Point of the talk: To show developers how to eliminate costly, complex cloud data warehouses for medium-scale datasets by embedding DuckDB directly into Python applications for serverless, zero-copy data processing.

Duration: 45 Minutes

Detailed Breakdown: Many data pipelines unnecessarily spin up heavy, distributed clusters (like Spark) to process 10–50 GB datasets, incurring high financial costs and latency. This talk introduces DuckDB as an in-process, columnar analytical engine that lives inside your Python runtime. We will explore how to write ultra-fast SQL directly over local or remote Parquet, CSV, and JSON files. Attendees will learn the architecture of zero-copy memory sharing between DuckDB and PyArrow/Polars, ensuring seamless data handoffs without serialization overhead. Finally, we will build a local-first dashboard pipeline that runs entirely on a single machine while matching the speed of a cloud warehouse.