Data & AI Architect
Jonatan Collante
I believe enterprise Data & AI platforms should make governance, reliability and operability architectural properties — not production afterthoughts.
Databricks · Azure · Data Platforms · Agentic AI · Governance
Explore architecture →Selected Architecture
Enterprise Data & AI Platform
A reference architecture for governed, scalable and production-ready Data & AI capabilities on Databricks and Azure.
Detailed description
Five-stage pipeline. Sources — operational systems, SaaS applications, and streaming events — flow into Ingestion, which handles batch and CDC/streaming patterns. Ingestion lands data in the Data Platform: a bronze, silver, gold lakehouse enclosed by a Unity Catalog governance boundary that enforces access control and lineage for everything inside it. The platform produces governed Data Products — curated datasets and feature sets — which are consumed by BI, ML/model serving, and agentic AI applications.
- 01
Architecture
Reference architectures for enterprise Data & AI systems.
- 02
Accelerators
Reusable frameworks for moving from PoC to production.
- 03
Insights
Architecture decisions, analyses and perspectives.
- 04
Lab
Selected experiments in emerging Data & AI patterns.
Selected Architecture Decisions
ADR·001
Single-Agent vs Multi-Agent Architecture
When does additional agent autonomy create value, and when does it just create another governance boundary to operate?
Latest Insights
perspective
Governance as Architecture, Not Afterthought
Treating governance as a property decided at design time, not a control layer bolted on once a platform is already in production.
About
Jonatan Collante is a Data & AI Architect working at the intersection of enterprise data platforms, cloud architecture and AI. His focus is turning technically promising ideas into governed, operable and production-ready systems.
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