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RevenueBase delivers data to a managed S3 bucket and grants your AWS account read access. Data is available in CSV, JSON, and Parquet formats — you get all three with every delivery. Each monthly delivery lands in a new date-stamped folder; previous deliveries are never overwritten.

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Setup your AWS S3 integration

1

Locate Integrations

On the RevenueBase web application, select Integrations from the side bar.
2

Fill in your AWS Account ID

Setting up your AWS S3 integration in RevenueBase.
Under Step 1 — Your AWS details, submit your AWS Account ID.
You can find your AWS Account ID in the top-right dropdown of the AWS Console, or by running aws sts get-caller-identity in the AWS CLI.
3

Wait for bucket configuration

Once your AWS Account ID has been submitted, please allow up to 24 hours as we prepare to provision your S3 buckets. Once provisioned, you will receive an email including your bucket ARN, region, access details, and a step-by-step setup guide.
Setting up your AWS S3 integration in RevenueBase.
4

Verify access

Once your S3 bucket is provisioned, test your connection using the AWS CLI:
aws s3 ls s3://<your-bucket-alias>/<delivery_date>/  \
  --request-payer requester  \
  --region <target-bucket-region>
You can also access via ARN:
aws s3 ls s3://arn:aws:s3:<region>:<account-id>:accesspoint/<access-point-name>/<delivery_date>/  \
  --request-payer requester  \
  --region <target-bucket-region>
Use the AWS CLI — not the AWS Console web UI — to access RevenueBase buckets. The Console does not support --request-payer and will return access errors.

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Bucket structure

Your data lives in date-stamped folders within the S3 bucket. The path structure is: 
<delivery_date>/<dataset_name>/<file_format>/
Path segmentDescription
delivery_dateDate of delivery in YYYYMMDD format (e.g., 20260201)
dataset_nameThe dataset included in your subscription (e.g., per, org, or a pre-joined dataset)
file_formatOne of json, csv, or parquet
For more on Parquet, see the official Parquet documentation.
Data is split across multiple files within each delivery folder. This is by design — it enables parallel processing and significantly improves import performance for large datasets.

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Next Steps