Deploy a serverless solution that can restore your S3 bucket to a previous state in minutes or hours, depending on scale. This tool can revert thousands of changes in smaller buckets in under 15 minutes, helping you quickly recover from accidental deletions or corruptions.
Overview
This Guidance demonstrates how to recover Amazon S3 datasets at scale by reverting to a specified point-in-time using S3 Versioning. The process begins by deploying an AWS CloudFormation template that creates Athena queries to identify necessary object actions. Lambda functions then orchestrate these queries and create S3 Batch Operations using CSV manifests, efficiently restoring the bucket to its previous state. You can restore millions of objects quickly, with the ability to revert 1 million changes in a 10-billion-object bucket in under an hour, or handle smaller restorations in just 15 minutes.
Benefits
Accelerate data recovery
Minimize operational overhead
Leverage fully managed AWS services like Lambda, Athena, and S3 Batch Operations to handle complex rollback operations without managing infrastructure. The solution automatically identifies necessary actions through Athena queries and orchestrates the restoration process, allowing your team to focus on core business activities.
Scale with confidence
Handle massive data recovery operations efficiently across billions of objects in your S3 buckets. The architecture can detect and revert 1 million changes in a bucket containing 10 billion objects in under an hour, providing enterprise-grade recovery capabilities for your most critical storage environments.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Step 1
Deploy with confidence
Everything you need to launch this Guidance in your account is right here.
Let's make it happen
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.