neo4j-s3-integration

Loading SDTM data into a Neo4j graph database is automated by ingesting validated domains as labeled nodes and establishing cross-domain relationships using the neo4j-driver. This enables graph-based analytics for regulatory-ready outputs.

Uploading SDTM regulatory artifacts to AWS S3 is supported through boto3 integration. The process stores outputs, metadata, and reports using a consistent key structure to ensure persistent cloud storage.

Python packages required for SDTM ETL to Neo4j and S3 are neo4j-driver and boto3. You must also configure environment variables for Neo4j credentials and AWS access keys to perform deterministic loading and uploads.

Modeling SDTM domains in a graph database for Phase 7 submissions involves creating labeled nodes like SDTM_DM and SDTM_AE. The process establishes essential cross-domain relationships to support coordinated data warehouse loading.

This SDTM data warehouse loading approach works for ongoing data persistence by coordinating graph updates and S3 cloud storage. It covers regulatory submissions across Phase 7 and maintains outputs with a consistent key structure.

Limitations of using Neo4j and S3 for SDTM ETL include the requirement for explicit environment configuration for both platforms. Users must manage Python-based tooling and ensure deterministic loading across cross-domain relationships.