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DZone.com Feed Recent posts on DZone.com From ETL to Lakeflow: Shifting to a Declarative Data Paradigm Jun 15th 2026, 15:00 by Seshendranath Balla Venkata If you've worked on a data platform for more than a few years, you've almost certainly built the same pipeline twice. First, the way the team wrote pipelines in 2019: a notebook here, a Python script there, an Airflow DAG to glue it all together, and a long document explaining the order things had to run in. Then the rewrite, two years later, when somebody quit, and nobody could remember why a particular task had a sleep(180) in it.  Lakeflow is Databricks' answer to that pattern, and the shift it's pushing for is bigger than the marketing makes it sound. It isn't a new orchestrator. It's a move from imperative pipelines, where you write the steps, to declarative pipelines, where you write the destination and let the engine figure out the steps. What follows is the practical version of that shift — what's actually different, where the gains are real, and how to migrate without ending up with a half-converted lakehouse. ‍ I Reverse-Engineered 50 API Breaches. The Same Five Mistakes Keep Appearing. Jun 15th 2026, 14:00 by Igboanugo David Ugochukwu Between December 22, 2025 and January 15, 2026, an attacker spent 24 consecutive days inside Navia Benefit Solutions' systems. They quietly and methodically pulled Social Security numbers, dates of birth, health plan enrollment details, and COBRA records belonging to 2,697,540 Americans. These include teachers, state workers, and school administrators. People who signed up for employer benefits through HR software and had no idea which third-party company held their data. Navia didn't catch it for more than three weeks after the attacker had already stopped. The company published a breach notice on March 13, 2026. Individual notification letters went out on March 18 — eighty-six days after the intrusion began. ‍ Building a Vector Index in Azure AI Search: HNSW, Profiles, and RAG Retrieval Jun 15th 2026, 13:00 by Jubin Abhishek Soni In this article, we will understand how vector search works in Azure AI Search and how to use it as the retrieval layer in a Retrieval-Augmented Generation (RAG) system. The article is meant for software engineers. We will not stop at theory. We will build a small, working example that you can run on your own machine and follow along step by step. By the end, you will have a small document search service that takes a user question, finds the most relevant text using vector similarity, and prepares the context that you can pass to a language model. ‍ Engineering Closed-Loop Graph-RAG Systems, Part 4: Evaluating a Graph-RAG System Jun 15th 2026, 12:00 by Sriharsha Makineni This article is part 4 of a 4-part series on 'Engineering Closed-Loop Graph-RAG Systems.' The simplest method to evaluate a RAG system is by asking yourself if your generated answer is correct. ‍ ‍ You are receiving this email because you subscribed to this feed at blogtrottr.com. By using Blogtrottr, you agree to our terms. If you no longer wish to receive these emails, you can unsubscribe from this feed, edit this subscription, or manage all your subscriptions.