This feature would enable the application to pull, process, and store data directly from cloud-based services, such as AWS, Google Cloud, and Azure. By integrating cloud data support, we aim to provide users with more flexibility and scalability in managing their data, especially when dealing with large datasets or distributed systems.
Implementation Considerations:
API Integrations: We would need to support APIs for major cloud providers (e.g., S3 for AWS, Blob Storage for Azure, and Google Cloud Storage).
Authentication and Security: Secure access management is critical, so we may need integrate with cloud authentication protocols like IAM.
Data Formats and Compatibility: Support for multiple data formats (e.g., CSV, JSON, DICOM) to ensure compatibility with various data types stored on the cloud.
This feature would enable the application to pull, process, and store data directly from cloud-based services, such as AWS, Google Cloud, and Azure. By integrating cloud data support, we aim to provide users with more flexibility and scalability in managing their data, especially when dealing with large datasets or distributed systems.
Implementation Considerations: API Integrations: We would need to support APIs for major cloud providers (e.g., S3 for AWS, Blob Storage for Azure, and Google Cloud Storage). Authentication and Security: Secure access management is critical, so we may need integrate with cloud authentication protocols like IAM. Data Formats and Compatibility: Support for multiple data formats (e.g., CSV, JSON, DICOM) to ensure compatibility with various data types stored on the cloud.
ref: https://github.com/webdataset/webdataset https://github.com/mosaicml/streaming https://aws.amazon.com/healthimaging/