<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Praitna Blog</title><description>Insights on AWS data engineering, AI/ML and LLMs.</description><link>https://www.praitna.com/</link><item><title>Building a Production-Ready Lakehouse on AWS with Apache Iceberg</title><link>https://www.praitna.com/blog/aws-lakehouse-with-apache-iceberg/</link><guid isPermaLink="true">https://www.praitna.com/blog/aws-lakehouse-with-apache-iceberg/</guid><description>A practical blueprint for an S3 + Iceberg lakehouse on AWS: layers, services, and the operational details that decide whether it succeeds.</description><pubDate>Thu, 01 Oct 2026 00:00:00 GMT</pubDate></item><item><title>RAG on AWS: Taking LLM Prototypes to Production</title><link>https://www.praitna.com/blog/rag-on-aws-from-prototype-to-production/</link><guid isPermaLink="true">https://www.praitna.com/blog/rag-on-aws-from-prototype-to-production/</guid><description>Retrieval-augmented generation demos are easy. Production RAG is not. Here is what changes between the two, and how to build it on Amazon Bedrock.</description><pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Staff Augmentation vs. Project Delivery: Choosing the Right Model for Data &amp; AI Work</title><link>https://www.praitna.com/blog/staff-augmentation-vs-project-delivery/</link><guid isPermaLink="true">https://www.praitna.com/blog/staff-augmentation-vs-project-delivery/</guid><description>When should you add engineers to your team, and when should you hand off an outcome? A practical guide for data and AI leaders.</description><pubDate>Sat, 05 Sep 2026 00:00:00 GMT</pubDate></item></channel></rss>