5 DuckDB–Arrow–Polars Workflows in Minutes

Turn day-long pipelines into small, local, reproducible runs without clusters or drama.

5 min readSep 17, 2025

Five practical DuckDB–Apache Arrow–Polars workflows to crush ETL time: query Parquet fast, move data zero-copy, and ship clean datasets in minutes.

You open a notebook. Point SQL at Parquet. Hand the result to a blazing DataFrame without copies. Ten minutes later, you’ve reshaped gigabytes into tidy output — no cluster tickets, no surprise bills. That’s the DuckDB–Arrow–Polars handshake at work.

Why this trio works

DuckDB is a vectorized, in-process SQL engine that loves Parquet and pushes filters/projections down to the file. Apache Arrow is the columnar memory format that lets data hop between systems without serialization. Polars is a Lightning-fast DataFrame with a lazy engine built on Arrow, ideal for columnar transforms and final tidy-ups.

Together they shrink ETL because you:

Scan once, copy never. DuckDB reads Parquet, outputs Arrow; Polars reads Arrow directly.
Do set logic where it’s cheapest. Joins, windows, and aggregations fly in DuckDB; row-wise tweaks and featurization are ergonomic in Polars.
Stay in open formats…

Another great article with more food for thought.

In Workflow 2: Warehouse Offload & Reconciliation (Postgres → Parquet → Diff), have you considered the pg_duckdb extension and how it would affect the integration of polars or not?

Thanks for your article, I will start following you.

5 DuckDB–Arrow–Polars Workflows in Minutes

Turn day-long pipelines into small, local, reproducible runs without clusters or drama.

Why this trio works

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Written by Thinking Loop

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