To visualize AWS Cost and Usage Report (CUR) data, deliver an Athena-compatible Parquet report to Amazon S3, query it with Athena, and connect the results to Amazon QuickSight. Athena handles SQL analysis; QuickSight turns the data into interactive charts and dashboards. For a ready-made cost dashboard rather than a custom build, consider AWS Cloud Intelligence Dashboards (CUDOS).
How the Athena-to-dashboard workflow works
Athena queries CUR data in S3 with standard SQL, without requiring you to manage a data warehouse. AWS describes Athena as a serverless query service for analyzing CUR data stored in S3: AWS Data Exports documentation. The usual flow is to deliver the report, expose it through the AWS Glue Data Catalog, query or prepare it in Athena, then use Athena data in QuickSight.
- Deliver the report to S3. Create or select a dedicated bucket and configure a CUR or Data Export with Athena integration. AWS recommends a new bucket and report for this workflow. Choose Parquet output. If you need resource-level analysis, include resource IDs. The Athena integration partitions data by year and month. See AWS’s CUR and Athena setup guide.
- Make the data available to Athena. Deploy AWS’s provided CloudFormation integration, or configure the Glue crawler and catalog manually. Confirm that the CUR table appears in Athena before proceeding. See AWS’s Athena CUR walkthrough.
- Query and shape the data. Start with an aggregation that answers a specific cost question. Use partition filters such as year and month, and select only the columns needed; Parquet and partition pruning can reduce the data scanned.
- Connect QuickSight. Grant QuickSight access to the Athena workgroup and query-results S3 bucket, as well as the bucket containing the CUR files. Create an Athena dataset or upload the CUR manifest, then choose Visualize. AWS outlines the connection in its CUR-to-QuickSight procedure.
- Build visuals around decisions. Use time-series lines for monthly trends, stacked bars to compare service or account mix, and tables when resource-level detail matters. Add filters for dimensions such as account, region, product, tag, or usage type. These are practical chart choices, not a chart set mandated by AWS.
Start with a useful Athena query
A basic service-cost query groups CUR line items by product code and sums unblended cost. The exact table name and available columns depend on your report and catalog setup. Replace your_cur_table with the table Athena shows for your report.
SELECT
line_item_product_code AS service,
SUM(line_item_unblended_cost) AS unblended_cost
FROM your_cur_table
WHERE year = '2026'
GROUP BY line_item_product_code
ORDER BY unblended_cost DESC;The year condition is a partition filter in the documented Athena pattern; adjust the value to the year you want to inspect. For a monthly trend, include the month partition in the result and grouping. Verify the report’s partition column types and names in Athena, since schemas can vary with configuration.
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For repeatable dashboards, create Athena views for recurring analyses such as costs by account, region, usage type, resource ID, or data transfer. A view can also encode a shared business definition—for example, which line items the organization counts as data-transfer cost—so dashboard authors use the same logic. AWS’s examples cover CUR dimensions and reusable views in its CUR visualization guide.
Choose the right level of detail
- Service totals: Group by product code to identify which AWS services contribute most to spend.
- Account and region: Group or filter by linked account and region to compare organizational areas or deployment locations.
- Usage type: Use usage-type breakdowns to investigate what kind of consumption drives a service’s charge.
- Resource-level analysis: Include resource IDs in the report when configuring it if you need to trace eligible line items to individual resources. Greater detail can make datasets and dashboards more complex.
- Shared-cost definitions: Use a view to consistently classify or expose shared costs. AWS’s cost-management guidance notes that visibility into shared costs can help organizations identify their drivers and optimization opportunities: AWS cost dataset guidance.
When to use Athena, QuickSight, or CUDOS
| Approach | Best fit | Trade-off |
|---|---|---|
| Athena queries and views | Ad-hoc investigation, custom SQL, and detailed analysis of CUR fields | You define and maintain queries and any shared business logic. |
| QuickSight connected to Athena | Interactive dashboards and visual exploration by business users | You must configure access to Athena and the relevant S3 locations, then build and maintain the presentation. |
| AWS Cloud Intelligence Dashboards/CUDOS | A prebuilt AWS cost-analytics experience when designing dashboards from scratch is unnecessary | It reduces dashboard design work but uses a predefined model that may not match every organization’s definitions or reporting needs. |
Athena is the analysis layer, while QuickSight is the visualization layer; they are complementary rather than competing choices. CUDOS is the alternative to evaluate when a prebuilt dashboard experience is more useful than full control over dashboard design. See AWS Cloud Intelligence Dashboards.
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CUR’s Athena integration uses Parquet, a columnar format. Filtering partitions and selecting only necessary columns limits the data scanned. An AWS blog published in 2019 estimated that Parquet and column-based compression could reduce per-query costs by 30% to 90% in its context; that range is not a guaranteed saving for every report or workload. See the 2019 AWS walkthrough.
QuickSight needs explicit permission for both the Athena workgroup/query-results location and the S3 bucket holding CUR data. If the dataset cannot load, check these grants and confirm that the CUR table is visible and queryable in Athena before troubleshooting the visual itself. AWS’s connection instructions describe the required access path.
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What to verify before relying on a dashboard
- Confirm the report’s delivery, partitions, and catalog table are current enough for the decision you are making.
- Check the query’s aggregation and filters against the intended cost definition; unblended cost is one available measure, not automatically the right business measure for every organization.
- Validate that users who need the dashboard can access its dataset and that its S3 and Athena permissions are correctly configured.
- Check AWS’s current regional availability, feature documentation, and pricing for the services and configuration in your account. The available documentation does not establish a universal end-to-end price or guaranteed dashboard refresh latency.
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