Anant Pingle

Building a data-driven application is easy; building one that is secure, performant, and scalable is where most teams struggle. This session provides a definitive field guide to the architectural patterns used by the most successful builders on the Databricks App platform. We will break down the ""how-to"" for the most in-demand app categories: Internal Tools, AI Copilots, Agentic Workflows, and Customer-Facing Products. For each pattern, we will dive into the technical specifics that matter most: - Connectivity & State: Best practices for integrating with Databricks SQL and Lakebase. - Identity & Auth: Implementing On-Behalf-Of (OBO) tokens and service principal delegation to ensure data privacy. - Agentic Design: Orchestrating LLMs and tool-calling within a full-stack environment. - Performance: Strategies for caching and low-latency UI responses. Join us to learn how to avoid common pitfalls and leverage standardized blueprints to ship better apps.

In this talk, we showcase how NS (Dutch Railways) built an end-to-end Databricks solution that processes train telemetry in real time, enabling live detection of carriage failures and improving overall reliability. The platform handles more than 100 billion data points per day with sub-second end-to-end latency. The session features a live demo of real train telemetry, giving the audience a direct, in‑room experience of real time. We explain how the solution was engineered using the new Real-Time Mode for Spark Structured Streaming. The entire stack is built in PySpark and SQL, so no microservices, no separate streaming framework, just Databricks. Throughout the session, we connect to other high velocity domains with cases like real time parcel rerouting, live airport baggage tracking and real-time signals feeding analytics and AI. Attendees leave with practical insights into bringing real time data into the modern data stack without the complexity of traditional streaming systems.