<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Arjun Sajeevan</title><link>https://arjunsajeevan.com/</link><description>Recent content on Arjun Sajeevan</description><generator>Hugo -- 0.156.0</generator><language>en-us</language><lastBuildDate>Thu, 19 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://arjunsajeevan.com/index.xml" rel="self" type="application/rss+xml"/><item><title>Fastest Teradata Migration: TPT</title><link>https://arjunsajeevan.com/posts/fastest-teradata-migration-tpt/</link><pubDate>Thu, 19 Mar 2026 00:00:00 +0000</pubDate><guid>https://arjunsajeevan.com/posts/fastest-teradata-migration-tpt/</guid><description>How to move 2 Billion+ records using Teradata Parallel Transporter (TPT) for maximum throughput.</description></item><item><title>PySpark DataFrames vs. Spark SQL: Which One Should You Use?</title><link>https://arjunsajeevan.com/posts/spark-api-vs-sql/</link><pubDate>Thu, 26 Feb 2026 00:00:00 +0000</pubDate><guid>https://arjunsajeevan.com/posts/spark-api-vs-sql/</guid><description>A deep dive into performance, dynamic queries, and reusability to help you choose the right tool for your Spark pipelines.</description></item><item><title>Modernizing Architecture: Migrating from Hadoop to Data Lakehouse</title><link>https://arjunsajeevan.com/posts/hadoop-to-datalakehouse/</link><pubDate>Wed, 25 Feb 2026 00:00:00 +0000</pubDate><guid>https://arjunsajeevan.com/posts/hadoop-to-datalakehouse/</guid><description>A deep dive into why enterprises are moving away from legacy Hadoop systems toward a unified Data Lakehouse architecture.</description></item><item><title>Real-Time Streaming Analytics: Kafka &amp; PySpark</title><link>https://arjunsajeevan.com/projects/streaming-analytics/</link><pubDate>Sun, 22 Feb 2026 00:00:00 +0000</pubDate><guid>https://arjunsajeevan.com/projects/streaming-analytics/</guid><description>A deep dive into building an event-driven data pipeline using Python, Docker, Apache Kafka, and PySpark Structured Streaming.</description></item><item><title>SQL Server Performance: Accelerating Inserts with TABLOCK</title><link>https://arjunsajeevan.com/posts/sql-tablock-performance/</link><pubDate>Sat, 21 Feb 2026 00:00:00 +0000</pubDate><guid>https://arjunsajeevan.com/posts/sql-tablock-performance/</guid><description>How to use TABLOCK to enable minimal logging and speed up bulk data loads in SQL Server.</description></item><item><title>Contact</title><link>https://arjunsajeevan.com/contact/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://arjunsajeevan.com/contact/</guid><description>&lt;p&gt;I am always open to discussing data architecture, challenging engineering problems, or potential collaborations.&lt;/p&gt;
&lt;p&gt;The best way to reach me is via email or LinkedIn. I typically respond within 24 hours.&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;📧 &lt;strong&gt;Email:&lt;/strong&gt; &lt;a href="mailto:arjunsajeevan.career@gmail.com"&gt;arjunsajeevan.career@gmail.com&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;💼 &lt;strong&gt;LinkedIn:&lt;/strong&gt; &lt;a href="https://www.linkedin.com/in/arjun-sajeevan-66634a167"&gt;Arjun Sajeevan&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;🐙 &lt;strong&gt;GitHub:&lt;/strong&gt; &lt;a href="https://github.com/arjun-sajeevan"&gt;arjun-sajeevan&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;If you are reaching out regarding freelance consulting or system architecture audits, please include a brief overview of your current tech stack in your message.&lt;/p&gt;</description></item></channel></rss>