Analytics Design & Architecture in the AI Era
A 10-article series on how AI is reshaping analytics design and architecture — moving teams from rigid, human-curated data stacks toward connected, intelligent 'fabrics' where AI agents query, enrich, and act alongside human analysts. Written as a learning resource for analytics professionals at every level, from beginners to senior architects.
Data Fabric & Data Mesh
A spider web woven from many independently-owned strands, connected by shared silk, so data stays decentralized in ownership yet reachable from any point.
Data Contracts & Schema Design
The handshake agreement between data producers and consumers, spelling out the exact terms before any data changes hands.
Context Engineering for AI Agents
Packing an agent's backpack with exactly what a task needs — no dead weight, nothing missing — before sending it out on the trail.
Batch vs. Event-Driven Architecture
A postal worker runs the scheduled mail route and the rush courier desk out of the same office, showing when to batch data on a schedule and when to react to it the instant it happens.
Data Pipelines & ETL/ELT
Moving from batch scripts to AI-aware pipelines that clean, enrich, and route data continuously.
Data Platform Cost & FinOps
Keeping compute and storage spend sane as AI workloads multiply queries.
Data Quality & Observability
Catching bad data before it poisons a model, dashboard, or an autonomous agent's decision.
Data Warehousing & Lakehouses
The evolving home for structured and unstructured data, and how AI queries it directly.
Orchestration & Workflow Tools
Coordinating jobs, humans, and agents across a modern data stack.
Semantic Layers & Metrics Stores
A shared source of truth so humans and AI agents mean the same thing by "revenue."
Streaming & Real-Time Data
Architectures for data that never stops moving, and the AI systems watching it live.