Architecture patterns, system design, and lessons from building data & AI platforms at scale.
A practical field guide to shipping AI-built apps that survive real users: the six blind spots, scaling & tenancy explained, real-world problems with modern fixes, a build lifecycle, and a do's & don'ts reference.
A 9-step architecture that turns thousands of unstructured documents into reliable, grounded answers — applicable to legal, medical, financial, and any knowledge-dense domain.
When documents disagree — and they will — your system must know who wins and tell the user why. A systematic approach for any domain with 1000+ documents.
How to structure storage, indexing, and ingestion when your RAG system grows from a PoC to a production knowledge base — in any industry.
The conversation has shifted from building reports to building intelligent data ecosystems. Here's the analytics maturity journey I've seen across enterprises.
Every dataset should serve four consumers: reporting, advanced analytics, ML, and GenAI. Here's the architecture principle I follow — and the common mistakes I see.
A successful migration is a business transformation disguised as a technology project. Three approaches, architecture principles, and a readiness scorecard from real-world enterprise migrations.
Why Data Vault, Star Schemas, and Big Flat Tables all have a place in modern data platforms — and why the best architects are loyal to outcomes, not patterns.
Data Architecture is not a technology role — it is a decision-making role. Six pillars, four career paths, a learning roadmap, and the mindset shift that separates engineers from architects.