Ankur Jain
Ankur is a Senior Data & Cloud Engineer at Innablr, working at the intersection of cloud infrastructure and modern data platforms. Based in Melbourne, he helps teams design efficient pipelines and scale analytics using tools like DuckDB, Databricks, and dbt.
Outside of work, he’s an anime nerd and a firm believer that ducks are objectively the coolest animals. Ankur thinks good tooling should feel like magic, bad tooling should be deleted, and Jupyter notebooks should come with a warning label.
Session
I’ve used pandas for years, but as my data grew, my local workflows started to slow down. Joins got sluggish, memory errors showed up, and simple tasks became harder to manage. That’s when I found DuckDB, a fast, in-process SQL engine that brought the speed and flexibility I was missing.
This talk isn’t about replacing pandas. It’s about knowing when to reach for something different. I’ll share how DuckDB helped streamline my workflow, with real examples, side-by-side comparisons, and a quick intro to DuckLake, a SQL-based Lakehouse format that fits naturally into modern Python analytics.