<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Apache Spark on NV Trends</title><link>https://blogs.nvtrends.com/tags/apache-spark/</link><description>Recent content in Apache Spark on NV Trends</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 11 Oct 2026 03:50:20 +0000</lastBuildDate><atom:link href="https://blogs.nvtrends.com/tags/apache-spark/index.xml" rel="self" type="application/rss+xml"/><item><title>What is Big Data and How It Is Used: Complete Guide</title><link>https://blogs.nvtrends.com/posts/2026/what-is-big-data/</link><pubDate>Wed, 18 Feb 2026 14:45:00 +0000</pubDate><guid>https://blogs.nvtrends.com/posts/2026/what-is-big-data/</guid><description>&lt;p&gt;Big Data refers to massive, rapidly accumulating, and complex collections of structured, semi-structured, and unstructured digital information that exceed the storage capacity, processing throughput, and analytical capabilities of traditional relational databases. In a conventional enterprise software setup, a relational database management system (RDBMS) like MySQL or PostgreSQL operating on a single physical server can manage structured transactional records with fixed schemas. However, when incoming data scales from gigabytes into petabytes and streams arrive in millisecond intervals, single-server systems encounter severe hardware bottlenecks across disk input/output (I/O), network bandwidth, and memory allocation.&lt;/p&gt;</description></item></channel></rss>