Connect AWS CloudWatch Metrics to Looker Studio: Dashboards in Minutes

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AWS CloudWatch Metrics
Looker Studio

Visualize AWS CloudWatch Metrics in Looker Studio

Kondado enables business teams to visualize AWS CloudWatch Metrics directly in Looker Studio without technical setup or intermediate storage systems. Simply connect your AWS account as a data source, select the specific metrics pipelines you need for your operational analysis, and start building intuitive dashboards that track application performance, infrastructure health, and resource utilization across your entire cloud environment. Kondado replicates your selected metrics automatically on your chosen schedule, keeping your reports current without manual exports, CSV downloads, or spreadsheet maintenance, so you can focus on making data-driven decisions rather than managing data flows.

Kondado connects AWS CloudWatch Metrics to Looker Studio via a direct integration that updates on a configurable schedule ranging from every 5 minutes to daily, eliminating the need for complex manual processes, intermediate databases, or technical expertise while delivering automated monitoring insights to business users.

Our prices start from $19 USD/month, and you can try Kondado for free for 14 days with no credit card required

With Kondado, you can replicate both incremental metrics and moving window metrics directly into Looker Studio for comprehensive monitoring analysis and operational visibility. The Metrics (Incremental) pipeline captures new data points since your last update, perfect for tracking ongoing application performance trends and identifying gradual infrastructure changes over time without reprocessing historical information. Meanwhile, the Metrics (Moving Window) pipeline allows you to define specific time ranges for historical analysis, enabling you to compare current system health against previous periods and spot seasonal patterns in your AWS resource utilization.

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Dynamic data

Kondado automatically reads the schema of your AWS CloudWatch Metrics. All tables, views, and fields available in your account are extracted without manual configuration.

2
available pipelines

What Kondado extracts

Métricas (Incremental)
Metrics collected incrementally from the last execution
Métricas (Janela Móvel)
Metrics with a configurable moving time window
Integration Description
Métricas (Incremental) Metrics collected incrementally from the last execution
Métricas (Janela Móvel) Metrics with a configurable moving time window

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How to create AWS CloudWatch Metrics dashboards in Looker Studio

Visualize your data automatically — no spreadsheet exports or custom scripts.

1
Connect AWS CloudWatch Metrics

Log into Kondado and add AWS CloudWatch Metrics as a data source, then authenticate with your AWS account credentials to establish the connection. Select the specific metrics pipelines you want to replicate, choosing between incremental updates or moving window timeframes based on your analysis needs.

2
Choose Looker Studio Destination

Select Looker Studio as your destination to send the replicated metrics data directly into Google's visualization platform. Configure your update schedule to refresh data every 5 minutes, hourly, or daily depending on how frequently you need to monitor your AWS infrastructure.

3
Create Dashboards and Schedule

Build custom dashboards in Looker Studio using your CloudWatch metrics to visualize CPU utilization, application latency, or error rates across your AWS environment. Set up automated refresh schedules so your reports stay current without manual updates, allowing your team to make decisions based on the latest monitoring data.

Access AWS CloudWatch Metrics data in Looker Studio and combine it with dozens of other data sources

If the source you want is not listed, drop us a chat message. We love to add new sources!

Visualize AWS CloudWatch Metrics data in other BI tools

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Frequently Asked Questions (FAQ)

Answers about visualizing AWS CloudWatch Metrics data in Looker Studio automatically

How do I connect AWS CloudWatch Metrics to Looker Studio using Kondado?
Start by adding AWS CloudWatch Metrics as a new data source in your Kondado account, then authenticate with your AWS credentials to establish the connection. Select the specific metrics pipelines you want to visualize and choose Looker Studio as your destination to begin replicating data. The entire setup requires no coding or technical configuration, allowing business users to complete the connection in minutes.
What types of dashboards can I create with AWS CloudWatch Metrics data?
You can build custom dashboards that monitor EC2 instance performance, track Lambda function execution times, visualize RDS database health, and analyze CloudFront distribution metrics. Business teams often create operational reports showing CPU utilization trends, memory consumption patterns, and error rate tracking across multiple AWS services. These visualizations help operations teams identify bottlenecks and optimize resource allocation without accessing the AWS console directly.
How frequently can I schedule updates for AWS CloudWatch Metrics in Looker Studio?
Kondado offers flexible scheduling options ranging from every 5 minutes for near-real-time monitoring to hourly or daily updates for trend analysis. You can configure different refresh frequencies for different metrics pipelines based on your specific business needs and reporting requirements. This ensures your Looker Studio dashboards always display current data without manual intervention or CSV imports.
Which performance KPIs are best to track from AWS CloudWatch Metrics?
Focus on metrics that directly impact business operations, such as application response times, API gateway latency, and infrastructure availability percentages. Operations teams should monitor CPU utilization, memory usage, and disk I/O metrics to prevent performance degradation before it affects customers. Custom business metrics pushed to CloudWatch can also be visualized alongside infrastructure data for comprehensive operational intelligence.
Does Kondado offer pre-built templates for AWS CloudWatch Metrics dashboards?
Kondado does not provide pre-built report templates for this data source, allowing you to create fully customized dashboards tailored to your specific monitoring needs. You can design unique visualizations that focus on the exact AWS services and metrics relevant to your business operations and stakeholder requirements. This flexibility ensures your reports highlight the KPIs that matter most to your team without unnecessary clutter.
How do I share AWS CloudWatch Metrics reports with stakeholders in Looker Studio?
Once your dashboard is created in Looker Studio, use the platform's built-in sharing features to distribute reports via email links or embed them in internal portals. You can set viewing permissions for specific team members or make reports accessible to entire departments, ensuring everyone accesses the same current data. Automated refresh schedules keep shared reports updated without requiring manual distribution of new files.
Can non-technical users set up AWS CloudWatch Metrics visualization without coding?
Yes, Kondado's no-code interface allows business users, marketing teams, and operations managers to connect AWS CloudWatch Metrics without writing scripts or understanding API documentation. The visual setup process guides you through authentication, pipeline selection, and destination configuration using simple point-and-click actions. This empowers domain experts to create monitoring dashboards independently without relying on IT teams or developers.
What is the difference between incremental and moving window metrics pipelines?
The Metrics (Incremental) pipeline replicates only new data points collected since the last scheduled update, making it efficient for tracking ongoing performance trends and current system health. In contrast, the Metrics (Moving Window) pipeline retrieves data from a configurable time period you specify, enabling historical comparisons and seasonal analysis across specific date ranges. Choose incremental for operational monitoring and moving window for deep-dive analysis and trend benchmarking.

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