What problems is Amazon Simple Storage Service (S3) solving and how is that benefiting you?
We faced quite a few problems in implementing and integrating S3 in our ETL solution. Like one is deleting or moving object is slower in S3 , compare to deleting files in Hadoop or any other storage.
To improve the performance of our spark ETL job, we used some calculation for buffer setting, s3a protocol-based URL, spark to s3 params etc. While Amazon S3 provides robust security features, users are responsible for managing their own access controls, encryption, and other security measures, which can be complex and time-consuming.
About Integration with FiveTran and S3, integration is not so hard , its easy , but some time slowness can impact your ETL pipelines.
I would like to add what I experienced , there are few things to note like : Ensuring that Fivetran has the necessary permissions to access your S3 bucket. Ensure the credentials you provided to Fivetran have the required permissions to read and write objects to the bucket.
Region mismatch: Verify that Fivetran and S3 are located in the same region. If they are not in the same region, data transfer may be slow, or Fivetran may be unable to access the bucket.
Bucket name and path: Check that you use the correct bucket name and path in Fivetran. Ensure that the bucket exists and that the path you are specifying is correct.
Data format: Ensure that the data format Fivetran is using to write to S3 is compatible with the data format you are expecting. If Fivetran is using a different format, you may need to convert it before using it in your downstream processes. Review collected by and hosted on G2.com.