Common Issues and How to Fix Them
1. S3 Bucket Not Found or Doesn’t Appear
Problem:
When entering the bucket name or browsing, your S3 bucket does not show up or results in a "Bucket not found" error.
Solution:
Confirm that the bucket exists in the AWS region your connection is configured for.
Ensure the IAM role or credentials used in the connection have
s3:ListAllMyBucketspermission.Double-check for typos in the bucket name.
Tip: Use the autocomplete dropdown to verify available buckets.
2. Access Denied or Permission Error
Problem:
You're seeing an “Access Denied” error when trying to access or write to a file in the specified S3 path.
Solution:
Confirm the IAM user or role has the necessary permissions (
s3:GetObject,s3:PutObject,s3:ListBucket) for the specific bucket and path.Check for any S3 bucket policies or encryption settings that may restrict access.
Avoid using paths that are restricted by organizational guardrails or SCPs.
Tip: Run a test using AWS CLI with the same credentials to confirm access outside of Nexadata.
3. Invalid S3 Path or File Not Found
Problem:
When reading from S3, the dataset fails with a message like “File not found” or “Invalid path”.
Solution:
Ensure the full file path (including file name and extension) is correct.
If the file was recently moved or renamed, update the path accordingly.
Verify case sensitivity—S3 object keys are case-sensitive.
Tip: Use the AWS S3 console to confirm the exact object key.
4. Output Not Written to S3
Problem:
Your pipeline runs but the output file does not appear in the S3 bucket.
Solution:
Confirm you selected Dynamic or Static output and that the S3 path is valid.
For Static outputs, check if the file is being overwritten and not showing a timestamp.
Verify the IAM user/role has
PutObjectpermission.
Tip: Use Dynamic output for troubleshooting—it guarantees a unique file and avoids silent overwrites.
5. Unexpected File Format or Corrupted Output
Problem:
The output file is generated, but doesn’t open correctly in tools like Excel or your BI platform.
Solution:
Ensure the delimiter selected (Comma, Tab, etc.) matches the structure of your data.
Confirm the output format (e.g.,
.csv,.json,.parquet) aligns with the file extension in the S3 path.Avoid including spaces or special characters in file names or paths unless required.
Tip: Download the file manually and open in a text editor to validate the content.
6. Dataset Preview Fails
Problem:
When creating a dataset from S3, the preview or schema loading step fails.
Solution:
Check that the selected file is not empty and is in a readable format.
Validate the structure and delimiter of the file.
Re-upload a clean version of the file if needed.
Tip: Start with a small sample file to verify parsing works correctly.
Best Practices
Use consistent naming for buckets and paths to avoid confusion across teams.
Use Dynamic Outputs when you need to retain historical results or logs.
Set folder structures like
project-name/YYYY/MM/DD/for better file organization.Limit access scope in IAM roles to only the required buckets and paths for improved security.
Test with small files first to confirm pipeline behavior before scaling up.
Use meaningful file names with timestamps or identifiers to avoid collisions.
When to Contact Support
If you've tried the steps above and are still encountering issues:
Collect any error messages shown in the UI.
Take note of the S3 Bucket and Path used.
Record the approximate time of failure.
Identify whether the error occurred during dataset creation or output writing.
Then reach out to Nexadata Support with this information for faster resolution. Our team has deep experience troubleshooting S3 integrations and can help resolve permission, path, or formatting issues efficiently.