News Aggregator

A Beginner's Guide to Docker Compose

Aggregated on: 2025-11-06 20:11:00

The constant need to run docker run in multiple terminals for managing multi-container setups. If you’re new to Docker, you’ve likely already discovered the docker run command that makes it easy to run a single container. However, what happens if your app requires a database? A cache? Before you know it, you’re dealing with a terminal tab nightmare:

Unlocking Modernization: SUSE Virtualization on Arm64 With Harvester

Aggregated on: 2025-11-06 19:11:00

As the number of data centers and their size grow worldwide, requiring increased efficiency, scalability, and agility from IT infrastructure, the convergence of virtual machines (VMs) and cloud-native technologies is crucial for success. A recent conversation between Dave Neary of Ampere Computing and Alexandra Settle, Product Manager for SUSE Virtualization, highlights a significant step forward: the general availability of SUSE Virtualization for Arm64 architecture, and Harvester’s pivotal role within SUSE’s ecosystem. This white paper summarizes their discussion, highlighting how SUSE is empowering organizations to modernize infrastructure with energy-efficient, high-performance solutions.

A Developer's Guide to Sentiment Analysis With Naive Bayes and Python

Aggregated on: 2025-11-06 18:11:00

Sentiment analysis is a powerful tool for understanding customer feedback, social media comments, and product reviews. It allows us to programmatically determine whether a piece of text is positive, negative, or neutral. While complex models like Transformers (e.g., BERT) often grab the headlines, the classic Multinomial Naive Bayes classifier remains a surprisingly effective, efficient, and interpretable baseline, especially for text-based tasks. In this guide, we'll walk through a complete sentiment analysis project using Python and Scikit-learn. We'll cover:

A Beginner's Guide to Essential Commands to Fix Container Setup Issues

Aggregated on: 2025-11-06 17:11:00

You followed the tutorial. You ran Docker Run. And... nothing. The container exited immediately. Or maybe it is running, but localhost just shows “This site can’t be reached.” We’ve all been there. When you’re new to Docker, a failing container can feel like a sealed black box. You know something is wrong inside, but you have no idea how to look. In this quick guide, I’ll show you the five essential commands to open that box, see what’s happening, and fix your broken containers.

A Practical Guide to Modernizing Data Serving Architectures Using DBSQL

Aggregated on: 2025-11-06 16:11:00

If you’ve ever built analytical dashboards, chances are you’ve worked with the old “batch-load-to-RDS” pattern, where we load the data into RDS from a batch process and read from that operational data store, such as AWS RDS or other databases. We certainly did in our case — and for a while, it served us well. Our applications pulled data from a lakehouse like Snowflake or Delta Lake, transformed it in AWS Glue, and loaded it into Amazon RDS. The RDS database powered Lambda APIs that fed dashboards and front-end apps.

Optimizing Write-Heavy Database Workloads for Low Latency

Aggregated on: 2025-11-06 15:11:00

Write-heavy database workloads bring a distinctly different set of challenges than read-heavy ones. For example: Scaling writes can be costly, especially if you pay per operation, and writes are 5X more costly than reads Locking can add delays and reduce throughput I/O bottlenecks can lead to write amplification and complicate crash recovery Database backpressure can throttle the incoming load While cost matters quite a lot, in many cases, it’s not a topic we want to cover here. Rather, let’s focus on the performance-related complexities that teams commonly face and discuss your options for tackling them.

Tactical Domain-Driven Design: Bringing Strategy to Code

Aggregated on: 2025-11-06 14:11:00

In the previous article, I discussed the most often overlooked aspect of Domain-Driven Design: the strategic side. When it comes to software development, teams tend to rush toward code, believing that implementation will clarify the domain. History shows the opposite — building without understanding the underlying reason or direction often leads to systems that are technically correct but conceptually wrong. As the old Latin root of strategy (strategos, “the art of the general”) suggests, the plan must precede the movement. Now that we’ve explored the “why” and “what,” it’s time to turn to the “how.” Tactical DDD represents this next step — the process of transforming a well-understood domain into expressive, maintainable code. While strategic design defines boundaries and fosters a shared understanding, tactical design brings those ideas to life within each bounded context.

The JVM Pause That Wasn't: A War Story

Aggregated on: 2025-11-06 13:11:00

In high-performance computing, we are trained to hunt for bottlenecks in our code, our algorithms, or our infrastructure. But my favorite bug was not in any of those. It was an invisible interaction between the JVM's garbage collector and the server's disk, resulting in 15+ second, stop-the-world (STW) pauses on a service handling millions of requests per second. The Mystery: The 503 Spikes I was working on a large-scale Java service handling millions of user requests per second. The system was designed for extreme throughput, but we were plagued by intermittent spikes in load balancer timeouts, causing 503 responses to be returned to the users.

Master Production-Ready Big Data, Apache Spark Jobs in Databricks and Beyond: An Expert Guide

Aggregated on: 2025-11-06 12:11:00

This iteration is based on existing experience scaling big data with Apache Spark workloads and uses more refinements by still preserving the eight most important strategies but moving high-value but less important strategies — such as preferring narrow transformations, applying code-level best practices, leveraging Databricks Runtime features, and optimizing cluster configuration — to a Miscellaneous section, thereby not losing focus on impactful areas such as shuffles and memory, but still addressing them thoroughly. Diagrams for in-phased insights and example code can be completely executed in Databricks or vanilla Spark sessions, and for all of these to be worth your time, the application will yield unbelievable performance benefits, often in the range of 5–20x in real-world pipelines.

Bridging the Divide: Tactical Security Approaches for Vendor Integration in Hybrid Architectures

Aggregated on: 2025-11-05 20:11:00

Security architecture in hybrid environments has traditionally focused on well-known concepts such as OWASP vulnerabilities, identity and access management, role-based access control, network security, and the principle of least privilege. Best practices like secure coding and incorporating SAST/DAST testing into CI/CD pipelines are also widely discussed. However, when organizations operate in a hybrid model — running workloads both on-premises and in the cloud — while also integrating with vendor-managed cloud solutions, a different set of security design considerations comes into play. These scenarios are not uncommon, yet they are rarely highlighted in the context of secure solution implementation involving vendor software in hybrid environments.

Build a LangGraph Multi-Agent system in 20 Minutes with LaunchDarkly AI Configs

Aggregated on: 2025-11-05 19:11:00

Overview Build a working multi-agent system with dynamic configuration in 20 minutes using LangGraph multi-agent workflows, RAG search, and LaunchDarkly AI Configs. Part 1 of 3 of the series: Chaos to Clarity: Defensible AI Systems That Deliver on Your Goals

JPlus: A Modern Java Superset Language

Aggregated on: 2025-11-05 18:11:00

JPlus is a Java superset language running on the JVM that enhances developer productivity while staying fully compatible with the Java ecosystem. Why Support JPlus? JPlus fills a unique gap in the Java ecosystem:

AI Code Generation: The Productivity Paradox in Software Development

Aggregated on: 2025-11-05 17:11:00

Measuring and improving developer productivity has long been a complex and contentious topic in software engineering. With the rapid rise of AI across nearly every domain, it’s only natural that the impact of AI tooling on developer productivity has become a focal point of renewed debate. A widely held belief suggests that AI could either render developers obsolete or dramatically boost their productivity — depending on whom you ask. Numerous claims from organizations linking layoffs directly to AI adoption have further intensified this perception, casting AI as both a disruptor and a catalyst.

Unified CI/CD Interface: Strategic DevOps Acceleration

Aggregated on: 2025-11-05 16:11:00

Context Technology is evolving rapidly, and organizations must continuously adapt to remain competitive. One critical area of evolution is CI/CD (Continuous Integration/Continuous Deployment) methodology, which plays a key role in accelerating software delivery. Various CI/CD tools like Jenkins, Bamboo, and Cloud Build offer powerful features, and with the release of such new platforms, teams often consider migration to enhance efficiency, scalability, and cost-effectiveness. Despite shared pipeline stages — Build, Test, Scan, Store, Deploy, Monitor — each tool has: Unique syntax Proprietary structure Distinct execution models Lack of standardization complicates tool migration and creates technical debt. No seamless way to switch tools based on evolving business needs, scalability, or cost optimization. Technical Problem Significant challenges when migrating CI/CD pipelines across platforms: Pipeline Syntax Incompatibility Each tool uses a different DSL (e.g., Groovy for Jenkins, YAML for GitHub Actions, XML for Bamboo). Pipelines cannot be reused across platforms and must be rebuilt from scratch. Manual Rewriting Overhead Engineers manually reverse-engineer pipelines, translate logic, and test iteratively. This process is slow, error-prone, and non-scalable. Skills Mismatch & Learning Curve Engineers lack cross-platform CI/CD expertise. Ramp-up delays impact delivery velocity. No Standardized Migration Framework No platform-agnostic, intelligent solution exists for end-to-end CI/CD migration. Solution Overview Unified Interface: CI/CD Interface acts as an abstraction layer that internally communicates with various CI/CD platforms. Single Configuration Input: A DevOps engineer configures the pipeline once in a standardized format. Automated Translation: Whenever a new CI/CD platform is adopted, initiating a migration request via the interface allows the CI/CD Interface to generate the pipeline scripts for the selected CI tool based on the configurations. Comprehensive Platform Support: Supports all existing CI/CD tools and extends compatibility for future releases. Write Once, Run Forever: Eliminates repetitive configuration efforts, ensuring scalability and flexibility for DevOps teams. With Unified CI/CD Interface, organizations can reduce migration overhead, accelerate CI/CD adoption, and future-proof their DevOps workflows.

How to Integrate Playwright With Azure DevOps for Automated UI Testing

Aggregated on: 2025-11-05 15:11:00

Introduction Modern web applications require consistent, dependable cross-browser testing to ensure quality. Playwright has quickly emerged as a leading tool for end-to-end automation, offering speed, flexibility, and out-of-the-box support for today’s browsers and frameworks. On the other hand, Azure DevOps provides a robust cloud-based CI/CD solution from Microsoft. When combined, they create a powerful foundation for building reliable, scalable pipelines for automated UI testing. This article shows you how to integrate Playwright with Azure DevOps step by step to:

Monolith vs Microservices vs Modulith

Aggregated on: 2025-11-05 14:11:00

Introduction The software industry has embraced various architectural patterns, including monolithic and microservices architecture, to address business use cases effectively. In recent years, modulith architecture adoption has gained traction, although a version of this approach has already been utilized in many enterprises. Figure 1 illustrates the evolution of architecture patterns over time. The transition began with monolithic applications. However, as monolithic applications became increasingly resistant to change and challenging to manage, enterprises transitioned to microservices architecture. Over time, the growing number of microservices introduced complexities in management, prompting enterprises to adopt modulith architecture, which offers a balanced approach between monoliths and microservices.

The Tech Landscape of 2026: What Developers Need to Learn Now

Aggregated on: 2025-11-05 13:11:00

This roadmap outlines a shift from coder to systems architect. Developers must lead AI integration, build secure and scalable infrastructure, and think sustainably. Theme 1. AI-Driven Revolution: Prompt engineering, LLM orchestration, hyperautomation, data fluency Theme 2. Modern Infrastructure: Platform engineering, edge computing, DevSecOps Theme 3. Developer Toolkit: Low-code integration, green software, polyglot programming Your new role: Design and secure intelligent systems, not just code them.

AI-Assisted Software Engineering With OOPS, SOLID Principles, and Documentation

Aggregated on: 2025-11-05 12:11:00

Top-down and bottom-up approaches are two problem-solving approaches to divide and conquer a problem. What Is the Top-Down Approach? Take any problem, break it down until you are in a position to schedule it to a machine, OS, SDK, or software system.

Modular Monoliths Explained: Structure, Strategy, and Scalability

Aggregated on: 2025-11-04 20:10:59

Choosing the right architectural style is crucial for building robust systems. It’s a decision that must balance short, medium, and long-term needs — and the trade-offs involved deserve thoughtful consideration. In software engineering, we’re spoiled for choice. But with buzzwords flying around and shiny new paradigms tempting us at every turn (did someone say FOMO?), it’s easy to feel overwhelmed. Some styles get dismissed as “legacy” simply because they didn’t work well for a few organizations — even if they still hold merit.

Federated API Management: Deploying APIs From WSO2 to AWS API Gateway

Aggregated on: 2025-11-04 19:10:59

As enterprises evolve and grow, organizations are increasingly operating APIs across multiple environments, such as cloud, on-premises, and edge. In such setups, federated API gateway deployments have emerged as a powerful architectural pattern. This approach separates the control plane from the runtime gateways, enabling centralized API governance while allowing APIs to run closer to users or services for improved performance and resilience. At the core of this architecture is the idea of:

This Compiler Bottleneck Took 16 Hours Off Our Training Time

Aggregated on: 2025-11-04 18:10:59

A 60-hour training job had become the new normal. GPUs were saturated, data pipelines looked healthy, and infra monitoring didn’t flag any issues. But something was off. The model wasn't large, nor was the data complex enough to justify that duration. What we eventually discovered wasn't in the Python code or the model definition. It was buried deep in the compiler stack. Identifying the Invisible Bottleneck

Series (4/4): Toward a Shared Language Between Humans and Machines — Humans as Co-Creators: Ethics, Strategy, and the Future of a Shared Language

Aggregated on: 2025-11-04 17:10:59

AI inspires both fascination and fear: are machines capable of replacing us, or are they merely assistants? The real question is not substitution, but co-creation. How can we preserve the uniqueness of human intelligence while harnessing the power of models? This article explores the ethical, economic, and political challenges of a future where humans and machines will have to invent a common language together. In areas such as code translation or transcompilation, neural models can outperform traditional methods and speed up processes. But their role is not to replace human expertise; it is to extend and enhance it. In fields such as medicine, architecture, or education, AI can help simulate, plan, and generate alternatives, but in the end, it is the human who must decide, interpret, and give meaning.

Agentic AI using Apache Kafka as Event Broker with the Agent2Agent Protocol (A2A) and MCP

Aggregated on: 2025-11-04 16:25:59

Agentic AI is gaining traction as a design pattern for building more intelligent, autonomous, and collaborative systems. Unlike traditional task-based automation, agentic AI involves intelligent agents that operate independently, make contextual decisions, and collaborate with other agents or systems—across domains, departments, and even enterprises. In the enterprise world, agentic AI is more than just a technical concept; it is a transformative force. It represents a shift in how systems interact, learn, and evolve. But unlocking its full potential requires more than AI models and point-to-point APIs—it demands the right integration backbone.

From Noise to Outcome-Driven Observability: An SLO-First Strategy to Deliver Business Value Through Telemetry

Aggregated on: 2025-11-04 15:25:59

Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Intelligent Observability: Building a Foundation for Reliability at Scale. Outcome-driven observability, anchored by a strategy that puts service-level objectives (SLOs) first, is a shift in modern software engineering practices. It moves the conversation beyond a reactive, tool-centric approach to a proactive, discipline-driven methodology.

Advanced Patterns in Salesforce LWC: Reusable Components and Performance Optimization

Aggregated on: 2025-11-04 14:25:59

If you’ve built Lightning Web Components (LWC) at scale, you’ve probably hit the same walls I did: duplicated logic, bloated bundles, rerenders that come out of nowhere, and components that were never meant to talk to each other but somehow ended up coupled. When I first transitioned from Aura and Visualforce to LWC, the basics felt easy: reactive properties, lifecycle hooks, and clean templates. But as our team started building enterprise-grade Salesforce apps dozens of screens, hundreds of components the cracks started showing. Performance dipped. Reusability turned into a myth. New devs struggled to onboard without breaking something.

Top Takeaways From Devoxx Belgium 2025

Aggregated on: 2025-11-04 13:25:59

In October 2025, I visited Devoxx Belgium, and again it was an awesome event! I learned a lot and received quite a lot of information, which I do not want to withhold from you. In this blog, you can find my takeaways of Devoxx Belgium 2025! Introduction Devoxx Belgium is the largest Java conference in Europe. This year, it was already the 22nd edition. As always, Devoxx is being held in the fantastic theatres of Kinepolis Antwerp. Each year, there is a rush on the tickets. Tickets are released in several batches, so if you could not get a ticket during the first batch, you will get another chance.

AIOps to Agentic AIOps: Building Trustworthy Symbiotic Workflows With Human-in-the-Loop LLMs

Aggregated on: 2025-11-04 12:25:59

Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Intelligent Observability: Building a Foundation for Reliability at Scale. Imagine a world where the 3:00 AM PagerDuty alert doesn’t lead to a frantic scramble, but rather to a concise summary of the problem, a vetted solution, and a one-click button to approve the fix. This transformative capability represents the next frontier of AIOps (artificial intelligence for IT operations), powered by agentic AI systems that are designed to perceive, reason, act, and learn. This shift promises a significant reduction in mean time to resolution (MTTR) but critically relies on human-in-the-loop (HITL) safeguards to ensure accountability and prevent issues like AI hallucinations.

Detecting Supply Chain Attacks in NPM, PyPI, and Docker: Real-World Techniques That Work

Aggregated on: 2025-11-03 20:25:59

The digital ecosystem breathes through trust. Every npm install, every pip install, every docker pull represents a leap of faith — a developer placing confidence in code written by strangers, maintained by volunteers, distributed through systems they've never seen. This trust, however, has become the Achilles' heel of modern software development. Supply chain attacks don't knock on your front door. They slip through the dependencies you invited in yourself.

Building a Resilient Observability Stack in 2025: Practical Steps to Reduce Tool Sprawl With OpenTelemetry, Unified Platforms, and AI-Ready Monitoring

Aggregated on: 2025-11-03 19:25:59

Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Intelligent Observability: Building a Foundation for Reliability at Scale. Platform consolidation is an important topic in 2025 as tool sprawl and platform fragmentation are costing engineering teams time, money, and focus. Some surveys of observability practitioners show that 80% of teams are working on reducing vendor count and consolidating their observability and monitoring tools.

5 Critical Databricks Performance Hacks That Most Engineers Miss (100x Faster Queries)

Aggregated on: 2025-11-03 18:25:59

Databricks performance tuning is not guesswork; it needs a deep understanding of internals. In this guide, I will explore six practical optimization techniques every data engineer should apply to achieve faster, cost-efficient production.

What Is Agent Observability? Key Lessons Learned

Aggregated on: 2025-11-03 17:25:59

Agents are proliferating like wildfire, yet there is a ton of confusion surrounding foundational concepts such as agent observability. Is it the same as AI observability? What problem does it solve, and how does it work? Fear not, we'll dive into these questions and more. Along the way, we will cite specific user examples as well as our own experience in pushing a customer-facing AI agent into production.

Understanding Bigfile Tablespace Defaults in Oracle Database 23ai: Impact and Benefits

Aggregated on: 2025-11-03 16:25:59

Oracle Database 23ai introduces a significant but subtle shift in its default storage architecture, the adoption of bigfile tablespaces as the default for most tablespaces, including core ones like SYSTEM and SYSAUX. This change reflects Oracle’s continued evolution towards simplifying storage management and scaling capabilities. As database professionals with years of experience managing complex environments, understanding this new default and its impact on performance and administration is critical. Background: What Are Bigfile Tablespaces? Bigfile tablespaces were originally introduced in Oracle 10g as a means to address scalability challenges in very large databases. Unlike traditional small-file tablespaces, which consist of multiple datafiles each limited in size (typically up to 32 GB or 128 GB, depending on block size and OS), a bigfile tablespace consists of a single large datafile that can grow up to 128 terabytes (TB) or more, depending on the platform and block size.

Navigating the Cyber Frontier: AI and ML's Role in Shaping Tomorrow's Threat Defense

Aggregated on: 2025-11-03 15:25:59

Abstract This article explores the transformative role of artificial intelligence (AI) and machine learning (ML) in cybersecurity. It delves into innovative strategies such as adaptive cyber deception and predictive behavioral analysis, which are reshaping defense mechanisms against cyber threats. The integration of AI in zero-trust architectures, quantum cryptography, and automation within cybersecurity frameworks highlights a shift towards more dynamic and proactive security measures. Furthermore, the challenges of the "black box" problem in AI decision-making and the potential for AI to automate routine cybersecurity tasks are discussed. The narrative underscores the importance of complementing technology with human insight for effective digital defenses. Introduction: A Personal Encounter With Cyber Evolution Let me rewind a few years back — a time when I was knee-deep in implementing a creditworthiness model at my previous role at Sar Tech LLC/Capital One. It was around the same time I encountered the formidable intersection of artificial intelligence (AI) and cybersecurity. While tuning machine learning (ML) algorithms to reduce loan approval risks, I witnessed firsthand how AI could pivot an organization's security posture in ways I hadn’t quite imagined before. This realization didn't stem from an academic paper or industry panel — it came from the challenge of protecting sensitive data while simultaneously fine-tuning predictive models. It was an "aha" moment, one which highlighted the potential of AI and ML in a broader, more dynamic context of cybersecurity.

8 Business Continuity Lessons Learned from the CrowdStrike Outage

Aggregated on: 2025-11-03 14:25:58

The July 2024 CrowdStrike outage sent shockwaves across global enterprises, paralyzing operations and forcing services offline. A faulty update to a widely used endpoint detection and response (EDR) solution caused the event to cascade into a full-blown operational crisis. The outage was so disruptive that it has since become a defining case study on preventing the impact of an EDR outage. Many organizations saw it as a wake-up call, making it a critical reminder that business continuity planning (BCP) must evolve alongside the growing adoption of third-party cybersecurity services. Companies now ensure uptime with the following lessons learned from Crowdstrike.

Delta Lake 4.0 and Delta Kernel: What's New in the Future of Data Lakehouses

Aggregated on: 2025-11-03 13:25:58

In data storage, the idea of a data lakehouse has transformed data storage and analysis in organizations. Data lakehouses combine the low-cost and scalability storage ability of data lakes and data warehouse’s reliability and performance. In this space, some players have emerged, such as Delta Lake, as strong open-source frameworks for implementing robust ACID-compliant data Lakes. Now, with the introduction of Delta Lake 4.0 and the development of Delta Kernel, the future of the lakehouse architecture is in a revolutionary transition. Brimming with features driving performance, scaling, and interoperability, these updates are to keep up with the increasing dynamics of data workloads in 2025 and beyond.

Human-AI Readiness

Aggregated on: 2025-11-03 12:25:58

The expectations are cosmic. The investments are colossal. Amazon, Google, Meta, and Microsoft collectively spent over $251 billion on infrastructure investment to support AI in 2024, up 62% from 2023's $155 billion, and they plan to spend more than $300 billion in 2025. The prize for those who can provide "superior intelligence on tap," as some are now touting, is infinite. The AI ecosystem is exploding, with new startups and innovative offerings pouring out of global tech hubs. The technology isn’t just evolving; it’s erupting. The theory of AI adoption is also evolving. While everyone acknowledges that risk remains high and vigilance is necessary, concerns are shifting from Terminator-style apocalyptic fantasies to the practical realities of global social disruption anticipated, as AI’s impact cascades through, well, everything. As is becoming clear, we’ll be living next to and collaborating with AI interfaces in every form, from phones and smart glasses to robots and drones.

A Framework for Securing Open-Source Observability at the Edge

Aggregated on: 2025-10-31 19:25:57

The Edge Observability Security Challenge Deploying an open-source observability solution to distributed retail edge locations creates a fundamental security challenge. With thousands of locations processing sensitive data like payments and customers' personally identifiable information (PII), every telemetry component running on the edge becomes a potential entry point for attackers. Edge environments operate in spaces where there is limited physical security, bandwidth constraints shared with business-critical application traffic, and no technical staff on-site for incident response. Therefore, traditional centralized monitoring security models do not fit in these conditions because they require abundant resources, dedicated security teams, and controlled physical environments. None of them exists on the edge.

Series (3/4): Toward a Shared Language Between Humans and Machines —Quantum Language and the Limits of Simulation

Aggregated on: 2025-10-31 18:25:57

Imagine compressing thousands of dimensions of meaning into a few qubits capable of processing all that information in parallel. That is the promise of Quantum Natural Language Processing. But can we truly translate the richness of human language into the abstract logic of quantum mechanics, without any grounding in reality? This article explores that frontier where science fiction and fundamental research meet. Research in this field experiments with translations by leveraging quantum parallelism and entanglement. In other words, it uses the unique properties of qubits to process multiple meanings simultaneously and to establish connections between them.

Streamlining Real-Time Ad Tech Systems: Techniques to Prevent Performance Bottlenecks

Aggregated on: 2025-10-31 17:25:57

Ad tech platforms operate at the bleeding edge of scale. Every millisecond counts as billions of requests flow through auctions, targeting services, and delivery pipelines. With net margins slim—most revenue is passed on to publishers — every CPU cycle saved can translate directly into profit. Optimizing these systems is not simply about shaving latency; it is about building architectures that remain efficient as workloads multiply and business pressures intensify. Minor inefficiencies, invisible in isolation, compound into significant overhead when magnified across thousands of servers. In a domain where revenue depends on speed and precision, the difference between sustainable growth and runaway costs often comes down to how carefully systems are engineered at the micro-level.

A Developer’s Experience of Onboarding to a Platform

Aggregated on: 2025-10-31 16:25:57

After years of managing cloud services in a traditional setting — manually provisioning clusters, setting up networks, managing credentials, and navigating deployment scripts — I thought I had mastered the rhythm of delivery. Dashboards, support tickets, and carefully planned change windows were the most important things in my life. It was safe, predictable, and well-organized, but it was also slow, tiring, and full of dependencies that only worked after a lot of planning and careful changes. Then came the shift.

Implementing Zero-Downtime Upgrades in an Enterprise SaaS Application

Aggregated on: 2025-10-31 15:25:57

Ensuring your enterprise SaaS application remains always available is more than just a technical objective; it’s a fundamental business requirement. Even short periods of downtime, such as those during routine software updates, can disrupt customers’ operations, erode their trust, and lead to contractual penalties if service-level agreements aren’t met. SaaS applications serve users across multiple time zones. Scheduling downtime that accommodates all users is impractical, making zero-downtime upgrades essential for global businesses. Zero-downtime processes allow for quicker deployment of features, bug fixes, and security patches, supporting agile development and reducing time-to-market. Mastering zero-downtime upgrade procedures, in tandem with robust multi-cloud and multi-region architectures, is essential in maintaining service availability. Recent high-profile outages on major cloud platforms have underscored the disruptive impact of unexpected downtime, affecting organizations worldwide across industries. By distributing workloads across multiple cloud providers and hosting applications in multiple geographic regions, organizations can reduce single points of failure and improve resilience against large-scale outages. If one cloud provider or region experiences issues, traffic can be quickly rerouted to healthy environments, minimizing the impact on end users.

Lessons Learned From Running Disaster Recovery Drills

Aggregated on: 2025-10-31 14:25:57

Disaster recovery (DR) is not just about backing up data — it’s also about ensuring that when the unexpected issue strikes, systems, people, and processes can recover quickly and efficiently. While planning and documentation are essential, the true test of a DR strategy comes from running drills. Through multiple exercises across organizations, here are the critical learnings that can significantly improve the effectiveness of DR initiatives.

Deployable Architecture: The Cornerstone of Scalable Platform Engineering

Aggregated on: 2025-10-31 13:25:57

As architects, you’ve likely seen the same story unfold across growing organizations: teams move fast, each solving problems in their own way — building pipelines, wiring infrastructure, and embedding security into their services from scratch. Initially, it works. But as the organization scales, the cracks begin to show. Environments drift. Deployments become brittle. Governance becomes reactive. And suddenly, the well-intentioned architecture you’ve crafted becomes challenging to replicate, secure, or evolve consistently across teams.

How Modern Developers Use AI-Assisted Coding to Validate Products Faster

Aggregated on: 2025-10-31 12:25:57

Software development has changed a lot in the past two years. I've been working with AI coding assistants since they first appeared. The most interesting part? It's not just about writing code faster. AI has changed how we validate our products. My co-founder and I noticed something strange on our latest project. Our team was shipping features super fast. But we also had more edge cases and security issues. This is the new reality. You move faster, but things get more complex. Most teams using AI tools face this.

An Open-Source ChatGPT App Generator

Aggregated on: 2025-10-31 11:25:57

OpenAI released ChatGPT apps just a couple of days ago. Such apps are incredibly interesting from a UX perspective, because sometimes a chat user interface simply won't cut it. Sometimes, you simply need a graphical user interface. For such cases, there are "ChatGPT apps." So, what is a ChatGPT app? Well, it's a fully functional user interface with buttons, dropdown lists, checkboxes, and everything you can create on the web. It can be as complex as Google Maps or as simple as a collect email form. It is basically "an app" hosted inside your AI chatbot. You can try a simple such app by clicking here.

When Coalesce Is Slower Than Repartition: A Spark Performance Paradox

Aggregated on: 2025-10-30 19:10:56

If you've worked with Apache Spark, you've probably heard the conventional wisdom: "Use coalesce() instead of repartition() when reducing partitions — it's faster because it avoids a shuffle." This advice appears in documentation, blog posts, and is repeated across Stack Overflow threads. But what if I told you this isn't always true? In a recent production workload, I discovered that using repartition() instead of coalesce() resulted in a 33% performance improvement (16 minutes vs. 23 minutes) when writing data to fewer partitions. This counterintuitive result reveals an important lesson about Spark's Catalyst optimizer that every Spark developer should understand.

SQL Ledger in SQL Server 2022: Tamper-Evident Audit Trails and Immutable Ledger Tables

Aggregated on: 2025-10-30 18:10:56

SQL Server 2022 introduced the Ledger feature to meet the growing need for tamper-evident audit trails in regulated and audit-heavy industries such as finance, healthcare, and supply chains. One of the most notable implementations of this feature is the append-only ledger table, which ensures that sensitive data is immutable once added, providing stronger guarantees of integrity and compliance. Below, we incorporate and expand on the example and details from Microsoft's official article on creating and using append-only ledger tables, showcasing its capabilities to preserve data integrity and support audit scenarios.

A Comprehensive Analysis of Async Communication in Microservice Architecture

Aggregated on: 2025-10-30 17:10:57

Microservice architecture has become a standard practice for companies, small and large. One of the challenges is communication between different services. I’ve worked with microservices for a decade now, and I’ve seen a lot of people struggle to understand how to implement a proper communication protocol. In this series of articles, I’ll share my knowledge and expertise on async communication in microservices.

From Model to Microservice: A Practical Guide to Deploying ML Models as APIs

Aggregated on: 2025-10-30 16:10:56

You’ve done it. You’ve spent weeks cleaning data, feature engineering, and hyperparameter tuning. You have a Jupyter Notebook showing a beautiful .fit() and a .predict() that works perfectly. The model accuracy is 99%. Victory! But now comes the hard part. Your stakeholder asks, "That's great, but how do we get this into the new mobile app?" Suddenly, the reality hits: a model in a notebook delivers zero business value. To be truly useful, your machine learning model needs to be integrated into applications, and the most robust, scalable way to do so is to deploy it as a Microservice API.

Keyword vs Semantic Search With AI

Aggregated on: 2025-10-30 15:10:56

When building a search for an application, you typically face two broad approaches: Traditional keyword-based search — match words exactly or with simple variants. Semantic (or vector) search — match meaning or context using AI embeddings. There’s also a hybrid approach, but I will leave that for a future article. Instead, in this post, I’ll walk you through how the two broad approaches work in Python using MariaDB and an AI embedding model, highlight where they differ, and show code that you can adapt.