News Aggregator

Iceberg Compaction and Fine-Grained Access Control: Performance Challenges and Solutions

Aggregated on: 2025-11-19 20:11:09

Modern data lakes increasingly rely on Apache Iceberg for managing large analytical datasets, while organizations simultaneously demand fine-grained access control (FGAC) to secure sensitive data. However, combining these technologies can create unexpected performance bottlenecks that significantly impact query execution times. This article explores the technical challenges that arise when implementing FGAC on Iceberg tables and provides practical guidance for choosing the right processing engine for your use case. Understanding Iceberg Compaction Apache Iceberg is an open table format designed for huge analytical datasets. One of its core features is compaction — the process of combining smaller data files into larger, more efficient ones to optimize query performance and reduce metadata overhead.

Zero Trust in API Gateways: Building Bulletproof Infrastructure With Istio and OPA

Aggregated on: 2025-11-19 19:26:09

APIs: The New Battlefield Every API endpoint is a doorway. Some lead to treasure vaults. Others? Straight into disaster. I've spent the last five years watching enterprises get blindsided by API attacks they never saw coming. Payment processors are losing millions through lateral movement. SaaS platforms are hemorrhaging customer data via misconfigured gateways. E-commerce giants are getting their product catalogs scraped by sophisticated bots.

Good CI Is the Key to a Great Developer Onboarding Experience

Aggregated on: 2025-11-19 18:26:09

I have spent the last 20 years working with large enterprises. First, as a developer myself, and later, helping teams across different stages of their journey. One of the biggest challenges for any engineer, whether new or experienced, is becoming productive quickly. For technical employees, especially those hired to write code, nothing builds confidence faster than being able to make a change to an existing repository and immediately know whether that change is good before it gets merged. A good CI system should automate every step and execute quickly to provide clear signals that definitively answer the question: “Is this change good enough to merge?”

DPDK Cryptography Build and Tuning Guide

Aggregated on: 2025-11-19 17:26:09

One of the many use cases customers run on Ampere-powered systems is packet processing workloads built on DPDK. Ampere has published a setup and tuning guide for DPDK to assist customers with getting the best performance from these workloads. Since many customers make heavy use of encryption/decryption operations in their DPDK applications, we are supplementing the existing DPDK tuning guide with additional information on crypto library support and how to build DPDK with these crypto libraries. Note: These steps should happen before building the DPDK library.

Why Your Architecture Team Is Slow (And It's Not the Technology)

Aggregated on: 2025-11-19 16:26:09

Last Tuesday, I watched a senior architect spend forty-five minutes presenting a technically flawless case for migrating from REST to GraphQL. Beautiful diagrams. Solid reasoning. Compelling data. The team nodded along. And then nothing happened because this was the seventh architectural discussion that the team had scheduled in three weeks. Not seven decisions. Seven discussions.

Creating an End-to-End ML Pipeline With Databricks and MLflow

Aggregated on: 2025-11-19 15:26:09

Within data-centric organizations, creating an end-to-end machine learning (ML) Pipeline that is reproducible, scalable, and traceable is an essential component. The integrated ecosystem of Delta Lake, Auto Loader, and MLflow in Databricks allows organizations to simplify the ML lifecycle from unrefined data ingestion all the way to production deployment. This tutorial provides a comprehensive guide on constructing an end-to-end ML pipeline on Databricks, utilizing MLflow for model tracking and the model registry, and leveraging Delta Lake for data management. We will demonstrate all the tasks in a unified workflow, including raw data ingestion, feature preparation, model training, and prediction serving.

Smart AI Agent Targeting With MCP Tools

Aggregated on: 2025-11-19 14:26:09

Overview Here’s what nobody tells you about multi-agentic systems: the hard part isn’t building them but making them profitable. One misconfigured model serving enterprise features to free users can burn $20K in a weekend. Meanwhile, you’re manually juggling dozens of requirements for different user tiers, regions, and privacy compliance, and each one is a potential failure point. Part 2 of 3 of the series: Chaos to Clarity: Defensible AI Systems That Deliver on Your Goals

Faster Build & Test Loops, Better DevX

Aggregated on: 2025-11-19 13:26:09

I was listening to a podcast recently about building agents, and they mentioned that writing code was never the bottleneck; it was everything around code writing that took the most time. So true, I thought. Of course, there’s much more to delivering software to customers, but I’ve always cringed at how much inefficiency exists in a typical build and test setup, and how little effort teams often put into addressing these bottlenecks. Recently, while testing a new build tool, I noticed that when it worked, it improved my build efficiency by more than 50%. That’s when it struck me how much productivity I had been losing and even more surprisingly, how unaware I’d been of it, simply because I had grown accustomed to those inefficiencies.

Meta Data: How Data about Your Data is Optimal for AI

Aggregated on: 2025-11-19 12:26:09

Introduction All AI models are built on data collected from a wide range of sources, including vast internet repositories. The real challenge is not just gathering this raw information, but extracting its value. Thanks to Data labeling and neural network architectures, significant progress has been made in turning unstructured data into intelligent models. Data is gold. As AI models parse all kinds of data from numerous sources, a model fed with more descriptive data, will perform better. We’ll talk about what metadata is and how this data about data increases AI efficiency.

From Zero to Local AI in 10 Minutes With Ollama + Python

Aggregated on: 2025-11-18 20:11:08

Why Ollama (And Why Now)? If you want production‑like experiments without cloud keys or per‑call fees, Ollama gives you a local‑first developer path: Zero friction: Install once; pull models on demand; everything runs on localhost by default. One API, two runtimes: The same API works for local and (optional) cloud models, so you can start on your laptop and scale later with minimal code changes. Batteries included: Simple CLI (ollama run, ollama pull), a clean REST API, an official Python client, embeddings, and vision support. Repeatability: A Modelfile (think: Dockerfile for models) captures system prompts and parameters so teams get the same behaviour. What’s New in Late 2025 (at a Glance) Cloud models (preview): Run larger models on managed GPUs with the same API surface; develop locally, scale in the cloud without code changes. OpenAI‑compatible endpoints: Point OpenAI SDKs at Ollama (/v1) for easy migration and local testing. Windows desktop app: Official GUI for Windows users; drag‑and‑drop, multimodal inputs, and background service management. Safety/quality updates: Recent safety‑classification models and runtime optimizations (e.g., flash‑attention toggles in select backends) to improve performance. How Ollama Works (Architecture in 90 Seconds) Runtime: A lightweight server listens on localhost:11434 and exposes REST endpoints for chat, generate, and embeddings. Responses stream token‑by‑token. Model format (GGUF): Models are packaged in quantized .gguf binaries for efficient CPU/GPU inference and fast memory‑mapped loading. Inference engine: Built on the llama.cpp family of kernels with GPU offload via Metal (Apple Silicon), CUDA (NVIDIA), and others; choose quantization for your hardware. Configuration: Modelfile pins base model, system prompt, parameters, adapters (LoRA), and optional templates — so your team’s runs are reproducible. Install in 60 Seconds macOS / Windows / Linux 1. Download and install Ollama from the official site (choose your OS).

How to Create a Responsive Filter Component on React Guide

Aggregated on: 2025-11-18 19:11:08

In web development, responsive and user-friendly components have never been more important. One of these is a filter component that enables web users to quickly filter the user interface (UI) and data elements and display only relevant fields. The challenge is creating a filter component that can fit any screen size. This article will demonstrate how to implement a responsive filter component using React. The tutorial explains how developers can build flexible filters into their web applications.

Building Gateway Analytics: My Journey to Making API Traffic Data Useful

Aggregated on: 2025-11-18 18:11:08

APIs are everywhere today. Whether it's buying something online, logging into a mobile app, or streaming a movie, an API is always working behind the scenes. Over the last decade, APIs have become the backbone of modern software systems. As an application scales, the volume of API calls increases rapidly, and managing them becomes more complex. This is where API gateways come into action. An API gateway acts as an entry point for all internal or external API traffic. It sits in front of the backend services and handles responsibilities such as authentications, routing, rate limiting, logging, performance monitoring, and more.

Embedding Ethics Into Multi-AI Agentic Self-Healing Data Pipelines

Aggregated on: 2025-11-18 17:11:08

The race to design a fully autonomous system is fostering innovations in the development of modern data systems. Developers are striving to create data ecosystems that are self-correcting and have minimal downtime so as to manage data movement effectively within their organizations. Due to such a drive for automation, the use of self-healing data pipelines has increased rapidly. A conventional data pipeline consists of data processing elements connected in a relevant manner to move data between two different data systems. For example, extracting data from IoT devices, such as temperature sensors, and loading it into an analytical database for monitoring forms a simple ELT data pipeline. Such traditional pipelines are prone to limitations, including downtime, crashes, low scalability, and excessive monitoring overhead.

NVIDIA GPU Operator Explained: Simplifying GPU Workloads on Kubernetes

Aggregated on: 2025-11-18 16:11:08

While GPUs have long been a staple in industries like gaming, video editing, CAD, and 3D rendering, their role has evolved dramatically over the years. Originally designed to handle graphics-intensive tasks, GPUs have proven to be powerful tools for a wide range of computationally demanding applications. Today, their ability to perform massive parallel processing has made them indispensable in modern fields such as data science, artificial intelligence and machine learning (AI/ML), robotics, cryptocurrency mining, and scientific computing. This shift was catalysed by the introduction of CUDA (Compute Unified Device Architecture) by NVIDIA in 2007, which unlocked the potential of GPUs for general-purpose computing. As a result, GPUs are no longer just graphics accelerators; they’re now at the heart of cutting-edge innovation across industries. In this blog post, we will discuss the NVIDIA GPU operator on Kubernetes and how to deploy it on the Kubernetes Cluster.

Building a Containerized Quarkus API on AWS ECS/Fargate With CDK

Aggregated on: 2025-11-18 15:11:08

In a three-article series published recently on this site (Part 1, Part 2, Part 3), I've been demonstrating the power of the AWS Cloud Development Kit (CDK) in the Infrastructure as Code (IaC) area, especially when coupled with the ubiquitous Java and its supersonic/subatomic cloud-native stack: Quarkus. While focusing on the CDK fundamentals in Java, like Stack and Construct, together with their Quarkus implementations, this series was a bit frugal as far as the infrastructure elements were concerned. Indeed, for the sake of clarity and simplification, the infrastructure used to illustrate how to use the CDK with Java and Quarkus was inherently consensual. Hence, the idea for a new series, of which this article is the first, is a series less concerned with CDK internals and more dedicated to the infrastructure itself.

Aggregation Strategies for Scalable Data Insights: A Technical Perspective

Aggregated on: 2025-11-18 14:56:08

Elasticsearch is a cornerstone of our analytics infrastructure, and mastering its aggregation capabilities is essential for achieving optimal performance and accuracy. This blog explores our experiences comparing three essential Elasticsearch aggregation types: Sampler, Composite, and Terms. We’ll evaluate their strengths, limitations, and ideal use cases to help you make informed decisions. The Power of Aggregation in Elasticsearch Elasticsearch aggregations offer a powerful means of summarizing and analyzing data. They allow us to group documents into buckets based on specific criteria and then perform calculations on those buckets. This is essential for tasks like:

From Symptoms to Solutions: Troubleshooting Java Memory Leaks and OutOfMemoryError

Aggregated on: 2025-11-18 14:56:08

Troubleshooting memory problems, such as memory leaks and OutOfMemoryError, can be an intimidating task even for experienced engineers. In this post, we would like to share simple tips, tools, and tricks so that even a novice engineer can isolate memory problems and resolve them quickly. What Are Common Signs of a Java Memory Leak That Might Lead to OutOfMemoryError? Before your application throws an OutOfMemoryError, it usually gives you a few warning signs. If you catch them early, you can prevent downtime and customer impact. Here’s what you should keep an eye on:

Pinecone vs. Weaviate: The Trade-offs You Only Discover in Production

Aggregated on: 2025-11-18 12:56:08

When we built our first AI-powered semantic search system, choosing a vector database felt straightforward. Pinecone and Weaviate both looked great on paper — fast, scalable, and built for embeddings. We did what any team under time pressure would do: pick the one that promised the least friction. It worked beautifully ... until it didn't. That's when we learned the hard way that the biggest differences between vector databases don't show up in feature lists or benchmarks - they appear only after you've gone live!

Beyond the Vibe: Why AI Coding Workflows Need a Framework

Aggregated on: 2025-11-17 20:11:08

For decades, software development has been a story of evolving methodologies. We moved from the rigid assembly line of Waterfall to the collaborative, iterative cycles of Agile and Scrum. Each shift was driven by a need to better manage complexity. Today, we stand at a similar inflection point. A new, powerful collaborator has joined the team: Artificial Intelligence.

Private AI at Home: A RAG-Powered Secure Chatbot for Everyday Help

Aggregated on: 2025-11-17 19:11:08

Abstract This article explores the design and deployment of a secure, retrieval-augmented generation (RAG)- powered chatbot tailored for family use using Spring AI. By combining Spring AI’s modular orchestration capabilities with a local vector store and embedding models, the chatbot delivers grounded, context-aware responses to everyday queries — ranging from locating personal documents to offering tech guidance. Emphasizing privacy and ease of use, the system ensures that sensitive data remains within the trusted home environment while providing intuitive, voice-enabled assistance. To guarantee full control and data security, the chatbot is built and hosted entirely on personal infrastructure, with models and vector databases running on Linux-based home PCs. Spring AI was chosen for its cross-platform compatibility and seamless integration with JVM-based tooling, making it ideal for reproducible, secure deployments across diverse environments. This project demonstrates how modern AI frameworks can be repurposed to simplify life for non-technical users, offering a blueprint for personalized, secure, and reproducible AI solutions in domestic settings.

From Data Lakes to Intelligence Lakes: Augmenting Apache Iceberg With Generative AI Metadata on AWS

Aggregated on: 2025-11-17 18:11:08

Over the last decade, we've seen data lakes evolve from static storage into dynamic, queryable systems. With Apache Iceberg, engineers gained ACID transactions and schema evolution on Amazon S3. With AWS Glue, metadata management became serverless and automatic.

Kubernetes CNI Drivers

Aggregated on: 2025-11-17 17:11:08

Ever wondered how we just create a pod in Kubernetes and it gets an IP address magically, can communicate with each other, and host nodes without issues? Networking is not that simple, so how does all this magic work? With this article, I attempt to unwrap the mystery and provide an understanding of the inner workings of CNI (Container Network Interface). First, let’s start with the why.

How Agentic AI Enhances API Testing

Aggregated on: 2025-11-17 16:11:08

Many developers have used generative artificial intelligence to create or complete code, but few have leveraged agentic AI. This AI subset can reason and execute complex tasks autonomously. Its high level of independence and unique proactive approach make it ideal for application programming interface (API) testing. Understanding Agentic AI’s Role in API Testing The use of AI in API testing is relatively new, but well-documented. Researchers have demonstrated the effectiveness of large language models (LLMs) in API testing, particularly when a single test case corresponds to multiple values. Experimental results show precision can reach as high as 100% under stringent conditions, but recall falls below 20%. Under relaxed conditions, recall rates approach 90% while maintaining high precision.

Beyond Outages: Building True Resilience After the AWS Outage

Aggregated on: 2025-11-17 15:11:08

When AWS went dark last week, the internet seemed to take a deep breath with it. Streaming services froze, fintech apps stalled, and even smart home devices blinked out. For a few hours, much of the digital world that relied on AWS ran on borrowed patience. It wasn't just an outage; it was a reminder. A reminder that even the most reliable cloud can still have cloudy days (pun intended).

Integrating AWS With Okta for Just-in-Time (JIT) Access: A Practical Guide From the Field

Aggregated on: 2025-11-17 14:11:08

When our engineering team decided to tighten security around AWS access without slowing developers down, we quickly ran into a familiar trade-off — speed vs. control. We had engineers needing quick access to production for debugging, deployments, and performance checks, but long-lived IAM users and static credentials made our compliance team nervous. That’s where Okta-driven Just-in-Time (JIT) access came in. This post walks through how we set up AWS + Okta integration to give developers on-demand, time-bound access to AWS using SAML federation and Okta Workflows. I’ll share exactly what worked, what didn’t, and what we learned while making it production-ready.

From Agent AI to Agentic AI: Building Self-Healing Kubernetes Clusters That Learn

Aggregated on: 2025-11-17 13:11:08

In Part 1: AI-Driven Kubernetes Diagnostics, we built an AI agent that analyzes Kubernetes pod failures and suggests fixes. It works when you're at your desk, ready to approve each action. For the purposes of this article, here's a scenario.

The Software Architect's Mandate: Treating ChatGPT as a System, Not a Search Engine

Aggregated on: 2025-11-17 12:11:08

As a software architect, I've spent years designing systems that transform inputs into optimal outputs. When I first encountered ChatGPT, I recognized a familiar pattern: most users were treating it like a poorly designed API—sending malformed requests and wondering why the responses were suboptimal. After analyzing thousands of interactions and reverse-engineering what makes for effective usage versus ineffective usage, I've identified the architectural principles that unlock ChatGPT's true potential. This isn't about prompt templates or tricks, but rather about really understanding the system architecture and designing your interactions in light of it.

Evaluating AI Vulnerability Detection: How Reliable Are LLMs for Secure Coding?

Aggregated on: 2025-11-14 21:11:06

Large language models (LLMs) can be used to generate source code, and these AI coding assistants have changed the landscape for how we produce software. Speeding up boilerplate tasks like syntax checking, generating test cases, and suggesting bug fixes accelerates the time to deliver production-ready code. What about securing our code from vulnerabilities? If AI can understand entire repositories within a context window, one might jump to the conclusion that they can also be used to replace traditional security scanning tools that are based on static analysis of source code.

5 Agent CI/CD Evaluation Best Practices

Aggregated on: 2025-11-14 20:11:06

Building reliability into your production applications isn’t flashy, but evaluations are critical to success. This is especially true when it comes to testing the impact of system changes, as small tweaks to your AI agents — like prompt versions, agent orchestration, and model changes — can have a large impact.

Spectre and Meltdown: How Modern CPUs Traded Security for Speed

Aggregated on: 2025-11-14 19:11:06

For years, CPU designers focused on making processors faster. Techniques like out-of-order and speculative execution became standard to keep every part of the chip busy. These tricks helped achieve huge performance gains, but they also opened the door to a new kind of vulnerability. In 2018, two major security flaws, Spectre and Meltdown, showed that the very features that made processors efficient could be used to steal private data from memory. These attacks broke some of the most fundamental assumptions about isolation between programs and the operating system.

Data Ingestion Using Logstash: PostgreSQL to Elasticsearch

Aggregated on: 2025-11-14 18:11:06

What Is Logstash? Logstash is an open-source data processing pipeline from Elastic. It is being used to ingest, transform, and ship data to different sources, including Elasticsearch, Kafka, flat files, etc. Logstash pipeline includes three different processes:

Best Practices for Migrating Data From Legacy Systems With AI

Aggregated on: 2025-11-14 17:11:06

Data migration is one of the most challenging and high-risk parts of legacy system modernization. No wonder engineers and business leaders are experimenting with modern tools to speed up this process. AI is among the most promising. In this article, I will share my experience with AI-powered data migration, including what worked well and key lessons learned along the way.

How the Craft of Software Engineering is Evolving With AI Advancement

Aggregated on: 2025-11-14 16:11:06

Summary Artificial intelligence is transforming the nature of software engineering, shifting it from a primarily code-centric discipline toward one that emphasizes systems thinking, verification, and orchestration. Emerging paradigms such as “vibe coding” and defensive AI programming illustrate how the field now demands not just technical mastery but also a broader understanding of how intelligent systems interact and evolve. While AI offers unprecedented productivity gains to those who integrate it effectively, engineers must cultivate the discernment and methodological structure required to maintain reliability, quality, and correctness at scale. Evolution from Builders to Curators Software engineering has long been associated primarily with coding. Entry-level engineers focus on churning out code, and as they grow in their careers, they learn more about system design, architecture, etc. But with the rise of AI-assisted coding tools, that primary identity is shifting.

Proactive vs Reactive: Rethinking Your AWS Monitoring Approach

Aggregated on: 2025-11-14 15:11:06

Keeping your AWS environment healthy is no longer just about responding to alerts when something goes wrong. As workloads scale and get more complex, relying on reactive monitoring leaves you exposed to downtime, security risks, and performance issues. Forward-thinking teams are reevaluating their AWS monitoring strategies — transitioning from reactive to proactive monitoring that prevents problems before they arise. This is not just a technical upgrade, it’s a strategic shift in how cloud architects, CTOs, and DevOps leaders ensure reliability, optimize resources, and security across modern infrastructure.

Exploring MCP, A2A, and Functional Calling: The Modern Innovations for Enterprise LLM Architecture

Aggregated on: 2025-11-14 14:11:06

Artificial intelligence is moving away from experimental prototypes to mission-critical enterprise-level infrastructure. Creating systems that work under the constraints and requirements present in the enterprise world has never been more challenging for developers. There are three innovative architectural patterns that will define production-grade AI solutions: model-context protocol (MCP), agent-to-agent Protocol (A2A), and functional calling. Model-Context Protocol (MCP): Context Execution Modality LLMs are limited in their handling of state, memory specific to the application, and context window. MCP addresses these issues by treating context as an architectural component. In MCP, context is not static but dynamic, based on sophisticated retrieval that injects the relevant data just in time.

Building Smarter Systems: Architecting AI Agents for Real-World Tasks

Aggregated on: 2025-11-14 13:11:06

Introduction AI agents are emerging as powerful building blocks for modern software systems. An AI agent is an autonomous software component that can observe its environment, make decisions, and act towards achieving a goal. Crucially, it operates with a degree of independence using real-time data, adapting to changing conditions, “unlike traditional scripts or workflows,” which follow fixed logic. This flexibility makes AI agents ideal for real-world tasks that involve complex, dynamic workflows. In this article, we’ll explore how to architect AI agents for workflow automation in a way that senior engineers can appreciate – focusing on clear language, practical tools (like Python’s LangChain and FastAPI), and sound engineering practices. AI Agents in Workflow Automation One high-impact application of AI agents is workflow automation. These agents can handle routine, repetitive tasks across business processes – from triaging support tickets and updating CRM records to validating form submissions or flagging errors in system logs. For example, instead of a human manually sorting incoming emails or a static script moving files on a schedule, an AI agent can understand the content and context, then decide the appropriate action. Unlike traditional automation tools that rigidly follow pre-defined rules, an AI agent adjusts its behavior based on context and goals. This means that if conditions change (for example, a new type of support issue arises), the agent can reason about how to handle it rather than needing a manual code update. The result is smarter workflow automation that can save time and reduce errors in real-world operations.

Data-Driven Enterprise Apps: How Laravel Simplifies Analytics Integration

Aggregated on: 2025-11-14 12:11:06

Data has become the very foundation of modern digital systems; it is the very essence of decision-making, innovation, and, ultimately, growth in the contemporary business world. Companies make extensive use of analytics to gain a deeper understanding of their customers, improve business operations, and make more accurate predictions. On the other hand, integrating data analysis into enterprise applications presents numerous challenges, including the need for scalability and flexibility. At this point, Laravel, the favored PHP framework, comes into play as the ideal tool. Laravel is widely regarded for its readability and ease of hiring Laravel developers. It is a structured method of building powerful, data-driven enterprise applications. And most importantly, it simplifies the process of embedding analytics, which is becoming increasingly vital in enterprise app development.

Databricks vs. Snowflake: Complete Architecture Mapping for Enterprise AI and Big Data

Aggregated on: 2025-11-13 20:11:04

As data ecosystems continue to evolve in the multi-cloud environment, organizations are increasingly blending platforms to optimize for specific workloads. A common pain point I've experienced is when architecting enterprise data solutions: terminology can often be a barrier. How do core concepts in Databricks translate to Snowflake? It's not just about semantics; rather, it's about building resilient and governed architectures across platforms without reinventing the wheel. In this article, I'll go beyond surface-level comparisons, exploring design patterns and illustrating flows and structures. Whether you're a data architect migrating workloads or a leader fostering cross-team collaboration, this mapping puts the emphasis on governance, domain-driven design, and data products, rather than vendor lock-in. Think of it as a blueprint for hybrid operations that supports multi-platform models.

Versioned Caching: A Practical Pattern for High-Performance Lookup Data

Aggregated on: 2025-11-13 19:11:04

Introduction to the Problem In modern web applications, there are different types of data that the client consumes from the server: dynamic data (like product listings or shopping carts), user-specific data, and static lookup data. Lookup data might include lists such as countries, regions, currencies, ZIP codes, or dropdown configurations that rarely change but must be available on every client session. Usually, to store and retrieve such data, a distributed cache is used, for example, Redis (from my practice). However, in large systems with dozens of endpoints and high-throughput requirements, even a distributed cache can become a performance bottleneck. That is exactly the situation I faced on one of my past projects, and in this article, I will share with you the approach we used to build a more efficient caching strategy using HTTP cache headers and versioning.

AI SDLC Transformation, Part 1: Where to Start?

Aggregated on: 2025-11-13 18:11:04

Most engineering leaders today feel the same tension: everyone talks about “AI in software delivery,” but few know where to start. Should you launch pilots? Train teams? Complement Jira or some other SDLC tools with some AI copilot plugins? Or just wait until the chaos settles?

When Memory Overflows: Too Many ApplicationContexts in Spring Integration Tests

Aggregated on: 2025-11-13 17:11:04

In Spring, the ApplicationContext is the central container object that manages all beans (i.e., components, services, repositories, etc.). Its tasks include reading the configuration (Java Config, XML, annotations), creating and managing bean instances, handling dependency injection, and running the application lifecycle.

How to Simply Manage Releases With Git

Aggregated on: 2025-11-13 16:11:04

This post briefly outlines a simple yet clean process for managing the releases of an application via Git/GitHub. Its goal is to provide a straightforward recipe, while it's assumed that these practices may vary from situation to situation, from team to team, from project to project. Setup The subject is a small project whose source code resides in GitHub. Details on how it was created and synced into the source repository may be found here, although this aspect is less important in the context of this article.

DocumentDB Goes Cloud-Native: Introducing the DocumentDB Kubernetes Operator

Aggregated on: 2025-11-13 15:11:04

Today, we're excited to announce the DocumentDB Kubernetes Operator, an open-source, cloud-native solution to deploy, manage, and scale DocumentDB instances on Kubernetes. DocumentDB is a MongoDB-compatible, open-source document database built on PostgreSQL. The DocumentDB Kubernetes Operator represents a natural evolution of the DocumentDB ecosystem, following our open source announcement and recent joining of the Linux Foundation. When it comes to distributed databases, there is no one-size-fits-all solution. Database-as-a-Service (DBaaS) options may not always meet customers' data sovereignty or portability needs. On the other hand, managing database clusters manually is complex and resource-intensive. What’s needed is a balanced approach that automates routine tasks like updates and backups, while simplifying operations such as scaling, failover, and recovery. This is precisely where Kubernetes excels — bridging automation with operational simplicity.

How 8x8 Saved and Boosted Performance 30% by Adopting Ampere®

Aggregated on: 2025-11-13 14:11:04

The Challenge In 2020, during the COVID-19 pandemic, demand for 8x8’s encrypted video conferencing service increased exponentially worldwide — jumping from 20 virtual servers to 8,000 in a matter of weeks. Although the work an 8x8 video router does is simple enough to explain, it’s actually quite CPU-intensive. Scaling this system out meant deploying more servers in the cloud. 8x8 servers were suffering through the same number of network performance bottlenecks in a matter of hours as their engineers would have expected to have dealt with over the course of an entire year. “Usually when something goes wrong, a machine would get overwhelmed, there would be too much demand on the CPU, and there would be not enough work done to take packets out of queues as they arrived,” explained Emil Ivov of 8x8.

ANTLR-Powered Query Engine and Floorplan Parser for Real Estate Data

Aggregated on: 2025-11-13 13:11:04

Some time ago, my colleagues published two deep-dive technical articles that walk through a real challenge we faced: turning chaotic, inconsistent real estate data into something machines and users can actually work with. If you’ve ever tried to make sense of MLS feeds, build a custom property search, or generate layouts from encoded property plans, you know the pain. This wasn’t a matter of plugging in a library or buying an API. We had to build the underlying parsers ourselves.

Building Fault-Tolerant Applications with Temporal Workflow Engine

Aggregated on: 2025-11-13 12:11:04

Workflows and orchestration are critical to most software systems. In fact, at a fundamental level, every piece of software is essentially a workflow: you write functions and execute them in a specific order. These functions retain state (variables), pass state along, discard state, and renew state. There are dependencies and relationships between these functions. Broadly speaking, any software system can be divided into two parts, orchestration and atomic actions. Orchestration handles retries, state management between methods, resource management, and error or crash handling. Now, imagine a system that handles all the orchestration aspects of software development, allowing you to focus solely on the atomic steps. Enter Temporal, a workflow engine that does exactly that. Temporal manages workflow orchestration for you, allowing you to focus on developing core business logic. From this perspective, Temporal is a truly powerful tool in my opinion.

Facilitate AI-Human Architectural Thinking

Aggregated on: 2025-11-12 20:11:04

Architectural thinking or modeling will only be effective when it involves a landscape, decisional, and structural view. People talk a lot about systems thinking, but to ensure a viable architectural model, systems thinking must approach it from the three pillars of thinking. AI-powered architectural thinking or modeling also requires these three pillars of thinking. Let’s briefly look at each of these from a perspective of enterprise solution architecture (ESA), which falls somewhere between enterprise architecture (EA) and solution architecture (SA).

The Terraform State Locking Migration You Need to Know About: Moving Beyond DynamoDB

Aggregated on: 2025-11-12 19:11:03

If you've been working with Terraform for a while, you probably have backend configurations that look something like this scattered across your infrastructure repositories. I know I did. And if you're like me, you might have missed a pretty significant announcement from HashiCorp that's going to affect how we handle state locking going forward. Let me save you some future headaches: DynamoDB-based state locking is being deprecated.

Compose Architecture, Done Right: MVI’s Unidirectional State vs. MVVM

Aggregated on: 2025-11-12 18:11:03

Why MVVM Feels Clunky With Compose MVVM grew up with XML and two-way data binding. Compose flipped the model: the UI is a function of state. That mismatch shows up in common pain points: Scattered state. Multiple LiveData/Flows (loading, items, error, searchQuery, pagination…) mutate independently. Compose recomposes at odd times, and you start sprinkling remember { mutableStateOf(...) } to “patch” glitches. The “SingleLiveEvent” saga. One-off actions (toasts, navigation, snackbars) don’t belong in your steady UI state, so teams hack in special event wrappers that break on configuration change or process death. Implicit writes. With two-way binding (or eager observers), it’s not obvious who changed what. You hunt bugs by grepping for setters. Brittle tests. It’s hard to reproduce a bug when the state can be mutated from multiple pathways and observer races. What MVI Brings to the Table Model-View-Intent (MVI) is just a strict recipe for unidirectional data flow:

How to Map PostgreSQL JSON Data Types in Java Using asentinel-orm

Aggregated on: 2025-11-12 17:11:04

It isn’t seldom when software products need to easily and efficiently manage the direct storage and handling of JSON content directly into the underlying database. The purpose of this article is to exemplify how such tasks can be conveniently accomplished via the asentinel-orm, a lightweight ORM tool built on top of Spring JDBC, which possesses most of the features one would expect from such a project. We will start by defining a simple entity that contains a JSONB column. Then, we will configure a sample application that uses the asentinel-orm to handle its data access towards a PostgreSQL database that stores such entities. Lastly, we will exemplify and emphasize how the actual JSON data can be queried and stored properly.