How do I resolve issues when I'm importing data into Amazon Personalize?

Short description

When you're importing your data into Amazon Personalize, the following issues can occur:

• Your schema and dataset don't match.
• Your AWS Identity and Access Management (IAM) or Amazon Simple Storage Service (Amazon S3) bucket policy doesn't have the required permissions.

Resolution

Your schema and dataset don't match

Note: If you receive errors when you run AWS Command Line Interface (AWS CLI) commands, then see Troubleshooting errors for the AWS CLI. Also, make sure that you're using the most recent AWS CLI version.

When you import your dataset, Amazon Personalize uses your schema to verify that the data that you import matches the schema. If the records in your dataset don't match the dataset schema, then your dataset import job fails.

Your dataset type doesn't match the schema or a column is missing

You get one of the following error messages:

• "Failed to create a data import job for item interactions dataset.
  Input csv has rows that do not conform to the dataset schema. Please ensure all required data fields are present and that they are of the type specified in the schema. Failed validation fields: TIMESTAMP : TYPE_MISMATCH"
• "Failed to create a data import job for item interactions dataset.
  Input csv is missing the following columns: [MISSING_COLUMN]"

To resolve these issues, complete the following steps:

1. Run the describe-schema AWS CLI command to review your dataset schema:

   aws personalize describe-schema --schema-arn your_schema_ARN

   Note: Replace your_schema_ARN with the Amazon Resource Name (ARN) of your schema.
2. Compare the data types and columns for each field in the schema to the ones in your dataset.
3. Update the mismatched data type to the correct value or add the missing column.
4. Recreate the dataset import job.

Note: Make sure that the headers of your dataset columns and key-value pairs of each field match what you specify in the schema. Also, Format the schema fields to match the order of the corresponding headers in your CSV file. For more information, see Schema formatting requirements.

Your dataset doesn't meet the training requirements

If the dataset doesn't meet the minimum training requirements for item interactions, then you get an error message that's similar to the following one:

"Failed to create a data import job for item interactions dataset.
Failure reason in User Class: com.amazonaws.concierge.exceptions.InvalidFormatException"

To resolve this issue, update the dataset based on the issue that's stated in the error message.

Your IAM role or S3 bucket policy doesn't have the required permissions

You get the following error message:

"Unable to Import Data into Personalize from S3Reason: Insufficient privileges for accessing data in S3."

This issue occurs because the IAM role that you use for Amazon Personalize doesn't have permissions to perform actions on the S3 bucket. Or, the S3 bucket policy doesn't allow the IAM service role to perform Amazon S3 actions.

To resolve this issue, attach a policy to your IAM role that has the required permissions. If you use batch workflows, then see Service role policy for batch workflows. If you encrypted your S3 bucket with an AWS Key Management Service (AWS KMS) key, then see Giving Amazon Personalize permission to use your AWS KMS key.

To add the required permissions to your S3 bucket, see Attaching an Amazon Personalize access policy to your S3 bucket. If you use batch workflows, then see Amazon S3 bucket policy for batch workflows.

If you created the dataset in a different account from the account where the S3 bucket is located, then you must grant cross-account access. Edit the S3 bucket policy to include the ARN of the IAM service role in the Principal section. An example IAM role ARN is arn:aws:iam::111122223333:role/Amazon Personalize-user-name.

For more information about how to configure permissions for Amazon Personalize, see Setting up permissions.