Building Intelligent Enterprise Data Applications in Regulated, Large-Scale Environments

Delivering intelligent, data-driven features in enterprise applications is rarely about adopting new tools. Regulation, data ownership, performance constraints, and existing platforms usually drive the architecture. In this session, two practitioners with over 15 years of experience share how they designed and operated intelligent application features on top of established enterprise data platforms in large, regulated environments.

The session covers how SQL Server–based systems, Snowflake, and Databricks were used together to support transactional, analytical, and application workloads. We’ll discuss architectural decisions that worked, those that didn’t, and how governance and operational constraints shaped the final designs. The focus is on real trade-offs rather than idealised architectures, with examples drawn from production systems.

After this session, attendees will be able to: Understand common constraints when building intelligent features on enterprise data platforms Identify safe patterns for exposing data to application workloads Recognise architectural trade-offs between performance, governance, and maintainability Apply practical lessons learned from large-scale, regulated environments