Enterprise Data Platform
End-to-end reference architecture for a governed enterprise data platform — ingestion, storage, transformation, serving, and observability as one coherent system.
Reference Architectures
A curated collection of the platform and AI architecture patterns I design and build with — each with the thinking behind it.
End-to-end reference architecture for a governed enterprise data platform — ingestion, storage, transformation, serving, and observability as one coherent system.
Unifying the flexibility of a data lake with the reliability and performance of a warehouse, using open table formats and a governed serving layer.
Bronze, silver, and gold layers that progressively refine raw data into business-ready assets, with quality gates between each promotion.
A multi-agent system where planner, executor, and critic agents coordinate through shared state and tools to complete complex tasks autonomously.
Retrieval-augmented generation flow: ingestion and chunking, embedding, hybrid retrieval, reranking, and grounded generation with citations.
Using the Model Context Protocol to give agents secure, standardised access to enterprise tools, data sources, and actions.
A stateful, cyclic agent workflow modelled as a graph — enabling retries, branching, and human-in-the-loop checkpoints.
Cloud-native platform blueprint with infrastructure as code, serverless compute, and secure-by-default networking on AWS.
Warehouse sizing, secure data sharing, streams and tasks, and governance patterns for a cost-optimised Snowflake deployment.
Loosely coupled services communicating through events, enabling real-time processing, scalability, and resilience.
Independently deployable services with clear contracts, enabling teams to ship in parallel without stepping on each other.
High-throughput streaming pipeline for real-time ingestion, processing, and serving of continuous data at scale.
Whether you're modernising a data platform or bringing agentic AI into production, I'd love to hear what you're working on.