 Web App Load Testing Using Maven Plugins for Apache JMeter, and Analyzing the Results Dec 2nd 2025, 21:00 by Vincent DABURON In this article, we will walk you through how to conduct a load test and analyze the results using Java Maven technology. We'll covering everything from launching the test to generating informative graphs and tables. For this demonstration, we'll utilize various files, including Project Object Model (POM) files, JMeters scripts, and CSV data, from the jpetstore_loadtesting_dzone project available on GitHub. This will help illustrate the steps involved and the functionality of the necessary plugins and tools. You can find the project here: https://github.com/vdaburon/jpetstore_loadtesting_dzone. |  Apache Phoenix With Variable-Length Encoded Data Dec 2nd 2025, 19:00 by Viraj Jasani Apache Phoenix is an open-source, SQL skin over Apache HBase that enables lightning-fast OLTP (Online Transactional Processing) operations on petabytes of data using standard SQL queries. Phoenix helps combine the scalability of NoSQL with the familiarity and power of SQL. By supporting large-scale aggregate and non-aggregate functionality, Phoenix has evolved into an OLTP and OLAP (Online Analytical Processing) database. This makes it a compelling choice for organizations looking to combine real-time data processing with complex analytical querying in a single, unified system. Phoenix supports several variable-length data types: |  JDK 17 Memory Bloat in Containers: A Post-Mortem Dec 2nd 2025, 18:00 by saumya tyagi When engineering teams modernize Java applications, the shift from JDK 8 to newer Long-Term Support (LTS) versions, such as JDK 11, 17, and soon 21, might seem straightforward at first. Since Java maintains backward compatibility, it's easy to assume that the runtime behavior will remain largely unchanged. However, that's far from reality. In 2025, our team completed a major modernization initiative to migrate all of our Java microservices from JDK 8 to JDK 17. The development and QA phases went smoothly, with no major issues arising. But within hours of deploying to production, we faced a complete system breakdown. |  Architecting Cloud Data Migration From Legacy Warehouses Dec 2nd 2025, 17:00 by Swamy Ramajayam The Legacy Challenge in Enterprise Data For decades, enterprise data platforms were built on Teradata, Oracle, and other legacy systems. They were once the backbone of analytics, providing reliability and scale, but over time, they became rigid, costly, and difficult to evolve. Today, many of these platforms hold petabytes of data, support thousands of reports, and sit at the center of hundreds of dependent processes. What was once an enabler has become a bottleneck. The challenge is not just technology. Over the years, enterprises accumulate thousands of stored procedures, ETL pipelines, and reporting scripts embedded into these systems. Business rules and definitions are often hard-coded into SQL, reporting layers, or application logic. Migration to the cloud cannot be treated as a simple copy-and-paste job. Without a deliberate strategy, companies risk recreating the inefficiencies and inconsistencies of the past on a modern platform. |  Beyond Buzzwords: Demystifying Agentic AI Dec 2nd 2025, 16:00 by Pradyumna Amasebail Kodgi AI discussions today are filled with buzzwords — autonomy, orchestration, reasoning, context-awareness, and more. These terms often get used loosely, yet they are central to understanding the shift toward agentic AI. In this article, I'll unpack the most common buzzwords tied to AI agents, explain what they really mean, and show how they come together to shape agentic AI. What Are Agents Anyway? First, let us understand what an agent is. Why does everyone want to build an agent? | |
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