High-Perf Data

Why Polars and DataFusion share Arrow's columnar memory layout instead of inventing their own row-based structures, and what that shared substrate buys you.

Learn high-performance data processing in Rust with 9 Polars examples, covering CSV reading, lazy filtering, and schema inspection.

Explore Polars, the high-performance Rust DataFrame library. Learn how lazy query optimization, vectorized execution, and Arrow-backed memory layout boost data processing.

Learn to read and write CSV and Parquet files in Rust using Polars, manage schemas, apply compression, and optimize scan performance.

Build fast ETL tools in Rust using Clap for CLIs and Tokio/Axum for services, ensuring observability and clear failure modes.

Move dataframes between Rust and Python with Apache Arrow. Avoid slow serialization and preserve data types for high-performance data workflows.

Optimize data pipelines in Rust with best practices for Polars and Arrow. Learn to design, optimize, and troubleshoot high-performance ETL.

A single-page roundup of every highlight bullet from the 10 pages in the High-Perf Data section, grouped by source page so you can scan all 50 takeaways without opening each article individually.