Rediscovering DataFrames: 100× Analytics Without Leaving Pandas

You've hit the pandas ceiling: 10 GB DataFrames choke, you need vector search for embeddings, and reading from S3 takes longer than the query itself. The usual upgrades hurt — Spark rewrite, new ORM, separate vector DB. There's a shorter path: keep the pandas API, swap the engine underneath. Trade up to 100× faster analytics, native vector search, direct querying of S3 / Postgres / 40+ formats as SQL table functions, and time-series functions. Built on chDB — the in-process build of ClickHouse, which I created and lead at ClickHouse Inc. We'll cover the migration cheat-sheet, concrete latency numbers, honest tradeoffs vs DuckDB and native pandas, and close with a live build: a pandas-heavy data pipeline migrated column-by-column. Code published as an open repo.