The following diagram illustrates this use case. To address the challenge of implementing a data purge framework, we reduced the problem to the straightforward use case of deleting a user’s data from a platform that uses AWS for its data pipeline.
#Docker local dynamodb kinesis adapter code
This post walks you through a framework that helps you purge individual user data within your organization’s AWS hosted data lake, and an analytics solution that uses different AWS storage layers, along with sample code targeting Amazon S3. Despite its versatility and feature completeness, Amazon S3 doesn’t come with an out-of-the-box way to map a user identifier to S3 keys of objects that contain user’s data. In the context of the AWS big data and analytics ecosystem, every architecture, regardless of the problem it targets, uses Amazon Simple Storage Service (Amazon S3) as the core storage service. One article of GDPR is the “right to erasure” or “right to be forgotten” which may require you to implement a solution to delete specific users’ personal data. General Data Protection Regulation (GDPR) is an important aspect of today’s technology world, and processing data in compliance with GDPR is a necessity for those who implement solutions within the AWS public cloud.
Post Syndicated from George Komninos original
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