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DZone.com Feed Recent posts on DZone.com Is the Data Warehouse Dead? 3 Patterns From Enterprise Architecture That Answer This Question Jun 5th 2026, 15:00 by Nabarun Bandyopadhyay Architectural Debate There is a classic debate that data architects often have among themselves: how to fit a traditional data warehouse on a data lake or enterprise data platform. This article walks through the architecture evolution and describes three architecture patterns that I have implemented across enterprises to help you decide where a data warehouse fits in a modern data platform. The data warehouse acted as a single source of truth that finance, retail, and operations teams could trust for day-to-day reporting. Appliance warehouses like Teradata, Netezza, and SybaseIQ dominated enterprise data for decades, and SQL was the universal language that held it all together. ‍ Spring AI Advisors: Chat Memory, Token Tracking, and Message Logging Jun 5th 2026, 14:30 by Horatiu Dan Abstract The previous two articles in this series — Building a Spring AI Assistant with MCP Servers: A Step-by-Step Tutorial and Securing the AI Host and Spring AI MCP Server Communication with API Keys — laid the groundwork for moving from prototype to production when building business-driven Spring AI applications. In this last one, the tutorial is concluded. Why Advisors? When you build something with Spring AI's ChatClient, sooner or later you want behavior that crosses every request — keep conversation history so the next prompt has context, count tokens so you know what each call costs, log the raw request and response payloads when something goes wrong. Threading that logic through your service code, one method at a time, is exactly the kind of cross-cutting concern Aspect-Oriented Programming was invented for, and Spring AI's advisors are essentially that: AOP for the AI call path. ‍ Why Round-Robin Won't Save You: Load Balancing Challenges in Data Streaming Services With Heterogeneous Traffic Jun 5th 2026, 14:00 by Semyon Slepov If you've ever run a data streaming service that handles more than one type of workload, you've probably hit a wall that no amount of round-robin tuning can fix. This is a common failure mode in production streaming environments. This post is about the specific ways traditional load-balancing strategies break down when your traffic isn't uniform. I'll focus on CPU utilization as the primary example throughout, since it's the most common bottleneck in compute-heavy streaming workloads, but the same principles apply to memory, network bandwidth, and other system resources. ‍ How to Parse Large XML Files in PHP Without Running Out of Memory Jun 5th 2026, 13:00 by Nicholas Volkhin XML is still everywhere: supplier feeds, marketplace catalogs, partner exports, legacy APIs, SOAP-ish payloads, ETL jobs. None of that is glamorous, but plenty of production systems still depend on it. The real problem starts when the file is no longer small. ‍ Why Your Test Automation Is Always Behind the Code And the Architecture That Fixes It Jun 5th 2026, 12:00 by Waqar Hashmi There is a pattern that repeats itself across engineering organizations regardless of team size, tech stack, or industry. A sprint ends. Features are shipped. The QA team is still writing automation for the previous sprint. The backlog of unautomated scenarios grows. Leadership asks what it would take to close the gap. The answer comes back: more engineers, more time, more tooling budget. ‍ ‍ 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.