Elizabeth Christensen

In this hands-on lab, we put you in the driver’s seat of the modern AI application stack. Together we will build a powerful, data-friendly Python application using just two core technologies: Streamlit’s “pure Python" approach for a rapid, beautiful frontend, and Postgres for robust data handling and semantic search. We will build a functional AI agent that allows users to query data using Snowflake Cortex AI. You will learn how to build a Streamlit interface, connect to an agent, and add interactive, data-driven elements like charts, graphs and maps. You’ll leave this session with the tools to unlock value by turning existing data and static text into an interactive AI agent.

Learn how data engineering roles continue to evolve as the world of OLTP and OLAP merge with Snowflake new capabilities. With increasing integration between Snowflake and Postgres, this session demonstrates new architectures that are now feasible with Snowflake and Postgres. You'll see how new features like pg_lake, in combination with Iceberg, will bridge the divide between Snowflake and Postgres as you say goodbye to older extract and load design patterns.

Security in your database needs to be handled at quite a few different levels, from individual users, to in transit calls, and the server and storage itself. This talk is a high level introductory overview of security topics and best practices in Postgres.

PostGIS forms the backbone of many open-source geospatial projects. I’ll help you get started with managing Postgres outside of your local machine. We’ll cover hosting, sizing, tuning, backups, connections, and basic performance topics to get you started.

Pgfeatureserv and pgtileserv are lightweight Go applications for getting your spatial data into web apps. This will be an introductory talk on how to set up these tools, use the sample code, and build your own spatial web maps.