About This Architecture

Streamlit-based synthetic data generation platform leveraging Amazon Bedrock LLM to create realistic test datasets from schema metadata and feed templates. The Streamlit Web UI connects to a Python backend service that orchestrates Amazon Bedrock API calls, feeding the LLM with knowledge from SAM documentation and feed structure definitions. The Synthetic Data Generator produces test data outputs validated against schema metadata, enabling rapid prototyping of data-driven applications without exposing production data. Fork this diagram on Diagrams.so to customize the backend logic, swap LLM models, or integrate additional data sources and validation layers.

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How do I build a synthetic data generation pipeline using Amazon Bedrock and Streamlit?

This diagram shows a complete synthetic data generation architecture where Streamlit Web UI connects to a Python backend that calls Amazon Bedrock LLM. The LLM uses knowledge from SAM documentation and feed templates to generate realistic test data validated against schema metadata, enabling safe prototyping without production data exposure.