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amazon-s3-find-and-forget

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Amazon S3 Find and Forget is a solution to handle data erasure requests from data lakes stored on Amazon S3, for example, pursuant to the European General Data Protection Regulation (GDPR)

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<h1 align="center">
    Amazon S3 Find and Forget
    <br>
    <img src="https://img.shields.io/github/v/release/awslabs/amazon-s3-find-and-forget?include_prereleases"> 
    <img src="https://github.com/awslabs/amazon-s3-find-and-forget/workflows/Unit%20Tests/badge.svg"> 
    <img src="https://codecov.io/gh/awslabs/amazon-s3-find-and-forget/branch/master/graph/badge.svg">
</h1>

> Warning: Consult the
> [Production Readiness guidelines](docs/PRODUCTION_READINESS_GUIDELINES.md)
> prior to using the solution with production data

Amazon S3 Find and Forget is a solution to the need to selectively erase records
from data lakes stored on Amazon Simple Storage Service (Amazon S3). This
solution can assist data lake operators to handle data erasure requests, for
example, pursuant to the European General Data Protection Regulation (GDPR).

The solution can be used with Parquet and JSON format data stored in Amazon S3
buckets. Your data lake is connected to the solution via AWS Glue tables and by
specifying which columns in the tables need to be used to identify the data to
be erased.

Once configured, you can queue record identifiers that you want the
corresponding data erased for. You can then run a deletion job to remove the
data corresponding to the records specified from the objects in the data lake. A
report log is provided of all the S3 objects modified.

## Installation

The solution is available as an AWS CloudFormation template and should take
about 20 to 40 minutes to deploy. See the
[deployment guide](docs/USER_GUIDE.md#deploying-the-solution) for one-click
deployment instructions, and the [cost overview guide](docs/COST_OVERVIEW.md) to
learn about costs.

## Usage

The solution provides a web user interface, and a REST API to allow you to
integrate it in your own applications.

See the [user guide](docs/USER_GUIDE.md) to learn how to use the solution and
the [API specification](docs/api/README.md) to integrate the solution with your
own applications.

## Architecture

The goal of the solution is to provide a secure, reliable, performant and cost
effective tool for finding and removing individual records within objects stored
in S3 buckets. In order to achieve this goal the solution has adopted the
following design principles:

1. **Secure by design:**
   - Every component is implemented with least privilege access
   - Encryption is performed at all layers at rest and in transit
   - Authentication is provided out of the box
   - Expiration of logs is configurable
   - Record identifiers (known as **Match IDs**) are automatically obfuscated or
     irreversibly deleted as soon as possible when persisting state
2. **Built to scale:** The system is designed and tested to work with
   petabyte-scale Data Lakes containing thousands of partitions and hundreds of
   thousands of objects
3. **Cost optimised:**
   - **Perform work in batches:** Since the time complexity of removing a single
     vs multiple records in a single object is practically equal and it is
     common for data owners to have the requirement of removing data within a
     given _timeframe_, the solution is designed to allow the solution operator
     to "queue" multiple matches to be removed in a single job.
   - **Fail fast:** A deletion job takes place in two distinct phases: Find and
     Forget. The Find phase queries the objects in your S3 data lakes to find
     any objects which contain records where a specified column contains at
     least one of the Match IDs in the deletion queue. If any queries fail, the
     job will abandon as soon as possible and the Forget phase will not take
     place. The Forget Phase takes the list of objects returned from the Find
     phase, and deletes only the relevant rows in those objects.
   - **Optimised for Parquet:** The split phase approach optimises scanning for
     columnar dense formats such as Parquet. The Find phase only retrieves and
     processes the data for relevant columns when determining which S3 objects
     need to be processed in the Forget phase. This approach can have
     significant cost savings when operating on large data lakes with sparse
     matches.
   - **Serverless:** Where possible, the solution only uses Serverless
     components to avoid costs for idle resources. All the components for Web
     UI, API and Deletion Jobs are Serverless (for more information consult the
     [Cost Overview guide](docs/COST_OVERVIEW.md)).
4. **Robust monitoring and logging:** When performing deletion jobs, information
   is provided in real-time to provide visibility. After the job completes,
   detailed reports are available documenting all the actions performed to
   individual S3 Objects, and detailed error traces in case of failures to
   facilitate troubleshooting processes and identify remediation actions. For
   more information consult the
   [Troubleshooting guide](docs/TROUBLESHOOTING.md).

### High-level overview diagram

![Architecture Diagram](https://raw.githubusercontent.com/awslabs/amazon-s3-find-and-forget/master/docs/images/architecture.png)

See the [Architecture guide](docs/ARCHITECTURE.md) to learn more about the
architecture.

## Documentation

- [User Guide](docs/USER_GUIDE.md)
- [Deployment](docs/USER_GUIDE.md#deploying-the-solution)
- [Architecture](docs/ARCHITECTURE.md)
- [Troubleshooting](docs/TROUBLESHOOTING.md)
- [Monitoring the Solution](docs/MONITORING.md)
- [Security](docs/SECURITY.md)
- [Cost Overview](docs/COST_OVERVIEW.md)
- [Limits](docs/LIMITS.md)
- [API Specification](docs/api/README.md)
- [Local Development](docs/LOCAL_DEVELOPMENT.md)
- [Production Readiness guidelines](docs/PRODUCTION_READINESS_GUIDELINES.md)
- [Change Log](CHANGELOG.md)
- [Upgrade Guide](docs/UPGRADE_GUIDE.md)

## Contributing

Contributions are more than welcome. Please read the
[code of conduct](CODE_OF_CONDUCT.md) and the
[contributing guidelines](CONTRIBUTING.md).

## License Summary

This project is licensed under the Apache-2.0 License.

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