News Aggregator

Hudi vs. Delta vs. Iceberg: How to Choose the Right Lakehouse Table Format

Aggregated on: 2025-11-10 16:26:02

Why This Matters A few years ago, data teams had to make a tough choice: the flexibility of a data lake or the reliability of a data warehouse. Now, the lakehouse architecture bridges that gap, combining cheap object storage with transactional guarantees, schema management, and even time travel. But here’s the catch — none of this works without a table format to organize the chaos of raw files. If you’ve ever tried to manage updates, deletes, or schema changes in a plain S3 bucket, you know the pain. Table formats like Apache Hudi, Delta Lake, and Apache Iceberg solve this by adding a metadata layer that turns files into structured, queryable tables. They all promise ACID transactions, schema evolution, and scalability, but they’re not interchangeable. The right choice depends on your workload, team, and long-term goals.

Regression Analysis for Time Series Data: Models and Applications

Aggregated on: 2025-11-10 15:26:02

The global big data and business analytics market is projected to grow to $961 billion by 2032. A substantial portion comprises analytics software, most of which supports time series regression analysis. This analytics method is widely used across business domains and is helpful in most forms of planning, forecasting, monitoring, and modeling. That’s exactly why it’s useful to be well-versed in forms of regression analytics and know how it can be used in your business area. In this blog post, you’ll find out about regression analysis of time series and its difference from standard regression analysis. Moreover, you’ll learn how to conduct a regression analysis in clear steps. You’ll also review business use cases of using data regression analysis, explore challenges and limitations, and find out whether this analysis method can work for your business.

AI-Driven Generative Design: Automating Architectural Layouts

Aggregated on: 2025-11-10 14:26:02

Architectural design historically has been a human-centric, iterative process: sketch, evaluate, refine. But as computational power and AI algorithms evolve, a new paradigm is emerging — generative design — where software assists in producing (or even automating) layout decisions. In an AI-driven generative design system, one does not just “draw” rooms or corridors; instead, one defines constraints (e.g., adjacency, circulation, lighting, structural rules), and the system proposes multiple candidate layouts, often optimizing for multiple objectives (area utilization, daylight access, egress paths, energy efficiency).

There’s No API for Operational Chaos: Lessons from Scaling Acquired SaaS

Aggregated on: 2025-11-10 13:26:02

The deal closed on Friday. By Monday, the acquired company’s CI/CD pipeline had triggered an outage during onboarding. No one knew where the deployment scripts were, and half the infrastructure was still named after an ex-employee’s dog. This is not edge case material — it is standard operating procedure in the world of permanent capital SaaS.

The Exponential Price of Neglecting Modern Web Application Testing

Aggregated on: 2025-11-10 12:26:02

The True Challenge of Modern Web App QA The complexity of today's web applications has fundamentally changed the nature of software quality assurance. We've moved far beyond the simple, static pages of the past. Today's applications are vast, intricate ecosystems defined by: Microservices and distributed architectures running in the cloud Components deployed at the edge Real-time data feeds and APIs from third parties User interfaces built with multiple, deeply nested layers Each of these interconnected components introduces unique quirks, hidden dependencies, and potential failure points. Despite this exponential increase in complexity, testing often remains an under-resourced afterthought — a simple box to tick just before a major release.

Kubernetes v1.34: Enabling Smarter Traffic Routing With PreferSameNode and PreferSameZone

Aggregated on: 2025-11-07 20:26:01

Kubernetes has steadily evolved into an industry standard for container orchestration, powering platforms from small developer clusters to hyperscale AI and data infrastructures. Every new release introduces features that not only make workloads easier to manage but also improve performance, cost efficiency, and resilience. With the v1.34 release, one of the standout enhancements is the introduction of traffic distribution preferences for Kubernetes Services. Specifically:

Beyond Dashboards: How Autonomous AI Agents Are Redefining Enterprise Analytics

Aggregated on: 2025-11-07 19:26:01

The field of data analytics is going through the biggest change since business intelligence platforms were first introduced. Agentic AI — "intelligent systems that perceive, reason, plan, and act on their own" — is changing the way businesses get information and make choices. Generative AI only responds to prompts, but agentic systems work on their own, going after complex goals with little human oversight. They also have persistent memory and can adapt to new situations. This change marks a shift from reactive analytics to proactive intelligence. Companies are moving away from static dashboards and manual analysis cycles in favor of self-driving systems that monitor data, find insights, and make decisions. The market is expected to be worth $196.6 billion by 2034, and 62% of executives expect a return on investment of more than 100%. This change is transitioning from small-scale tests to large-scale deployments, which significantly impact how businesses operate.

Prompt Engineering vs Context Engineering

Aggregated on: 2025-11-07 18:11:01

As AI integration becomes standard in modern applications, developers face a very critical decision: How do we effectively communicate with large language models (LLMs) to get reliable and accurate results? The answer lies in understanding two distinct but often confused approaches: prompt engineering and context engineering. While these terms are frequently used interchangeably, they represent fundamentally different strategies for working with AI systems. Understanding their differences is important for building robust and production-ready applications that leverage LLMs effectively.

Understanding Proxies and the Importance of Japanese Proxies in Modern Networking

Aggregated on: 2025-11-07 17:11:01

In the current digital age, which largely revolves around the use of the internet, privacy and security, as well as access to the whole world, are some of the main concerns for individuals and businesses. One of the most effective methods to achieve these objectives is the use of proxy servers. Proxy servers essentially change your IP address and reroute your online traffic. Many types of proxies are available, but Japanese proxies are considered to be the best because they are not only stable but also have very low latency and offer you excellent regional access.

How Does a Scrum Master Improve the Productivity of the Development Team?

Aggregated on: 2025-11-07 17:11:01

The role of a Scrum Master is to establish Scrum, and the Scrum Master is accountable for the Scrum Team’s effectiveness. Thus, it is quite tempting to ask how a Scrum Master can help improve the productivity of the development team. But, in a complex working environment like software development, productivity is often not the right measure to showcase all the complexities of software developers’ knowledge work. In simple working environments, productivity means a ratio of output to input. The traditional idea is to know how much is achieved (output) with a given amount of resources (inputs), largely in numbers, and the focus is on maximizing the output. That’s why, in traditional project management of software development projects, stakeholders evaluate the development team’s productivity based on the lines of code. Or, even today in Agile project management, stakeholders with a traditional mindset ask for the number of story points per iteration, known as Sprint Velocity.

Production-Grade React Project Structure: From Setup to Scale

Aggregated on: 2025-11-07 15:26:01

Do you struggle to organize your React project folder structures? The right folder organization does more than just keep things tidy. The development efficiency and team collaboration of your project depend on it. A solid folder structure makes the project look clean and speeds up scalability, collaboration, and debugging. The way developers organize files and components is vital to the long-term success of any React project. React applications are easier to scale, debug, and onboard new team members through a clean folder structure and clear system design. The best folder structure practices help developers find files and components quickly, which reduces the time spent searching through the code. The consistent folder structure of your application prevents chaos and confusion as it grows. Team members work together more smoothly, with fewer merge conflicts and improved productivity.

Laravel + Next.js Integration Guide (Real-World Setup, 2025)

Aggregated on: 2025-11-07 14:26:01

The goal is simple: build a secure app that uses SSR or SSG for SEO, CSRF-safe session cookies, clear CORS rules, and a smooth deploy to Vercel plus Forge or Vapour. By the end, readers will know how to wire Laravel routes and controllers, fetch from Next.js with credentials, handle CSRF and sessions, and deploy with confidence. A short troubleshooting list covers common 401, 419, and CORS issues so teams do not get stuck.

Understanding the Idle-Timer

Aggregated on: 2025-11-07 13:26:01

What is an Idle Timer? An idle timer is a software component that tracks user inactivity or idleness within an application or website. It measures the duration of time a user remains inactive—no mouse movements, keyboard inputs, or touch events—and triggers specific actions after a predefined idle period, such as logging out the user, showing a warning, or saving work. What Problems Does an Idle Timer Solve? Idle timers are essential in many scenarios, including:

Workload Identities: Bridging Infrastructure and Application Security

Aggregated on: 2025-11-07 12:26:01

Infrastructure security has long been about protecting networks, hosts, and cloud platforms. Application security focuses on securing APIs, data flows, and business logic to protect critical assets. Both approaches are critical, but they can’t provide complete protection on their own. When isolated from each other, there is a higher risk that attackers can exploit the security gaps in either layer, which is why workload identities should be employed to serve as a bridge that unifies both layers. The Two Security Worlds: Infrastructure vs. Application Infrastructure security is about protecting the foundation, including measures such as network segmentation, least-privilege identity and access management (IAM) roles, container isolation, and applying zero-trust principles at the infrastructure level. Application security works higher up in the stack, closer to data and user interactions. It deals with authentication, authorization, token management, session handling, and API protection.

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.