<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Parquet on Home</title><link>https://oedokumaci.com/tags/parquet/</link><description>Recent content in Parquet on Home</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Oral Ersoy Dokumaci</copyright><lastBuildDate>Mon, 05 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://oedokumaci.com/tags/parquet/index.xml" rel="self" type="application/rss+xml"/><item><title>Why Parquet Dominates Modern Data Engineering</title><link>https://oedokumaci.com/blog/posts/apache-parquet/</link><pubDate>Mon, 05 Oct 2026 00:00:00 +0000</pubDate><guid>https://oedokumaci.com/blog/posts/apache-parquet/</guid><description>Changing one line from CSV to Parquet makes reading the fares of ten million New York taxi trips 27 times faster, and the file is a sixth of the size. In this chapter, we measure why with Polars, DuckDB, PyArrow, and pandas: columnar storage, types in the file, the footer, encodings, pages, compression, predicate pushdown, and row group size. We also see where row storage still wins.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://oedokumaci.com/blog/posts/apache-parquet/featured.jpg"/></item></channel></rss>