<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Information on Parquet Features on Parquet</title><link>/blog/features/</link><description>Recent content in Information on Parquet Features on Parquet</description><generator>Hugo</generator><language>en</language><atom:link href="/blog/features/index.xml" rel="self" type="application/rss+xml"/><item><title>Variant Type in Apache Parquet for Semi-Structured Data</title><link>/blog/2026/02/27/variant-type-in-apache-parquet-for-semi-structured-data/</link><pubDate>Fri, 27 Feb 2026 00:00:00 +0000</pubDate><guid>/blog/2026/02/27/variant-type-in-apache-parquet-for-semi-structured-data/</guid><description>&lt;p&gt;The Apache Parquet community is excited to announce the addition of the &lt;strong&gt;Variant type&lt;/strong&gt;—a feature that brings native support for semi-structured data to Parquet, significantly improving efficiency compared to less efficient formats such as JSON. This marks a significant addition to Parquet, demonstrating how the format continues to evolve to meet modern data engineering needs.&lt;/p&gt;
&lt;p&gt;While Apache Parquet has long been the standard for structured data where each value has a fixed and known type, handling heterogeneous, nested data often required a compromise: either store it as a costly-to-parse JSON string or flatten it into a rigid schema. The introduction of the Variant logical type provides a native, high-performance solution for semi-structured data that is already seeing rapid uptake across the ecosystem.&lt;/p&gt;</description></item><item><title>Native Geospatial Types in Apache Parquet</title><link>/blog/2026/02/13/native-geospatial-types-in-apache-parquet/</link><pubDate>Fri, 13 Feb 2026 00:00:00 +0000</pubDate><guid>/blog/2026/02/13/native-geospatial-types-in-apache-parquet/</guid><description>&lt;p&gt;Geospatial data has become a core input for modern analytics across logistics, climate science, urban planning, mobility, and location intelligence. Yet for a long time, spatial data lived outside the mainstream analytics ecosystem. In primarily non-spatial data engineering workflows, spatial data was common but required workarounds to handle efficiently at scale. Formats such as Shapefile, GeoJSON, or proprietary spatial databases worked well for visualization and GIS workflows, but they did not integrate cleanly with large scale analytical engines.&lt;/p&gt;</description></item></channel></rss>