A high-level architecture story spanning cloud object storage, Snowflake and Databricks: layered data design, incremental processing, semantic access, interoperability, governance, observability and cost-aware workload engineering.
This page intentionally stays at architecture level today. It describes the patterns and engineering direction without exposing client-confidential implementation details.
Organized cloud data around clear processing responsibilities: preserve source fidelity at ingestion, add operational metadata and incremental-processing controls, then expose governed consumption-ready datasets.
Combined scalable data processing with governed semantic access and AI capabilities, while treating compute sizing, query design and workload isolation as architectural concerns.
Used lakehouse patterns where distributed processing, open formats or cross-platform access are valuable. Delta supports transactional lake processing, while Iceberg is evaluated where interoperability across engines is a primary requirement.
Extended the platform toward natural-language analytics and lightweight data applications while maintaining visibility into workload behavior and consumption.