Jasper Puts

The rise of AI agents and intelligent applications has fundamentally changed what developers need from their data infrastructure. Modern applications demand low-latency access to operational data, seamless integration with AI models, and the ability to iterate rapidly. All without sacrificing security or governance. Traditional approaches often force developers to manage separate systems, adding complexity and slowing down innovation. Lakebase brings a fully managed Postgres database directly into the Databricks ecosystem, enabling developers to build applications with familiar tools while benefiting from native integration with the broader lakehouse architecture. This unified approach eliminates data silos and simplifies the development of intelligent applications that combine transactional workloads with analytics and AI. In this session, we will explore how Lakebase transforms the developer experience for building modern data and AI applications. We'll demonstrate how to leverage Lakebase for rapid prototyping, discuss its role in powering agentic workflows that require fast read/write access to structured data, and show how governance is maintained through Unity Catalog integration. Attendees will gain practical insights into accelerating their development cycles and building production-ready applications in the agentic age.

In today’s landscape, the rapid adoption of (Gen)AI has made applications essential for delivering real-time insights, personalized experiences, and operational efficiencies. However, developing and maintaining these applications is often complex and time-consuming, particularly when considering secure access to your data and models. Databricks Apps enables developers to build secure and scalable applications using popular frameworks such as Streamlit and Dash. Seamlessly integrated with the Databricks ecosystem, it empowers you to effortlessly build impactful applications that unlock the full potential of your data and AI assets.It ensures robust security through integration with Unity Catalog, providing fine-grained access control and governance. In this session, we will explore how users can develop and deploy Databricks Apps following best practices for code structure and CI/CD. We will discuss the development options that are available, demonstrate how to automate the deployment process, and highlight strategies for maintaining application quality and security. The session will feature live demos, and all code examples will be shared so participants can adapt them for their own use cases. Attendees will leave with practical knowledge to accelerate their app development lifecycle on Databricks.

As data engineering projects tackle complex and ever-growing amounts of data, many organizations turn to Databricks as their platform of choice for Spark workloads. Databricks offers a dozen of possibilities to develop your ETL pipelines, like notebooks, Python scripts, IDE support, and more. This makes it sometimes challenging to select the most suitable approach for a specific scenario or team composition. This session will cover the different options that are available, as well as discussing their pros and cons, and when to use which approach. Some software engineering best-practices will also be covered and how they can be easily included in data projects. By the end of the session, the audience will know some low-hanging fruits that offer key advantages for structuring projects and facilitate collaborating within.