News Aggregator

Scaling AI Workloads in Java Without Breaking Your APIs

Aggregated on: 2026-03-27 20:08:11

As AI inference moves from prototype to production, Java services must handle high-concurrency workloads without disrupting existing APIs. This article examines patterns for scaling AI model serving in Java while preserving API contracts. Here, we compare synchronous and asynchronous approaches, including modern virtual threads and reactive streams, and discuss when to use in-process JNI/FFM calls versus network calls, gRPC/REST. We also present concrete guidelines for API versioning, timeouts, circuit breakers, bulkheads, rate limiting, graceful degradation, and observability using tools like Resilience4j, Micrometer, and OpenTelemetry. Detailed Java code examples illustrate each pattern from a blocking wrapper with a thread pool and queue to a non-blocking implementation using CompletableFuture and virtual threads to a Reactor-based example. We also show a gRPC client/server stub, a batching implementation, Resilience4j integration, and Micrometer/OpenTelemetry instrumentation, as well as performance considerations and deployment best practices. Finally, we offer a benchmarking strategy and a migration checklist with anti-patterns to avoid.

The Hidden Cost of Flaky Tests in Test Automation

Aggregated on: 2026-03-27 19:08:11

A test result on the CI pipeline fails. A developer runs the process again, and the test passes. No changes to the code had been made. This happens frequently enough that many teams have experienced it before. When this occurs, it is treated as a common occurrence, and after a while, it becomes routine. As a result, automatic builds get rerun on failure, and only if they fail again do they receive any follow-up attention. Ultimately, the CI pipeline cannot be relied upon as a safe environment; instead, it becomes ambient background noise. Flaky tests are not just a nuisance. When flaky tests become frequent occurrences in CI processes, they undermine the team’s confidence, create inefficiencies within the development team, and introduce hidden costs that typically go unaccounted for.

The Hidden Cost of Legacy Infrastructure in Asset-Heavy Game Development

Aggregated on: 2026-03-27 19:08:11

Game developers spend endless engineering hours optimizing shaders, draw calls, and memory footprints. Solutions for runtime performance have advanced dramatically over the last decade, but the production pipeline hasn't kept pace. While engines like UE5 have revolutionized what we see on screen, the “pipes”, the version control systems (VCS) we use, have remained virtually static for over 20 years. This has created a pipeline performance plateau. Today, the most critical bottleneck for a studio is no longer at the runtime level of the CPU or GPU; it is in the development pipeline through the operational drag of legacy version control. The Obvious Choice is Git - or Is It? For a long time, the consensus was that for standard software engineering, Git is THE go-to tool. Its decentralized nature and branch-based workflows changed how we work and enabled parallel development. But the narrative is shifting. As we move into the agentic era, Git’s decentralized architecture is becoming a fundamental bottleneck for everyone, not just game developers.

Designing High-Concurrency Databricks Workloads Without Performance Degradation

Aggregated on: 2026-03-27 18:08:11

High concurrency in Databricks means many jobs or queries running in parallel, accessing the same data. Delta Lake provides ACID transactions and snapshot isolation, but without care, concurrent writes can conflict and waste compute. Optimizing the Delta table layout and Databricks' settings lets engineers keep performance stable under load. Key strategies include:

Why Good Models Fail After Deployment

Aggregated on: 2026-03-27 17:08:11

Six months ago, your recommendation model looked perfect. It hit 95% accuracy on the test set, passed cross-validation with strong scores, and the A/B test showed a 3% lift in engagement. The team celebrated and deployed with confidence. Today, that model is failing. Click-through rates have declined steadily. Users are complaining. The monitoring dashboards show no errors or crashes, but something has broken. The model that performed so well during development is struggling in production, and the decline was unexpected.

The Self-Healing Endpoint: Why Automation Alone No Longer Cuts It

Aggregated on: 2026-03-27 16:53:11

Most organizations have poured heavy capital into endpoint automation. That investment has yielded partial results at best. IT teams frequently find themselves trapped maintaining the very scripts designed to save them time. Recent data from the Automox 2026 State of Endpoint Management report reveals that only 6% of organizations consider themselves fully automated. Meanwhile, 57% operate as partially automated using custom workflows.

Engineering High-Performance Real-Time Leaderboard

Aggregated on: 2026-03-27 16:08:11

Leaderboard performance problems rarely announce themselves as “data structure issues.” They surface instead as CPU spikes, tail-latency explosions, and on-call alerts that refuse to quiet down. That’s exactly what we encountered: a slice-based leaderboard implementation that initially appeared perfectly reasonable, but began to collapse once the system surpassed the 10,000-user mark and update workloads started behaving like O(N²). This article walks through what broke, how profiling made the root cause undeniable, and how the issue was fixed by replacing the original approach with an indexed skip list augmented with span counters and a hash-based identity layer. The redesign reduced critical operations to O(log N), stabilized memory usage, and pushed update latency below one millisecond.

Essential Techniques for Production Vector Search Systems, Part 5: Reranking

Aggregated on: 2026-03-27 15:53:11

After implementing vector search systems at multiple companies, I wanted to document efficient techniques that can be very helpful for successful production deployments. I want to present these techniques by showcasing when to apply each one, how they complement each other, and the trade-offs they introduce. This will be a multi-part series that introduces all of the techniques one by one in each article. I have also included code snippets to quickly test each technique.

Designing Stop Loss in Modern AI-Driven Automated Trading Systems

Aggregated on: 2026-03-27 15:08:11

From Rule-Based Algos to AI-Based Decision Systems A decade ago, many electronic trading strategies were still mostly rule-based. You could often explain the logic in a few sentences. The systems were automated, but the decision rules were transparent and easy for humans to reason about. Modern quantitative desks increasingly lean on machine learning and deep learning — and if we want to be a bit buzzwordy, we can call these AI-based trading systems. Models ingest high-dimensional order book data, news, and alternative data, and decisions are made by gradient-boosted trees, deep networks, or ensembles rather than hand-coded heuristics.

Stop Leap-Second AI Drift in IoT Streams With PySpark

Aggregated on: 2026-03-27 14:53:11

Fintech and Enterprise platforms ingest massive volumes of timestamped data (big data) from IoT devices such as payment terminals, wearables, and mobile apps. Accurate timing is essential for fraud detection, risk scoring, and customer analytics. Yet a subtle irregularity called the leap second can corrupt timestamps and trigger AI drift, gradually degrading model performance in production. In this article, I will attempt to explain clearly what drift types are and how they can be prevented, based on my research paper. Details can be found here. Let's start.

Engineering Capacity Plans for Load-Shedding in High-Demand Enterprise Apps

Aggregated on: 2026-03-27 14:08:11

Large-scale enterprise applications typically have many microservices that are deployed across numerous cloud providers and various geographic locations. When running a high-demand period (i.e., during peak campaigns), the most significant engineering challenge faced by large-scale enterprise applications is not "slow down," but rather: "be correct when correctness matters," "be gracious when correctness doesn't matter," and "recover reliably and predictably." Below, I present a practical approach to capacity planning and demonstrated load-shedding patterns for large-scale enterprise applications based on historical campaign behavior, with examples and illustrations of actual data points from previous campaigns.

From Stream to Strategy: How TOON Enhances Real-Time Kafka Processing for AI

Aggregated on: 2026-03-27 13:53:11

AI agents now increasingly require real-time stream data processing as the environment involving the decision-making is dynamic, fast-changing, and event-driven. Unlike batch processing, which is how traditional data warehouses and BI tools work, real-time streaming enables AI agents to analyze events as they happen, responding instantly to fraud, system anomalies, customer behavior shifts, or operational changes. In competitive and automated environments, a matter of seconds can make the difference between an accurate decision and one that is off by miles, a risk not many organizations are willing to take. Continuous data streams are also key to enabling AI agents to adjust and adapt to emerging patterns, observe trends in real time, and refine predictions on the fly rather than making decisions based on stale snapshots. As with other automation systems that rely on increasingly intelligent agents (usually AI/ML) over time, real-time stream processing ensures that AIs remain responsive and context-aware, enabling them to make timely, high-impact decisions.

DNS Propagation Doesn't Have to Take 24 Hours

Aggregated on: 2026-03-27 13:08:12

You’ve probably been there. You update an A record in your DNS dashboard, then refresh your browser three times in a row. Nothing. Still showing the old server. Then someone in a different timezone messages you saying they can see the new version. But you can’t. You check again. Still nothing.

Secure Managed File Transfer vs APIs in Cloud Services

Aggregated on: 2026-03-27 12:53:11

Data transfer has become one of the most important — and sometimes misunderstood — parts of system architecture as businesses migrate more of their work to the cloud. Secure managed file transfer (MFT) is the main way most teams handle files and batch-oriented data. APIs are used for real-time communication between services. When companies try to utilize one instead of the other, problems arise. For example, they stretch APIs to accommodate huge file transfers or force file-based processes into real-time workflows that they were never meant to support. These misalignments often cause problems with dependability, security, and compliance, complicate operations, and make it take longer to find and fix problems. This essay talks about how secure MFT and APIs meet very distinct purposes, how they stack up against real-world business demands, and why a hybrid design is the best and safest way to build modern cloud applications.

Understanding Dropped Updates in Feed Generation Systems in Modern Applications

Aggregated on: 2026-03-27 12:08:11

Every day, we interact with feed generation systems across various applications. From scrolling through social media updates on Facebook, Instagram, or Twitter to browsing recommendations on Netflix, YouTube, or news aggregator apps, all these platforms rely on feed generation to serve up content tailored to our interests. This article explores what feed generation systems are, how they work (both in ingesting content and delivering it), and why maintaining a high-quality feed matters for user engagement.

Taming the JVM Latency Monster

Aggregated on: 2026-03-26 20:23:11

An Architect's Guide to 100GB+ Heaps in the Era of Agency In the "Chat Phase" of AI, we could afford a few seconds of lag while a model hallucinated a response. But as we transition into the Integration Renaissance — an era defined by autonomous agents that must Plan -> Execute -> Reflect — latency is no longer just a performance metric; it is a governance failure. When your autonomous agent mesh is responsible for settling a €5M intercompany invoice or triggering a supply chain move, a multi-second "Stop-the-World" (STW) garbage collection (GC) pause doesn't just slow down the application; it breaks the deterministic orchestration required for enterprise trust. For an integrator operating on modern Java virtual machines (JVMs), the challenge is clear: how do we manage mountains of data without the latency spikes that torpedo agentic workflows? The answer lies in the current triumvirate of advanced OpenJDK garbage collectors: G1, Shenandoah, and ZGC.

Automating Maven Dependency Upgrades Using AI

Aggregated on: 2026-03-26 19:23:11

Enterprise Java applications do not often break due to business logic. The reason they break is that dependency ecosystems evolve all the time. Manual maintenance in most large systems consists of hundreds of third-party libraries, and small upgrades occur regularly as a result of security patches, code corrections, or vendor advice. The problem is not recognizing outdated libraries. Tools such as OWASP Dependency-Check, Snyk, and Black Duck already do it well. The problem is a wastage of the developer's time in repetitive actions: checking Maven Central for the latest versions, validating whether the upgrade is safe, reading release notes, guessing what test cases should be executed, and raising a pull request with meaningful documentation.

MinIO AIStor and Ampere® Computing Reference Architecture for High-Performance AI Inference

Aggregated on: 2026-03-26 18:38:11

MinIO AIStor is a highly scalable, high-performance object storage solution tailored for AI workloads, especially in distributed or cloud-native environments. Designing a cluster for AI inference requires high-performance storage and efficient data retrieval. Thus, careful consideration must be given to storage architecture, compute resources, networking, and scalability. MinIO, Ampere®, Supermicro®, and Micron Technology Inc have partnered to deliver and validate performance through comprehensive testing on an Ampere® Altra® 128 core-powered storage cluster consisting of eight nodes to allow for: Scalability: Horizontal scaling of storage and I/O performance. Redundancy: Built-in erasure coding ensures data durability even if nodes or drives fail. High throughput: Parallel access and distributed storage enable fast read/write operations, suitable for AI or big data analytics. Kubernetes and bare-metal friendly: Can be deployed both on Kubernetes or as standalone Bare-metal nodes. The Ampere® Altra® family of processors provides predictable, consistent high performance under maximum load conditions. This is achieved through single-threaded compute cores, consistent operating frequency, and high core counts per socket. As a result, customers benefit from exceptional performance per rack, per watt, and per dollar.

Microsoft Responsible AI Principles Explained for Engineers

Aggregated on: 2026-03-26 18:23:11

How to Turn Responsible AI Principles into Real, Enforceable Systems Industry leaders in the tech industry are moving forward with artificial intelligence in all areas. Relatively, AI systems started to influence healthcare, insurance claims, hiring, credit scoring, fraud detection, and customer interactions by making decisions in respective areas. These are all the domains where decisions made by the AI system are very critical, though if mistakes happen, it will not be considered only as technical bugs, but it can lead to real-world harm, regulatory violations, and loss of trust in the system. Microsoft defines a set of responsible AI principles to guide the development and deployment of AI systems. These responsible AI principles help to reduce the mistakes made by the AI system. These principles provide a strong ethical and governance foundation. However, many engineering teams struggle with a critical gap.

Isolation Boundaries in Multi-Tenant AI Systems: Architecture Is the Only Real Guardrail

Aggregated on: 2026-03-26 17:23:11

Multi-tenant AI systems operate and fail differently from single-tenant traditional software. These systems don’t usually fail because of bypassed authentication; they usually fail because the system quietly allowed tenants to share something they shouldn’t have, such as execution paths, configuration state, retry pressure, or storage namespaces. In most single-tenant software, a single mistake usually affects only one customer, whereas in multi-tenant AI platforms, that same mistake can propagate sideways before any member of the development or operations team notices. The impact radius is no longer contained by default, unlike in single-tenant software.

Stateful AI: Streaming Long-Term Agent Memory With Amazon Kinesis

Aggregated on: 2026-03-26 16:38:10

As autonomous agents evolve from simple chatbots into complex workflow orchestrators, the “context window” has become the most significant bottleneck in AI engineering. While models like GPT-4o or Claude 3.5 Sonnet offer massive context windows, relying solely on short-term memory is computationally expensive and architecturally fragile. To build truly intelligent systems, we must decouple memory from the model, creating a persistent, streaming state layer. This article explores the architecture of streaming long-term memory (SLTM) using Amazon Kinesis. We will dive deep into how to transform transient agent interactions into a permanent, queryable knowledge base using real-time streaming, vector embeddings, and serverless processing.

When Kubernetes Says "All Green" But Your System Is Already Failing

Aggregated on: 2026-03-26 16:23:11

It's not a theoretical scenario. The cluster health checks all come back "green." Node status shows Ready across the board. Your monitoring stack reports nominal CPU and memory utilization. And somewhere in a utilities namespace, a container has restarted 24,069 times over the past 68 days — every five minutes, quietly, without triggering a single critical alert. That number — 24,069 restarts — came from a real non-production cluster scan run last week, an open-source Kubernetes scanner that operates with read-only permissions — it can see the state of the cluster, but it cannot and did not change a single thing. The failures we found were entirely of the cluster's own making. The namespace it lived in showed green in every dashboard the team monitored. No alert had fired. No ticket had been created. The workload had essentially been broken for over two months, and the cluster's observability layer had communicated exactly nothing about it.

Building Centralized Master Data Hub: Architecture, APIs, and Governance

Aggregated on: 2026-03-26 15:38:11

Many enterprises operating with a large legacy application landscape struggle with fragmented master data. Core entities such as country, location, product, broker, or security are often duplicated across multiple application databases. Over time, this results in data inconsistencies, redundant implementations, and high maintenance costs. This article outlines Master Data Hub (MDH) architecture, inspired by real-world enterprise transformation programs, and explains how to centralize master data using canonical schemas, API-first access, and strong governance.

From 30s to 200ms: Optimizing Multidimensional Time Series Analysis at Scale

Aggregated on: 2026-03-26 15:23:11

Monitoring production systems in real-time is crucial for reliability. Multidimensional anomaly detection is a very helpful tool in this regard. However, it does require time-series analysis to be blazing fast. This follow-up blog shows how to speed them up by using different strategies like indexing, filtering, bucketing, etc., to achieve a consistent performance in the 100s of ms range. Recap Most teams learn the hard way that global all-green dashboards can hide real incidents in a single cohort. In Part 1: A Guide to Multidimensional Anomaly Detection, we covered the why and the solution blueprint.

MCP vs Skills vs Agents With Scripts: Which One Should You Pick?

Aggregated on: 2026-03-26 14:38:10

I have been writing and building in the AI space for a while now. From writing about MCP when Anthropic first announced it in late 2024 to publishing a three-part series on AI infrastructure for agents and LLMs on DZone, one question keeps coming up in comments, DMs, and community calls: What is the right tool for the job when building with AI? For a long time, the answer felt obvious. You pick an agent framework, write some Python, and ship it. But the ecosystem has moved fast. We now have MCP servers connecting AI to the real world, Skills encoding domain know-how as simple markdown files, and agent scripts that can orchestrate entire workflows end to end. The options are better than ever. The confusion around them is too.

Document Generation API: How to Automate Personalized Document Creation at Scale

Aggregated on: 2026-03-26 14:38:10

Every company has the same hidden bottleneck: someone, somewhere, is manually building documents. They pull a client’s name from the CRM, paste it into a Word template, double-check the date, adjust the logo placement, and export to PDF. On a good day, that’s an intern handling a manageable workload. On a bad day, it’s an engineer who wired the entire layout into iText or PDFKit, and now Marketing needs the font changed across every document type. Both approaches share the same problem: they don’t scale. They’re manual workarounds dressed up as processes, and they collapse the moment volume jumps from a few hundred records to 50,000 invoices that need to ship overnight. Legacy Mail Merge tools hit the same wall.

Why RAG Alone Isn’t Enough: How MCP Completes the Agentforce Intelligence Stack?

Aggregated on: 2026-03-26 14:23:10

Retrieval-augmented generation (RAG) has emerged as one of the key building blocks for AI-based systems in recent years. RAG takes a language model and mixes it with external knowledge access. In short, it permits a system to extract useful information from big data sources and provide context-aware responses. On the surface, that may seem fantastic for smart agents, AI assistants, and question-answering systems. RAG can produce relevant information at scale and without needing to retrain the underlying model, generalizing across many domains. But in actual enterprise applications, constraints begin to appear. RAG is strong at fetching documents or data snippets and incorporating them into generated responses, but it has weaknesses in structured reasoning, long-horizon planning, and tool use. For machines that are required to access multiple systems, carry out stepwise operations, or undertake complex workflows, RAG alone is not enough. Models can hallucinate steps, misunderstand instructions, or fail to recognize dependencies between tools.

Bringing AI Agents to Cloud Engineering: How Autonomous Operations Are Changing Reliability at Scale

Aggregated on: 2026-03-26 13:23:10

Modern cloud systems are getting harder to manage. That is not a new observation, but the gap between system complexity and human response is growing faster than most teams expect. Microservices run across regions, deployments happen constantly, and workloads change without warning. Even well-staffed operations teams struggle to keep up. Traditional automation helps, but only to a point. Scripts, alerts, and scheduled jobs work when failure patterns are known in advance. They break down when incidents are unclear, cross multiple services, or do not match existing rules. In practice, many incidents still rely on human judgment, context switching, and experience under pressure.

Data Driven API Testing in Java With REST Assured and TestNG: Part 4

Aggregated on: 2026-03-26 12:23:10

APIs are at the heart of almost every application, and even small issues can have a big impact. Data-driven API testing with JSON files using REST Assured and TestNG makes it easier to validate multiple scenarios without rewriting the same tests again and again. By separating test logic from test data, we can build cleaner, flexible, and more scalable automation suites. In this article, we’ll walk through a practical, beginner-friendly approach to writing API automation tests with REST Assured and TestNG using JSON files as the data provider.

Stop Writing Slow Pandas Code: Vectorization and Modern Alternatives Explained

Aggregated on: 2026-03-25 20:08:10

Pandas performance problems rarely look catastrophic. They appear as pipelines that take four hours instead of twenty minutes, jobs that time out on datasets they handled comfortably six months ago, and transformation steps that become the silent bottleneck in an otherwise reasonable architecture. The code looks correct. It is just slow. The cause is almost always the same: Python-level row iteration where vectorized column operations belong, or datasets that have grown large enough that single-threaded execution is the real constraint. Both are fixable. This article covers the specific patterns that cause most Pandas slowdowns, with benchmark numbers and the modern alternatives, Polars and DuckDB, for when Pandas itself is not the right tool.

Production Database Migration or Modernization: A Comprehensive Planning Guide [Part 1]

Aggregated on: 2026-03-25 18:08:10

Migrating a production database that supports critical backend API services is one of the most challenging undertakings in software engineering. Whether you're modernizing from a legacy relational database to a NoSQL database like MongoDB, moving to a cloud-native solution like Azure Cosmos DB or AWS DynamoDB, or simply upgrading your database to a newer version, the stakes are high. A poorly executed migration can result in data loss, extended downtime, revenue impact, and erosion of customer trust — not to mention frustration among internal stakeholders! Commonly, migration timelines extend 4–6x longer than originally anticipated due to poor preparation, planning, and internal coordination. This extension drives up not only costs but also uncertainty and risk for other projects impacted by the migration.

Beyond “Lift-and-Shift”: How AI and GenAI Are Automating Complex Logic Conversion

Aggregated on: 2026-03-25 17:23:10

Image Source: Houston SEO Directory on Unsplash (For Illustrative purposes only) For the past decade, the promise of the cloud has been a siren song for enterprises trapped by the gravity of their legacy data warehouses. The initial, tempting path was “lift-and-shift”: move the applications and data, as-is, to a cloud VM. The industry has since learned a hard lesson.

AI Agents vs LLMs: Choosing the Right Tool for AI Tasks

Aggregated on: 2026-03-25 16:23:10

Large language models have changed how software teams think about automation, reasoning, and intelligence. Almost overnight, tasks that once required brittle rules or custom ML pipelines became promptable. But as adoption has grown, so has confusion. Teams now ask a new question that did not exist a few years ago: should we use a large language model directly, or should we build an AI agent around it? This distinction matters more than it seems. I have seen teams over-engineer agentic systems for problems that only needed a single LLM call. I have also seen teams struggle with fragile prompt chains when what they really needed was planning, memory, and tool orchestration.

Tokens and Transactions With AI

Aggregated on: 2026-03-25 15:53:10

Based on NVIDIA CEO Jesen Huang’s commentary on the Role of Databases for the Agentic Era in his GTC 2026 keynote. The diagram below is a readable version of Jensen's "Best Slide"; the content is created using LLM from the talk's transcript and then edited. Summary of the Talk [wrt Databases] For a database audience, the keynote underscores a fundamental shift: Data is no longer just stored and queried — it is continuously activated to power agentic systems. The talk highlights that the center of gravity is moving from traditional transactional and analytical databases toward AI-driven data platforms that unify structured, unstructured, and real-time data streams into a single operational fabric. Massive growth in AI infrastructure — driven by data center expansion and trillion-dollar-scale compute demand — signals that data systems must scale not just for queries, but for continuous inference and agent workflows.

Privacy-Conscious AI Development: How to Ship Faster Without Leaking Your Crown Jewels

Aggregated on: 2026-03-25 15:23:10

AI-assisted development is accelerating software delivery — but it also amplifies a question many teams still ignore: what happens to your sensitive data when you use AI tools? API keys, customer PII, internal business logic, production logs — once shared with third-party AI services, you may lose control over where that data is stored, who can access it, and how it’s used. Even with reputable providers, data may be logged or cached outside your visibility; support teams may access snippets; and content may be used to improve models unless you explicitly opt out. The result is elevated compliance risk (e.g., GDPR/CCPA) and potential competitive exposure if proprietary logic becomes training data.

Data-Driven API Testing in Java With REST Assured and TestNG: Part 3

Aggregated on: 2026-03-25 14:53:10

Data-driven testing enables testers to execute the same test logic with multiple sets of input data, improving coverage and reliability with minimal effort. By combining CSV files with TestNG’s @DataProvider annotation, test data can be easily separated from the test logic. This approach enables maintainability and makes test automation more scalable and flexible. This article explains how to implement data-driven testing with CSV files and TestNG in a clear, practical, and easy-to-follow manner.

Retries Will Bankrupt You Before Any Attacker Gets the Chance

Aggregated on: 2026-03-25 14:23:10

I've watched a $40,000 AWS bill materialize in a weekend. No breach, no botnet, no disgruntled ex-employee with root access. Just a misconfigured retry policy on a Lambda-backed payment processor that hit a flaky downstream vendor API during a Saturday night deployment. Every timeout spawned three children. Each child could time out too. That’s the thing nobody tells you when they hand you the Polly documentation and say, “Add resilience.” Resilience, implemented carelessly, is just a different failure mode with a credit card attached.

Operationalizing Agentic AI in Enterprises: A Problem-Constraints-Tradeoffs Case

Aggregated on: 2026-03-25 13:38:10

Editor’s Note: The following is an article written for and published in DZone’s 2026 Trend Report, Generative AI: From Prototypes to Production, Operationalizing AI at Scale. Our problem did not show up as a lack of intelligence. It appeared as instability.

Mastering Serverless Architecture: Event-Driven Design with Azure Functions and Cosmos DB

Aggregated on: 2026-03-25 13:23:10

The landscape of modern software engineering has shifted dramatically from monolithic, stateful applications toward decoupled, event-driven architectures. At the forefront of this evolution is the combination of Azure Functions and Azure Cosmos DB. This powerful duo enables developers to build systems that are massively scalable, cost-effective, and resilient. In this article, we take a deep dive into the technical intricacies of building end-to-end event-driven systems. We explore the mechanics of the Cosmos DB Change Feed, architectural design patterns such as CQRS and Materialized Views, and practical implementation strategies for production-grade serverless applications.

Swift: Master of Decoding Messy JSON

Aggregated on: 2026-03-25 12:23:10

I recently came across an interesting challenge involving JSON decoding in Swift. Like many developers, when faced with a large, complex JSON response, my first instinct was to reach for “quick fix” tools. I wanted to see how online resources, various JSON-to-Swift converters, and even modern AI models would handle a messy, repetitive data structure. To be honest, I was completely underwhelmed.

Agent-of-Agents Pattern: Enhancing Software Testing

Aggregated on: 2026-03-24 20:08:10

The Pre-Production Bottleneck A pull request (PR) gets merged, code review is complete, unit tests are green, and the feature looks good. But then comes the familiar question: Is this actually ready for production? Most engineering teams have a checklist: regression tests, security scans, performance validation, and integration checks. The problem is that executing all of this takes significant time. A full regression suite might take one to two hours. For a feature that touched a few files, running everything feels wasteful. But manually picking tests? That's how bugs slip into production.

Building Scalable Agentic Assistants: A Graph-Based Approach

Aggregated on: 2026-03-24 19:08:10

About a year ago, we were drawn into what appeared to be a straightforward problem: building an interface assistant that could answer questions about payments, disputes, refunds, transactions, and a few other sub-domains and provide insights. The reality turned out far more complex. Many teams already had multiple apis, data sources, internal tools, and domain experts collaborating. What we didn't have was a way to wire all this together into something that felt coherent, reliable, and scalable. Early experiments with single-agent chatbots worked for demos, but they collapsed under real organizational complexity. We needed to stop thinking in terms of agentic systems and start treating it as a coordinated system of agents, each with a narrow responsibility.

Robust Network Layer in Swift via Clean Architecture Approach

Aggregated on: 2026-03-24 18:08:10

Networking is the backbone of almost every modern iOS application. However, as projects grow in complexity, the network layer often becomes a “junk drawer” for URL construction, messy completion handlers, and scattered error logic. This tight coupling makes unit testing difficult and maintenance a nightmare. In this article, we are going to build a reusable, testable, and type-safe Network Layer from scratch. By leveraging Clean Architecture principles, the power of Swift Generics, and the modern elegance of Async/Await, we will create a solution that separates concerns and scales with your app.

Data-Driven API Testing in Java With REST Assured and TestNG: Part 2

Aggregated on: 2026-03-24 17:08:09

In the previous article, we explored how to implement data-driven testing using Object arrays and TestNG’s @DataProvider annotation. While this approach works well for small to medium-sized datasets, it is not ideal for handling large volumes of data. To address this limitation, TestNG also supports the use of Iterators, which provide a more efficient way to manage large and dynamic datasets. This article focuses on how to perform data-driven API automation testing using an Iterator with a DataProvider annotation of TestNG.

MariaDB Doesn't Depend on MySQL

Aggregated on: 2026-03-24 16:08:09

When MariaDB was first announced in 2009 by Michael “Monty” Widenius, it was positioned as a “fork of MySQL”. I think that was a Bad Idea™. Okay, maybe it wasn’t a bad idea as such. After all, MariaDB indeed is a fork of MySQL. But what is a fork in the software sense, and how is this reflected in MariaDB? A fork is a software project that takes the source code of another project and continues development independently from the original. Forks often start by maintaining compatibility with their parent project, but they can evolve to become detached from their own features, architecture, bug tracker, mailing list, development philosophy, and community. This is the case of MariaDB, with the addition that it continues to be highly compatible with old MySQL versions and with its current ecosystem at large.

The Phantom Write Problem: Why Your Idempotency Implementation Is Silently Losing Data

Aggregated on: 2026-03-24 15:08:09

Idempotency implementations commonly pass unit tests yet silently corrupt data in production due to four failure modes — including failure modes that manifest as "phantom writes" — a pattern previously undocumented as a unified class of idempotency failures. This article identifies these patterns based on debugging 12 production incidents and introduces the Idempotency Barrier pattern, a unified approach combining transactional state machines, atomic claiming, and boundary-aware key propagation. After deployment across three financial platforms, the pattern eliminated 99.98% of duplicate payment incidents and reduced monthly reconciliation costs by over $220,000. Disclosure: This research stems from debugging production incidents across multiple high-scale payment and order fulfillment platforms between 2023 and 2025. Company-specific details have been anonymized.

Understanding SHORTUSR/USRFIELDS in AUTHINFO to Meet 12-Character Identity Limits for MQ on Windows

Aggregated on: 2026-03-24 14:08:10

Introduction: Modern Directories Meet Legacy Constraints As organisations strengthen security and centralise identity management, IBM MQ administrators increasingly integrate with enterprise LDAP directories such as Microsoft Active Directory or OpenLDAP. This enables authentication using corporate credentials and authorisation through LDAP users or their group membership, instead of relying on local OS users. However, on Windows platforms, IBM MQ still enforces a long‑standing 12‑character limit on the user ID used for authorisation. This limitation does not come from LDAP; it originates from how MQ maps authenticated identities to Windows principals for Object Authority Manager (OAM) checks. IBM MQ’s Object Authority Manager was designed to work uniformly across Windows, UNIX (AIX/Linux), and z/OS, where OS usernames traditionally max out at 12 characters.

Imprisoning the Panic

Aggregated on: 2026-03-24 13:08:09

This single line of code recently made a significant portion of the Internet unavailable throughout the world. Rust let (feature_values, _) = features.append_with_names(&self.config.feature_names).unwrap();

Building an Agentic AIOps Pipeline With IBM Storage Insights, n8n, and Elastic

Aggregated on: 2026-03-24 12:08:10

IBM Storage Insights is a cloud-based storage monitoring and analytics platform designed to provide visibility across enterprise storage environments. It continuously collects telemetry from storage systems, analyzes capacity and performance trends, detects risks, and generates alerts when thresholds or anomalies are detected. These alerts can range from capacity and performance issues to configuration, security, and hardware health notifications. In large environments, Storage Insights becomes a critical early-warning system — but it can also generate a high volume of alerts that require triage, investigation, and remediation. Some are critical and demand immediate attention, many are informational, and a surprising number are duplicates of issues that have already been investigated and resolved. Over time, this creates a familiar problem for operations teams: alert fatigue. Engineers spend more time triaging notifications than solving real problems, and valuable context is scattered across dashboards, chat threads, and ticketing systems.

Beyond Reactive HPA: Designing a Predictive Autoscaler with KEDA and Time-Series Forecasting

Aggregated on: 2026-03-23 20:23:09

Kubernetes scaling relies predominantly on the Horizontal Pod Autoscaler (HPA), a robust feedback loop that adjusts capacity based on observed metric saturation. While reliable for steady-state traffic, HPA is inherently reactive, it mitigates resource exhaustion only after it has begun. For workloads with steep, predictable traffic ramps (such as morning log-in spikes or scheduled synchronization jobs), this reactive lag guarantees a period of transient performance degradation. To achieve strict Service Level Objectives (SLOs) during these ramps, infrastructure must shift from reacting to current load to anticipating future demand. This article details a feed-forward architecture using time-series forecasting (Prophet) and Kubernetes Event-Driven Autoscaling (KEDA) to provision capacity before the demand arrives.