Jakob Mund

Modern data platforms are rarely islands; they exist in a sprawling ecosystem of external databases, APIs, and third-party services. However, managing the "connective tissue"—JDBC drivers, secret rotation, and custom logic—often creates security silos and governance gaps. In this session, we will hands-on demonstrate how the next generation of Unity Catalog Connections, Secrets and Functions work in concert to centralize external data access, eliminate hardcoded credentials, and deploy governed UDFs across any compute type. Attendees will learn how to build a "zero-trust" data architecture that simplifies the user experience while providing full lineage and auditability from the source to the final insight.

SQL warehouses in Databricks can run more than just SQL. Join this session to learn how to get more out of your SQL warehouses and any tools built on top of it by leveraging Python. After attending this session, you will be familiar with Python user-defined functions and how to bring in custom dependencies from PyPi, as a custom wheel or even securely invoke cloud services with performance at scale.

Modern data workloads span multiple sources — data lakes, databases, apps like Salesforce and services like cloud functions. But as teams scale, secure data access and governance across shared compute becomes critical. In this session, learn how to confidently integrate external data and services into your workloads using Spark and Unity Catalog on Databricks. We'll explore compute options like serverless, clusters, workflows and SQL warehouses, and show how Unity Catalog’s Lakeguard enforces fine-grained governance — even when concurrently sharing compute by multiple users. Walk away ready to choose the right compute model for your team’s needs — without sacrificing security or efficiency.