What if your Python models could run inside your database—at scale, with parallel execution, and no data movement? Meet Exasol: the high-performance analytics database that speaks native Python, supercharged by a massively parallel processing (MPP) engine. In this talk, we’ll dive into how Exasol empowers Python developers and data scientists to run custom Python code—directly where the data lives—using user-defined functions (UDFs) and fully customizable script language containers.

Whether you’re doing model training, forecasting, categorization, or even tapping into the power of large language models, Exasol brings Python to the party with native support and serious horsepower.

You’ll learn how to: -Execute high-performance Python code inside your database using UDFs. -Bring any Python library into Exasol with containerized script languages. -Scale inference and forecasting across thousands of sensors or data points using Exasol’s MPP engine—no batch jobs, no bottlenecks. -Call APIs or run models in-database to enable real-time, insight-driven applications.

We’ll showcase real-world examples, like how one company forecasts sensor traffic volume across entire regions to optimize planning—running thousands of model inferences simultaneously with high speed performance.

If you’re tired of waiting for your models to run—or moving massive datasets just to do a quick prediction—this talk is for you. Python meets MPP, and the result is next-level analytics.