Health events across your organization using the AWS Health API, AWS Lambda, and Amazon QuickSight, so you can visualize the AWS event data in a way that fits your operational needs.
The AWS Health organizational view dashboard provides a customizable solution for your cloud operation team to collect and visualize the status of service health events for their AWS organization. It calls the AWS Health API to consolidate events that are visible to all AWS accounts (such as public events posted to the Service Health Dashboard) or specific accounts in your AWS organization using the impacted services (account-specific events). The well-formatted AWS health organizational event data can be stored in an Amazon Simple Storage Service (Amazon S3) bucket, where you can use QuickSight to visualize the data and create reports based on your business requirements.
The dashboard user signs in to the AWS master account in your AWS organization as an admin user, and then uses AWS CloudFormation to launch the stack. The QuickSight dashboard must be created manually. After the stack has been deployed, the Lambda function regularly queries the AWS Health API endpoint and retrieves health service status for your AWS organization, including service event information and, for account-specific events, impacted accounts and entities. Service health status data is consolidated into CSV format and stored in a user-specified S3 bucket. You can access QuickSight to create a dashboard to visualize the service health status data and create reports (for example, for event region distribution).
This documentation helps you understand how to apply the shared responsibility model whenusing Amazon QuickSight. The following topics show you how to configure Amazon QuickSight to meet your security andcompliance objectives. You also learn how to use other AWS services that can help you tomonitor and secure your Amazon QuickSight resources.
Before you can create visuals and dashboards that convey useful information, you need to transform and prepare the underlying data. The range and complexity of data transformation steps required depends on the visuals you would like in your dashboard. Often, the data transformation process is time-consuming and highly iterative, especially when you are working with large datasets.
Deliver insights to all your users when, where, and how they need them. BI users can explore modern, interactive dashboards; get insights within their applications; obtain scheduled, formatted reports with Paginated Reports; and make forward-looking decisions with machine learning (ML) insights. To further simplify data exploration, QuickSight allows users to ask questions about the data in natural language.
Embed interactive visualizations and dashboards, sophisticated dashboard authoring, or natural language query capabilities in your applications to differentiate user experiences and unlock new monetization opportunities.
The AWS Health Dashboard is the single place to learn about the availability and operations of AWS services. You can view the overall status of AWS services, and you can sign in to view personalized communications about your particular AWS account or organization. Your account view provides deeper visibility into resource issues, upcoming changes, and important notifications.
The dashboard provides multiple graphs for you to reference, filter, and adjust that are available out-of-the-box. The example in Figure 1 shows data from a sample web page, and the visualizations include:
The dashboard is created by using OpenSearch Dashboards, which gives you the flexibility to add new diagrams and visualizations. To get ideas for new visualizations, in addition to the ones shown here, see these AWS WAF logging examples.
For your testing, you can use any application that leverages AWS WAF. Your AWS WAF logs will be sent from AWS WAF through Kinesis Data Firehose directly to an Amazon OpenSearch Service cluster. The AWS WAF logs will then be visualized in OpenSearch Dashboards. After a couple of minutes, you should start seeing data on your dashboard, similar to the screenshot in Figure 1.
As shown in the preceding screenshots, some of the requests were blocked in accordance with the AWS WAF rules that I configured. The AWS WAF dashboard solution will display blocked requests, as well as normal traffic flowing through the OWASP website. The visualization results are shown in Figure 15.
This Well Architected lab will walk you through implementing a series of dashboards for all of your AWS accounts that will help you drive financial accountability, optimize cost, track usage goals, implement best-practices for governance, and achieve operational excellence.
Amazon Trusted Advisor helps you optimize your AWS infrastructure, improve security and performance, reduce overall costs, and monitors service limits. Organizational view lets you view Trusted Advisor checks for all accounts in your AWS Organizations. The only way to visualize the organizational view is to use the TAO dashboard. The TAO dashboard is a set of visualizations that provide comprehensive details and trends across your entire AWS Organization. Out-of-the-box benefits of the TAO dashboard include (but are not limited to):
The KPI and Modernization Dashboard helps your organization combine DevOps and IT infrastructure with Finance and the C-Suite to grow more efficiently and effectively on AWS. This dashboard lets you set and track modernization and optimization goals such as percent OnDemand, Spot adoption, and Graviton usage. By enabling every line of business to create and track usage goals, and your cloud center of excellence to make recommendations organization-wide, you can grow more efficiently and innovate more quickly on AWS. Out-of-the-box benefits of the KPI dashboard include (but are not limited to):
This dashboard helps your organization to visualize and trace right sizing recommendations from AWS Compute Optimizer. These recommendations will help you indentify Cost savings opportunities for over provisioned resources and also see the Operational risk from under provisioned ones.
One way to help offset the cost of CloudWatch features and services is to restrict the use of the dashboard and enforce a log retention policy for AWS WAF that makes it cost effective. If you use the queries and monitoring only as-needed, this can also help reduce costs. By limiting the running of queries and the matched log events for the Contributor Insights rules, you can enable the rules only when you need them. AWS WAF also provides the option to filter the logs that are sent when logging is enabled. For more information, see AWS WAF log filtering. 2ff7e9595c
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