Most engineers reach for Spark or BigQuery the moment they hear "10 million records." I did too — until I tried DuckDB. What happened next surprised me: 47 seconds, on my laptop, with 4GB RAM. No cluster. No cloud bill. No YAML configuration files. Let me show you exactly how I did it. 🤔 Why DuckDB? DuckDB is an in-process analytical database — think SQLite, but built for OLAP workloads. It runs entirely in memory using columnar storage and vectorized execution. The numbers speak for themselves: Tool 10M Records Query Time Infrastructure Pandas ~4.2 minutes Local PySpark ~1.8 minutes Local cluster setup DuckDB 47 seconds Local (no setup) Polars ~55 seconds Local 🛠️ Setup (30 seconds) pip install duckdb pandas Enter fullscreen mode Exit fullscreen mode That's it. No Docker. No JVM. No configuration.…