Data, AI, and Hybrid Patterns
Data, AI, and hybrid systems cross many trust and operating boundaries. This part shows how to design their flows, governance, safety, connectivity, lifecycle, and feedback as first-class architecture responsibilities.
What You Will Learn
Separate operational data, analytical data, ingestion, processing, and serving concerns.
Ground AI systems in data, tools, authorization, safety, evaluation, and monitoring.
Design hybrid connectivity, identity, routing, migration, and operating ownership.
Compare implementation options without losing the provider-neutral pattern.
Chapters
Data and Analytics Architecture — organize ingestion, storage, processing, governance, serving, and operations.
AI and Agentic Architecture — design model, retrieval, tool, safety, evaluation, and cost boundaries.
Hybrid Cloud Architecture — connect cloud and existing environments while preserving identity, resilience, and ownership.
How the Chapters Connect
The data chapter builds trustworthy information flows. The AI chapter uses those flows while adding probabilistic behavior and tool-use risk. The hybrid chapter extends both patterns across locations, networks, and operating teams.