Cloud Cost Management

How to Set Up AWS CUR 2.0 Data Exports to S3 for Cost Reporting

If you need a reliable way to analyze AWS spending, AWS Data Exports using CUR 2.0 is now the preferred option. It gives you detailed cost and usage data in a format that works well with Amazon Athena, BI dashboards, and internal reporting systems. In this guide, we will walk through the full setup process step by step.

Note: AWS now recommends Data Exports (CUR 2.0) instead of the legacy Cost & Usage Reports (CUR). If you are setting up cost reporting today, CUR 2.0 is the better choice.

Why AWS Data Exports (CUR 2.0) Matters

For teams that need better visibility into cloud spending, access to detailed billing data is essential. AWS Data Exports makes it easier to send structured cost and usage data directly to Amazon S3, where it can be queried, transformed, and used for deeper financial analysis.

Compared with the older legacy CUR setup, CUR 2.0 offers a cleaner experience, better schema flexibility, and native support for modern formats like Parquet. That makes it a stronger fit for analytics workflows and long-term cost optimization.

Prerequisites Before You Begin

Before starting the setup, make sure the following are in place:

You have access to the AWS Billing Console.
You are signed in to either a management account in AWS Organizations or a standalone AWS account.
You have permission to create or update S3 bucket policies.
You have permission to create Data Exports.
The region used in this setup is us-east-1 (N. Virginia).

Step 1: Open AWS Data Exports

Start by signing in to the AWS Console. From there, go to:

Billing and Cost Management → Data Exports

Click Create to begin setting up a new export.

Step 2: Choose the Export Type

Under Export details, select:

✅ Standard data export

This option is designed for exporting CUR 2.0 data so it can be used in Athena and other analytics platforms.

For the export name, you can use:

cost-usage-export

Step 3: Select the Data Table

In the Data table content settings section, choose:

✅ CUR 2.0

This is the modern cost and usage dataset recommended by AWS for current reporting workflows.

Step 4: Configure the Billing View

Under Billing view, use the default primary option unless your organization has a more advanced billing structure.

Type: Primary
View: Primary view

For most accounts, no changes are needed here.

Step 5: Choose Time Granularity

AWS lets you choose between hourly, daily, or monthly data granularity.

Hourly: Best for very detailed cost analysis
Daily: Balanced and recommended for most use cases
Monthly: Suitable for high-level summaries

In most cases, Daily is the best option because it keeps reporting manageable while still providing enough detail for trend analysis.

Step 6: Set the Delivery Format

In the Data export delivery options section, select the following:

Compression Type: Parquet
File Versioning: Overwrite existing data export file

Why Parquet?

It is optimized for Athena queries.
It helps reduce storage costs.
It delivers faster query performance.

Choosing Overwrite existing data export file keeps the export structure cleaner and is usually easier to manage over time.

Step 7: Configure Amazon S3 Storage

Scroll to Data export storage settings and click Configure.

Choose an S3 Bucket

You can either select an existing bucket or create a new one. For example:

cur-bucket

Important: AWS will ask to overwrite your bucket policy. This is necessary so the required AWS services can write export files into the bucket.

The policy update allows access for:

billingreports.amazonaws.com
bcm-data-exports.amazonaws.com

Confirm the checkbox:

✅ I agree to overwrite my S3 bucket policy

Then click Select bucket.

Set the S3 Path Prefix

In the prefix field, enter:

data-exports

Watch out: Do not add a trailing slash and do not use a dot. For example, data-exports/ is invalid.

Step 8: Create the Export

Once everything is configured, click Create.

If the setup is successful, you should see a confirmation message indicating that the export request has been submitted, and the export status should show as Healthy.

What Happens After Setup?

After the export is created, AWS typically completes the first delivery within 24 hours. After that, the data refreshes daily.

Your exported files will appear in a structure similar to this:

s3://cur-bucket/data-exports/

data-exports/
    cost-usage-export/
        year=2026/
            month=02/
                part-0000.parquet

These files are:

Stored in Parquet format
Partitioned by year and month
Ready for Athena-based analysis

Optional: Query the Export in Athena

Once the first export has landed in S3, you can connect it to Athena for querying.

Open Amazon Athena, configure the query result location if needed, and either create a database or use AwsDataCatalog.

A sample query might look like this:

SELECT *
FROM your_export_table
LIMIT 10;

Why Use Data Exports Instead of Legacy CUR?

AWS Data Exports is the direction AWS is pushing users toward, and for good reason. It offers a more modern experience and better compatibility with analytics services.

Feature	Legacy CUR	Data Exports (CUR 2.0)
Status	Legacy / being deprecated	Recommended
Schema control	Limited	Customizable
Athena optimization	Partial	Yes, native Parquet support
QuickSight integration	Manual	Built-in option
User experience	Older interface	Modern interface

Recommended Configuration Summary

Export Type	Standard data export
Data Table	CUR 2.0
Granularity	Daily
Format	Parquet
S3 Bucket	cur-bucket
Prefix	data-exports
Versioning	Overwrite existing

Final Thoughts

Setting up AWS CUR 2.0 through Data Exports is a practical step for any team that wants better visibility into cloud costs. Once the export is configured, you have a consistent stream of billing data landing in S3, ready for Athena queries, dashboards, or internal analysis pipelines.

If you are starting fresh, this is the setup path worth using. It is cleaner, more flexible, and much better aligned with how AWS wants cost reporting handled going forward.

Drupal