Architecture Library
Reusable data architecture patterns — from traditional analytics stacks to AI-native platforms. Each diagram shows a distinct engineering approach and its trade-offs.
Traditional Analytics Stack
The foundational pattern: structured ETL pipelines move data from operational sources into a centralised DWH, where BI tools deliver reports. Reliable, well-understood, but batch-oriented and not AI-ready.
Modern Data Platform
The lakehouse pattern decouples storage from compute, enabling open formats like Apache Iceberg to serve both BI queries and AI workloads. A semantic layer adds governed metric access on top.
AI-Native Analytics Platform
The next evolution: a governed data platform designed specifically for AI reasoning. The semantic layer becomes the contract between data and AI, enabling explainable, consistent, trustworthy answers.
Medallion Architecture
The dbt-enforced three-layer data model that separates raw ingestion from transformation from business logic. Raw preserves history. Staging cleans and types. Marts serve governed metrics.
Lakehouse Architecture
Storage decoupled from compute using Apache Iceberg on object storage. Open table format enables ACID transactions, schema evolution, and time-travel queries — serving both BI and AI workloads from the same data.
AI Agent over DWH
The autonomous analytics pattern: a scheduled AI agent monitors governed metrics, detects anomalies, generates natural language insights, and escalates to human review only when thresholds are crossed.