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@mycaule Could you tell me a bit more about the use case that you are trying to solve? why do you require dynamic data masking? In athena we can leverage lakeformation tagging. This means that data masking is not strictly necessary, because some columns can be simply excluded from user access when using the right lakeformatio tag. dbt-athena users can override column tags as part of model configs, doing so, for example, some sensitive columns containing PII, can be simply tagged properly, and IAM principals without PII tag access won't simply see the columns that are tagged as PII. |
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Another option for masking is to use S3 object lambda (https://aws.amazon.com/blogs/storage/automatically-modify-data-you-are-querying-with-amazon-athena-using-amazon-s3-object-lambda/). With the downside that the masking is not managed from dbt though. |
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Do you have ideas on how to implement dynamic data masking with Athena, macros or Trino functions?
Some users started from how Snowflake implemented it and tried to apply the principles in Redshift.
https://discourse.getdbt.com/t/how-to-implement-dynamic-data-masking-on-redshift/2043
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