About This Architecture

Digital engineering platform data architecture on AWS integrates multi-source engineering data—from requirement docs, 3D mockups, simulations, and field tests—through a standardized governance layer into a unified data storage tier. Data flows from collection (requirement files, simulation outputs, test data) through governance (cleansing, deduplication, metadata standardization, quality checks) into a VPC-isolated storage layer spanning two availability zones with RDS PostgreSQL, Amazon Timestream for time-series, and S3 for models and datasets. A services layer exposes this data via API Gateway, component invocation, search, and real-time metric calculation to five downstream business applications including digital design, mockup testing, and multi-discipline simulation tools. This architecture demonstrates how to govern heterogeneous engineering data at scale while maintaining security, availability, and cross-platform accessibility for product development teams. Fork and customize this diagram on Diagrams.so to adapt the governance rules, add additional data sources, or integrate your own simulation and CAD platforms.

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How do you design a scalable AWS data architecture for engineering platforms that integrates simulations, CAD models, and test data with governance?

This diagram shows a three-tier AWS architecture: a Data Collection Layer ingests requirement docs, 3D mockups, simulation outputs, and field tests; a Data Governance Layer standardizes, cleanses, and validates data; and a Data Storage Layer in a VPC uses RDS PostgreSQL, Amazon Timestream, and S3 across two availability zones. A Data Services Layer exposes unified APIs for component invocation, se