News Aggregator

CI/CD Is Not Enough: Stop Missing Test Failures With Intelligent Notifications

Aggregated on: 2025-09-04 11:14:48

The Visibility Gap in Enterprise Testing Modern test automation has matured. CI/CD pipelines are well-orchestrated, test coverage is high, and nightly regressions run like clockwork. But even with all this structure, one subtle problem still persists: nobody knows when things fail — at least not fast enough, or by the right people. Here’s how this usually plays out:

Developing a Nationwide Real-Time Telemetry Analytics Platform Using Google Cloud Platform and Apache Airflow

Aggregated on: 2025-09-03 20:29:48

In my tenure at TELUS, I was assigned a prominent project requiring substantial technical expertise: the development of a telemetry analytics platform that could analyze data in real-time from over 100,000 set-top boxes (STBs) deployed throughout Canada. The objective was not just about scale; it aimed to assist teams to make quicker operational decisions and enhance the experience for millions of customers. Initially, I recognized the outdated data infrastructure as a bottleneck, obstructing the data from reaching the teams who required it the most. This article portrays the methodologies we employed to modernize our infrastructure using Google Cloud Platform (GCP), Apache Airflow, and Infrastructure-as-Code tools to surmount the obstacles and deliver a future-proof solution. The Predicament: Ancient Bottlenecks and Unseen Black Spots Prior to this revamp, we predominantly relied on segregated and batch-oriented data pipelines incapable of supporting real-time diagnostics. Key concerns encompassed:

DSLs vs. Libraries: Evaluating Language Design in the GenAI Era

Aggregated on: 2025-09-03 19:29:48

Programming languages are the fundamental tools used to shape the digital world. Every developer has to choose at some point in their careers between general-purpose languages such as Python, Java, and C# and specialized domain-specific languages like SQL, CSS, or XAML. But with the evolution of AI the lines are getting blurred. We are observing shifts in not only how we write code but the definitions of productivity, maintainability, and innovation are beginning to change as well. As a result, the conventional trade-offs between DSLs and libraries are changing, and long-standing issues like expressiveness, integration complexity, and learning curves are being approached from new perspectives. The Traditional DSL vs Library Paradigm General-Purpose Languages (GPLs) are very versatile. They are packed with extensive libraries that allow developers to tackle problems across multiple domains. But this flexibility comes at the cost of writing more code and the need for significant domain knowledge to implement specialized solutions effectively.

Observability for the Invisible: Tracing Message Drops in Kafka Pipelines

Aggregated on: 2025-09-03 18:14:48

When an event drops silently in a distributed system, it is not a bug, it is an architectural blind spot. In high-scale messaging platforms, particularly those serving real-time APIs like WhatsApp Business or IoT command chains, telemetry failures are often mistaken for application errors. But the root cause lies deeper: observability gaps in event streams. This article explores how backend engineers and DevOps teams can detect, debug, and prevent message loss in Kafka-based streaming pipelines using tools like OpenTelemetry, Fluent Bit, Jaeger, and dead-letter queues. If your distributed messaging system handles millions of events, this guide outlines exactly how to make those events accountable.

Simple Efficient Spring/Kafka Datastreams

Aggregated on: 2025-09-03 17:14:48

I had the opportunity to work with Spring Cloud Data Flow streams and batches. The streams work in production and perform well. The main streams used Debezium to send the database deltas to Soap endpoints or provided Soap endpoints to write into the database. The events where send via Kafka. Spring Cloud Data Flow also provides a application to manage the streams and jobs. The streams are build with a data source and a data sink that are separate applications and are decoupled by the events send via Kafka. Stream 1 has a Debezium source and sends the database deltas via Kafka to the sink that transforms the event into a soap request to the application. Stream 2 receives a soap request from the application and sends an event to Kafka. The sink receives the event and creates the database entries for the event.

Understanding Zero-Copy

Aggregated on: 2025-09-03 16:14:48

In the realm of high-performance computing and network applications, efficient data handling is important. Traditional Input/Output (I/O) operations often involve redundant data copies, creating performance bottlenecks that can limit throughput and increase latency. Zero-copy is a powerful optimization technique that minimizes or eliminates these unnecessary data movements, leading to significant performance gains. Traditional Input/Output Path Consider a common scenario: an application needs to read a file from disk and transmit it over a network. In a traditional I/O model, this seemingly straightforward operation entails a series of data copies:

Understanding Apache Spark Join Types

Aggregated on: 2025-09-03 15:29:48

In this article, we are going to discuss three essential joins of Apache Spark. The data frame or table join operation is most commonly used for data transformations in Apache Spark. With Apache Spark, a developer can use joins to merge two or more data frames according to specific (sortable) keys. Writing a join operation has a straightforward syntax, but occasionally the inner workings are obscured. Apache Spark internal API suggests several algorithms for joins and selects one. A basic join operation could become costly if you do not know what these core algorithms are or which one Spark uses.

Container Security Essentials: From Images to Runtime Protection

Aggregated on: 2025-09-03 14:29:48

Container security is all about making sure you run an image that is exceptionally low in vulnerability and malware. I would love to say having zero vulnerabilities, but it is rarely possible in the real world. In the worst case, you at least want to address critical to medium vulnerabilities to have a good night's sleep and avoid potential compromise from bad actors. You could also think of container security like peeling an onion, where each layer adds resilience against potential threats. As part of this article, we will learn what the different steps are that we could take to increase the overall safety of the container infrastructure.

Cryptography Libraries on Ampere®

Aggregated on: 2025-09-03 13:44:48

Overview This white paper aims to provide the best-known practices for using open-source cryptography libraries on Ampere processors, including the Ampere® Altra® family and the AmpereOne® family of processors. Background Cryptography is the science of securing communication and data through mathematical techniques, ensuring confidentiality, integrity, and authenticity. It is widely used in web services, load balance proxies, databases, etc.

Why Zero Trust Is Not a Product but a Strategy You Can’t Ignore in 2025

Aggregated on: 2025-09-03 13:29:48

"We recently purchased a Zero Trust solution." A statement like that makes even the most seasoned security experts cringe. Zero Trust is a ubiquitous notion in 2025, appearing in product packaging, seminars, and sales presentations. However, the fundamental idea is still gravely misinterpreted. There is no such thing as buying Zero Trust. It's a way of thinking, a plan you follow, and a path you dedicate yourself to. In light of growing attack surfaces, heterogeneous workforces, and more complex threat actors, it is not only inefficient but also risky to approach Zero Trust as a checkbox.

File Systems <> Database: Full Circle

Aggregated on: 2025-09-03 12:14:48

File-based systems were the original data storage systems before the invention of database management systems (DBMS). Back in the 1970s, organizations manually stored data across servers in numerous files, such as flat files. These files have a fixed, rigid format and multiple copies of data stored for each department, resulting in data redundancy. These led to various challenges, especially data consistency, sharing, security, and retrieval. Analyzing these files was also challenging if we needed to join multiple files for one end-to-end record. As a result, file-based systems could not keep up with the changing data and innovations. With the invention of DBMS, data transactions comply with ACID properties (atomicity, consistency, isolation, durability), which allows for data consistency, integrity, recovery, and concurrency. In addition, today's advanced DBMS system provides disaster recovery, backup and restore, data searching, and data encryption and security. Even though the DBMS has evolved, due to the advancement of big data, cloud technologies, the Internet, social media, and advancing data formats, file storage is again a hot topic.

Toward Explainable AI (Part 6): Bridging Theory and Practice—What LIME Shows – and What It Leaves Out

Aggregated on: 2025-09-03 11:14:48

Series reminder: This series explores how explainability in AI helps build trust, ensure accountability, and align with real-world needs, from foundational principles to practical use cases. Previously, in Part V: A Hands-On Introduction to LIME: Explaining pneumonia detection step by step.

Stop Leaking Secrets: The Hidden Danger in Test Automation and How Vault Can Fix It

Aggregated on: 2025-09-02 20:14:48

Modern automation frameworks have come a long way—Playwright, Cypress, RestAssured, Cucumber, and Selenium enable teams to run sophisticated end-to-end validations across browsers and services. But under all that progress lies a risk that's still alarmingly common: secrets hardcoded into test code or environment files. These aren’t just theoretical risks. In one large enterprise, a regression test suite for an internal app had a credentials file committed in plain text six months prior. The automation “just worked,” but the secrets were not only stored in .env files—they were also printed to Jenkins console logs, referenced in Postman collections, and distributed across multiple forks. No one noticed until a security audit flagged it.

Technical Deep Dive: Scaling GenAI-Enhanced SBOM Analysis from Trivy Fix to Enterprise DevSecOps

Aggregated on: 2025-09-02 19:14:48

This article demonstrates how a critical Trivy SBOM generation fix (PR #9224) can be scaled into an enterprise GenAI-powered platform, delivering comprehensive DevSecOps automation and millions in cost savings. We will explore the technical implementation from core dependency resolution improvements to enterprise-scale AI-driven vulnerability intelligence. The Foundation: Cross-Result Dependency Resolution in Trivy Problem Statement: Incomplete SBOM Dependency Graphs Original Issue: SBOM dependency graph plotting was missing dependencies that existed across different scan results, particularly in multimodule projects where module B depends on a shared library from module A. The root cause was that dependency resolution only examined individual results, not all results in the report.

Monitoring Java Microservices on EKS Using New Relic APM and Kubernetes Metrics

Aggregated on: 2025-09-02 18:14:47

Amazon EKS makes running containerized applications easier, but it doesn’t give you automatic visibility into JVM internals like memory usage or garbage collection. For Java applications, observability requires two levels of integration: Cluster-level monitoring for pods, nodes, and deployments JVM-level APM instrumentation for heap, GC, threads, latency, etc. New Relic provides both via Helm for infrastructure metrics, and a lightweight Java agent for full JVM observability.

Modernizing Oracle Workloads With Real-Time Analytics

Aggregated on: 2025-09-02 17:14:48

On July 23, 2025, AWS announced Amazon Relational Database Service (Amazon RDS) for Oracle zero-ETL integration with Amazon Redshift, enabling near real-time analytics and machine learning (ML) on petabytes of transactional data. With this launch, you can create multiple zero-ETL integrations from a single Amazon RDS Oracle database, and you can apply data filtering for each integration to include or exclude specific databases and tables, tailoring replication to your needs. You can also use AWS CloudFormation to automate the configuration and deployment of resources needed for zero-ETL integration. Zero-ETL integrations make it simpler to analyze data from Amazon RDS to Amazon Redshift by removing the need for you to build and manage complex data pipelines and helping you derive holistic insights across many applications. Within seconds of data being written to Amazon RDS for Oracle, the data is replicated to Amazon Redshift. Using zero-ETL, you can enhance data analysis on near-real-time data with the rich analytics capabilities of Amazon Redshift, including integrated ML, Spark support, and materialized views.

Prototype for a Java Database Application With REST and Security

Aggregated on: 2025-09-02 16:29:48

Many times, while developing at work, I needed a template for a simple application from which to start adding specific code for the project at hand. In this article, I will create a simple Java application that connects to a database, exposes a few rest endpoints and secures those endpoints with role based access.

Building a Card Layout Using CSS Subgrid

Aggregated on: 2025-09-02 15:14:48

Creating clean, well-aligned card layouts is a common task in web development. In this tutorial, I’ll walk you through building a grid of four cards per row. Each card contains several content blocks — a title, image, price, bullet point list, and a call-to-action (CTA) button — aligned horizontally within the card using CSS Grid and the powerful CSS Subgrid feature. What You’ll Build A card grid layout (max of 4 cards per row). Each card contains multiple content blocks aligned horizontally. Use of CSS Grid for the overall layout. Use of CSS Subgrid for inner alignment of content inside each card. Why Use CSS Subgrid? CSS Subgrid is a relatively new feature that allows a nested grid to inherit the track sizing of its parent grid. This means you can align inner content perfectly with the outer grid without manually calculating or duplicating track sizes.

Autonomous QA Testing With Playwright, LangGraph, and GPT-4o on AWS

Aggregated on: 2025-09-02 14:14:47

Software testing has come a long way — from manual test cases and record-playback tools to modern CI-integrated test automation frameworks. But in an era of continuous delivery, microservices, and fast-changing UIs, even traditional automation struggles to keep up. Writing and maintaining test scripts manually has become a bottleneck, especially when rapid iteration is the norm. The future of testing is autonomous — where tests are not only executed automatically but are written, adapted, and self-corrected by intelligent agents.

PII Leakage Detection and Measuring the Accuracy of Reports and Statements Using Machine Learning

Aggregated on: 2025-09-02 13:14:47

Reports, invoices, and statements play a vital role in sharing weekly, monthly, and annual usage data and its trends with end-users on day-to-day activities. Starting from utility usage, financial trends, credit statements, and medical data are shared with humans in the form of reports and statements, both in electronic and paper formats. These documents contain PII, personally identifiable information, of users, including address, phone number, account numbers, medical history, and Social Security numbers. Data is also represented in tables, a wide variety of charts, and graphs for an enhanced user experience. Problem Organizations and institutions pay fines, penalties, and work on settlements, often now due to PII data breaches and inaccurate data in reports. The majority of organizations use a third-party vendor to generate and send out these statements to their customers. The chances of misdelivery or sharing inaccurate information are relatively high. Using a visual language model and machine learning techniques, we can eliminate the data breach by detecting and fixing it.

Mastering Prompt Engineering for Generative AI

Aggregated on: 2025-09-02 12:14:47

Prompt engineering is rapidly becoming a foundational skill in working with large language models (LLMs) and generative AI. As LLMs permeate software systems-powering chatbots, coding assistants, research agents, and more. the difference between a generic, shallow response and a nuanced, high-value output often comes down to how the model is prompted. For developers, product teams, and engineering leaders, understanding and leveraging state-of-the-art prompt strategies have tangible impacts on product relevance, accuracy, and user experience. This guide explores advanced prompting techniques, from Chain of Thought (CoT) and few-shot learning to retrieval-augmented generation (RAG), and provides practical advice for integrating them into real-world workflows.

Build Smarter Next-Gen AI Apps: A Step-by-Step LangChain v0.3+ Guide

Aggregated on: 2025-09-02 11:14:47

The New Era of LLM Apps In the last year, AI development has shifted rapidly from simple demos to robust, feature-rich applications. At the heart of this movement is LangChain, the open-source toolkit that makes it easier than ever to plug large language models into real-world data, tools, and workflows. If you've ever wanted to move beyond the standard chatbot — say, build a custom app that can analyze documents, retrieve live data, and even call external APIs — the new LangChain has you covered. Companies like Morningstar are already using LangChain to build their Intelligence Engine, allowing analysts to query massive research databases in natural language. Meanwhile, enterprises across industries report deployment cycles that are 3-5× faster when using LangChain compared to building from scratch.

How to Use ALB as a Firewall in IBM Cloud

Aggregated on: 2025-09-01 20:29:47

Do you have a use case where you want to implement a network firewall in IBM Cloud VPC that filters traffic based on hostname? For example, you may want to allow connections only to www.microsoft.com and www.apple.com, while blocking access to all other destinations. Currently, IBM Cloud does not provide a managed firewall service. However, it does support a bring-your-own-firewall approach with vendors such as Fortinet or Juniper, though customers are responsible for deploying and managing these solutions.

From CloudWatch to Cost Watch: Cutting Observability Costs With Vector

Aggregated on: 2025-09-01 19:29:47

Introduction In modern cloud environments, traditional approaches for storing logs in isolated systems have become inadequate. As distributed software systems become more common, where different components run across multiple services and regions, it is essential to continuously collect and forward both system and application logs to a centralized location for in-depth analysis. These logs play an important role in debugging, performance monitoring, and ensuring the overall health and reliability of the infrastructure. In the AWS cloud environment, many such components of the distributed software system are still hosted on Amazon EC2 instances and use an agent-based approach to transmit system and application logs to a centralized service, where this data is ingested and stored for further use by observability platforms. While observability improves operational insight and system reliability, it also increases the cost of data ingestion and long-term storage. Therefore, organizations must maintain a careful balance between observability depth and the financial sustainability of the platform. Selecting a resilient, scalable, and cost-effective ingestion and storage solution has become an important element of any observability strategy, especially when the platform is being used at enterprise scale.

The AI Co-Pilot: How to Lead When Your Team's Best Player Is a Machine

Aggregated on: 2025-09-01 18:14:47

It’s happening in stand-ups and one-on-ones everywhere. An engineer explains how they cleared a mountain of tickets over the weekend. "How'd you get it all done?" you ask. The answer is a quiet admission, almost a confession: "Uh, I was using Copilot." For years, we've managed teams of people. Now, we're managing teams of people who have an incredibly productive, sometimes inscrutable, and tireless new partner. This AI co-pilot can write boilerplate code in seconds, translate complex logic into a new language, and even suggest fixes for bugs that would have taken a junior engineer half a day to track down.

Exploring QtJambi: A Java Wrapper for Qt GUI Development—Challenges and Insights

Aggregated on: 2025-09-01 17:14:47

I recently experimented with QtJambi, a Java wrapper for the well-known Qt C++ library used to build GUIs. Here are some initial thoughts, remarks and observations: Building a QtJambi project can be somewhat challenging. It requires installing the Qt framework, configuring system paths to Qt’s native libraries, and setting proper JVM options. Although it is possible to bundle native libraries within the wrapper JARs, I haven’t tried this yet. The overall development approach is clean and straightforward. You create windows or dialogs, add layouts, place widgets (components or controls) into those layouts, configure widgets and then display the window or dialog to the user. This model should feel familiar to anyone with GUI experience. Diving deeper, QtJambi can become quite complex, comparable to usual Java Swing development. The API sometimes feels overly abstracted with many layers that could potentially be simplified. There is an abundance of overloaded methods and constructors, which can make it difficult to decide which ones to use. For example, the QShortcut class has 34 different constructors. This likely comes from a direct and not fully optimized mapping from the C++ Qt API. Like Swing, QtJambi is not thread-safe. All GUI updates must occur on the QtJambi UI thread only. Ignoring this can cause crashes, not just improper UI refresh like in Swing. There is no code reuse between Java Swing and QtJambi. Even concepts that appear close and reusable are not shared. QtJambi is essentially a projection of C++ Qt’s architecture and design patterns into Java, so learning it from scratch is necessary even for experienced Swing developers. Using AI tools to learn QtJambi can be tricky. AI often mixes Java Swing concepts with QtJambi, resulting in code that won’t compile. It can also confuse Qt’s C++ idioms when translating them directly to Java, which doesn’t always fit. Despite being a native wrapper, QtJambi has some integration challenges, especially on macOS. For example, handling the application Quit event works differently and only catching window-close events behaves properly out of the box. In contrast, native Java QuitHandler support is easier and more reliable there, but it doesn't work with QtJambi. Mixing Java AWT with QtJambi is problematic. This may leads to odd behaviors or crashes. The java.awt.Desktop class also does not function in this context. If you want a some times challenging Java GUI framework with crashes and quirks, QtJambi fits the bill! It brings a lot of power but also some of complexity and instability compared to standard Java UI options. There is a GUI builder that works with Qt, but it is possible to use its designs in QtJambi, generating source code or loading designs at runtime. The only issue: the cost starts from $600 per year for small businesses to >$5,000 per year for larger companies. Notable Applications Built With QtJambi Notable applications built with QtJambi are few. One example is the Interactive Brokers desktop trading platform (IBKR Desktop), which uses QtJambi for its user interface.

Building a Rate Limiter and Throttling Layer Using Spring Boot and Redis

Aggregated on: 2025-09-01 16:14:47

Imagine your backend API is stable, performant and deployed to production. Then someone writes a buggy frontend loop or a bot goes rogue, and suddenly your endpoint gets hit 100 times a second. That’s how your server’s CPU spikes, your database becomes overloaded, response times shoot up, and eventually your application turns unusable for real users. Even well-architected systems can crumble under this kind of stress, which leads to unhappy customers and costly incidents.

Top Metrics to Watch in Kubernetes

Aggregated on: 2025-09-01 15:14:47

Introduction If you’ve ever found yourself knee-deep in a Kubernetes incident, watching a production microservice fail with mysterious 5xx errors, you know the drill: alerts are firing, dashboards are lit up like a Christmas tree, and your team is scrambling to make sense of a flood of metrics across every layer of the stack. It’s not a question of if this happens-it’s when. In that high-pressure moment, the true challenge isn’t just debugging-it’s knowing where to look. For seasoned SREs and technical founders who live and breathe Kubernetes, the ability to quickly zero in on the right signals can make the difference between a five-minute fix and a five-hour outage.

Enhancing Productivity With RAG-Based GenAI Solutions

Aggregated on: 2025-09-01 14:14:47

So what exactly is RAG? In simple terms, it stands for retrieval-augmented generation. Let us focus on these two aspects: retrieval and generation. With standard generative AI (GenAI), you provide a prompt, and a GenAI application would use a large language model to come up with a suitable response for the prompt. Now, imagine an application that can retrieve information from various sources and then generate a response based on the retrieved information. That is exactly how a RAG GenAI works. It provides context to the generated example. Let us explore this further with an example. If we ask something like "What is the best way to back up my customer database?" to a GenAI application, it would probably respond with some generic stuff. I would not know the details of the customer database that I am talking about. Now, suppose I have a design document with all the details. It has a section on data stores and explicitly lists out the customer database that is hosted on Amazon DynamoDB. The design document is uploaded to my organization’s SharePoint. So, the application will first do a retrieval of contextual information from SharePoint, augment the prompt with retrieved information, and then generate a response based on that. In this case, the application will provide strategies for backing up a DynamoDB database and direct me to the relevant sections in my design document.

Code That Isn’t Afraid of Change

Aggregated on: 2025-09-01 13:14:47

In this article, you’ll find some personal observations and tips on how to keep a project alive and healthy over the years. No illusions of omniscience. With a touch of healthy cynicism. My personal experience is in building services as part of product development. In this article, I’m talking specifically about that kind of development–not about creating libraries, frameworks, databases, or other wonderful things. Nor am I touching on project-based development, where code is written to order and then handed off to others for maintenance.

The Statistical AI Parrot in Your Sprint

Aggregated on: 2025-09-01 12:29:47

TL; DR: The AI Parrot in the Room Your LLM tool doesn’t think. It’s a statistical AI parrot: sophisticated and trained on millions of conversations — but still a parrot. Teams that fail with AI either don’t understand this or act as if it doesn’t matter. Both mistakes are costly. The uncomfortable truth in Agile product development isn’t that AI will replace your team (it won’t) or that it’s useless hype (it isn’t). Most teams use these tools on problems that need contextual judgment, then accept outputs without the critical thinking Agile demands.

Toward Explainable AI (Part 5): Bridging Theory and Practice—A Hands-On Introduction to LIME

Aggregated on: 2025-09-01 11:14:47

Series reminder: This series explores how explainability in AI helps build trust, ensure accountability, and align with real-world needs, from foundational principles to practical use cases. Previously, in Part IV: Beyond Explainability: What Else Is Needed: Governance, limits, and the need for operational frameworks.

Why It’s Time to Reevaluate Quality Control Methods in Data Labeling

Aggregated on: 2025-08-29 20:29:45

What if the foundation of your AI models is built on flawed data without you knowing? The era of AI data labeling has undergone a dramatic transformation. What once involved straightforward tasks, such as answering “Is there a cat in this image?” or drawing bounding boxes around clearly defined objects, now demands sophisticated data preparation. Modern data labeling is far more complex: multi-modal datasets require deep semantic understanding, subjective judgments vary across cultures, and edge cases necessitate contextual understanding. Traditional quality control frameworks, designed for simpler, more objective labeling tasks, are no longer adequate to meet these challenges.

Implementing Write-Through Cache for Real-Time Data Processing: A Scalable Approach

Aggregated on: 2025-08-29 19:29:45

Real-time data processing systems often struggle with balancing performance and data consistency when handling high volumes of transactions. This article explores how a write-through local cache can optimize performance. Introduction to Write-Through Caches A write-through cache is a caching strategy where data is written to both the cache and the backing store simultaneously. This approach ensures that the cache always contains the most recent data while maintaining consistency with the underlying data store.

The Death of Static Rules: Making Microservices Smart, Flexible and Easy to Change

Aggregated on: 2025-08-29 18:14:45

Hey, team! Lately I have hit a wall, my microservices are so dominated with hardcoded rules that adjusting even the smallest nuance in policy was like disarming a bomb. I'm going to take you on my journey from messy if/else trees to clean, policy-driven microservices that update themselves (no redeploys). This will include every step from zero (no experience required) to hero, as well as some real-world examples, some questions for you to ponder and ideas you can use today. Let's go! What’s Wrong With Hardcoded Rules? A Simple Example—and Why It Sucks Let's say you are building an e-commerce checkout service. The service needs to charge a small surcharge when customers are located in certain countries. So, you write:

Keep Your Search Cluster Fit: Essential Health Checks to Keep Elasticsearch Healthy

Aggregated on: 2025-08-29 17:14:45

Elasticsearch (ES) is a powerful and distributed search and analytics engine, widely adopted for full-text search, logging, metrics, and real-time analytics. As the cornerstone of many data-driven systems, maintaining Elasticsearch’s health is crucial to ensure continuous availability, performance, and data integrity. A degraded or failing ES cluster can disrupt mission-critical applications, increase latency, or even cause data loss. To keep your Elasticsearch environment running smoothly, regular health checks must be conducted. These checks help detect early warning signs—such as disk saturation, unbalanced shards, or failed nodes before they escalate into critical failures. However, performing these tasks manually can be time-consuming and error-prone, especially in production environments with many nodes and indices.

Integration Testing AI Prompts With Ollama and Spring TestContainers

Aggregated on: 2025-08-29 16:14:45

AI features are becoming common in modern applications. If your Spring Boot app uses large language models (LLMs), it’s important to test how those models respond to real prompts. This helps you catch issues early and keeps your app reliable. In this article, you’ll learn how to write integration tests for AI prompts using Spring TestContainers and Ollama. You’ll see how to set up your environment, write prompt tests, and apply good testing practices - all using standard JUnit and Spring Boot.

Implementing Budget Policies and Budget Limits on Databricks

Aggregated on: 2025-08-29 15:14:45

This guide walks us through the steps to implement Budget Policies and Budget Policy limits on Serverless Compute in Databricks to effectively and accurately compute the costs incurred for compute usage. This guide covers step by step process of the implementation on the data platform to monitor and account for the cost incurred effectively. Pre-Requisites Databricks Admin access to set policies, view usage, manage tokens Cluster Policy enabled to restrict compute types, enforce limits Tags in place for team/project-level cost tracking REST API/token access for automation and enforcement Reporting tools to visualize and alert on usage Communication plan to ensure user awareness and adoption Introduction Databricks becomes central to analytics and AI pipelines, it's crucial to balance performance with cost control. Serverless compute simplifies scalability, but without budget policies and usage limits, costs can spiral.

Tuples and Records (Part 1): What They Mean for JavaScript Performance and Predictability

Aggregated on: 2025-08-29 14:14:45

JavaScript continually evolves to address modern development needs. Its latest updates often reflect trends in functional programming and immutable data handling. Two upcoming additions to the language, Tuples and Records, aim to simplify immutability while enhancing efficiency and developer experience. This article delves into these new features, discussing their purpose, syntax, benefits, and use cases.

Development of System Configuration Management: Handling Exclusive Configurations and Associated Templates

Aggregated on: 2025-08-29 13:14:45

Series Overview This article is Part 2.3 of a multi-part series: "Development of system configuration management." The complete series:

MCP for Agentic Systems: The Missing Protocol for Autonomous AI

Aggregated on: 2025-08-29 12:29:45

Introduction: Why Agentic Systems Need MCP Model Context Protocol (MCP) is a standardized communication framework specifically designed to manage complex, stateful interactions between AI agents and backend infrastructure. If you've moved beyond simple LLM completions and are building agentic applications, you've likely experienced the complexity. An agent, unlike a basic chatbot, perceives, reasons, plans, and acts dynamically. Managing its evolving state — plans, internal reasoning, tool usage history, and environmental understanding — rapidly becomes complex, brittle, and difficult to scale using traditional REST APIs. MCP provides a structured solution, centralizing state management and enabling clean, maintainable agent implementations.

Toward Explainable AI (Part 4): Bridging Theory and Practice—Beyond Explainability, What Else Is Needed

Aggregated on: 2025-08-29 11:29:45

Series reminder: This series explores how explainability in AI helps build trust, ensure accountability, and align with real-world needs, from foundational principles to practical use cases. Previously, in Part III: The Two Major Categories of Explainable AI Techniques. How XAI methods help open the black box.

Development of System Configuration Management: Building the CLI and API

Aggregated on: 2025-08-28 20:14:45

Series Overview This article is Part 2.2 of a multi-part series: "Development of system configuration management." The complete series:

Beyond Keys and Values: Structuring Data in Redis

Aggregated on: 2025-08-28 19:14:45

Redis is a well known, open source, in-memory data store. By design, it prioritizes speed, making reads exceptionally faster. Most of us are familiar with various caching techniques such as Cache-Aside, Write-Through, Write-Behind, Read-Through etc.

Building Recommendation Engines With AI and SQL

Aggregated on: 2025-08-28 18:29:45

Providing personalized experiences is key to engaging users and driving business growth. From e-commerce giants suggesting products you'll love to streaming services curating your next binge-watch, recommendation engines are at the heart of enhanced user engagement and satisfaction. Recommendation engines, powered by Artificial Intelligence (AI) and leveraging the power of Big Data, are at the forefront of this revolution. In my last article, we explored how analytics is evolving with the integration of ML and SQL. Here, I want to talk about how Artificial Intelligence (AI) and Big Data / SQL can be combined to build powerful recommendation engines, leveraging your existing data infrastructure to deliver tailored insights.

Practical Guide to Snowflake Performance Tuning With SQL and AI Enhancements

Aggregated on: 2025-08-28 17:29:45

If you're like many data practitioners who use Snowflake, odds are you've had moments when your queries got slow… at precisely the time everyone was desperate to get answers fast. Or maybe your compute expenses were through the roof during peak times, leaving you wondering: "How do I make Snowflake faster and smarter without going broke?" I've been there. And after so many performance tuning sessions, trawling slow queries, crawling QUERY_HISTORY, and analyzing patterns across multiple environments, I've gathered 13 battle-tested techniques that can really make a difference to your Snowflake performance, saving time, cutting costs, and improving overall query efficiency.

Designing Scalable Ingestion and Access Layers for Policy and Enforcement Data

Aggregated on: 2025-08-28 16:14:45

In trust and safety systems, the ability to access real-time signals — such as risk scores, policy flags, or enforcement states — is critical for preventing abuse and enabling secure, automated decision-making. These systems must ingest and expose high-volume data at low latency, often to serve machine learning models, rules engines, or enforcement workflows. Traditional database systems often fail to meet the low-latency, high-throughput demands of these workloads. In response, platforms are increasingly combining Apache Spark for scalable data ingestion with in-memory data grids to support sub-second access to mission-critical data.

How to Understand Emergent Behavior in Agentic AI: Chaos or Intelligence?

Aggregated on: 2025-08-28 15:14:45

Introduction: The Emergence Dilemma Emergent behaviour in agentic AI is quickly becoming one of the most intriguing phenomena in modern software systems. It refers to the way unexpected, often complex behaviours can arise from relatively simple components, especially when those components are allowed to interact in open-ended environments. In the case of language model-driven agents, we’re seeing systems that do far more than just respond to prompts: they plan, adapt, use tools, store context, and even come up with solutions that weren’t directly requested. Frameworks like LangChain’s ReAct pattern, Auto-GPT’s recursive planning loops, and CrewAI’s multi-agent structures have accelerated this trend. Developers report agents that decompose tasks on their own, generate internal workflows, or autonomously call API seven when none of these actions were explicitly part of the prompt. These behaviours emerge not from deterministic logic, but from probabilistic reasoning shaped by context, memory, and tool interactions.

Cry and Authenticate How AI is Changing Security

Aggregated on: 2025-08-28 14:14:45

I constantly have thoughts buzzing in my head, and I need to throw them somewhere or they'll just fly away. So I thought I’d write a few articles about how our lives are becoming more like the movies and games we grew up with. Let’s get started. Today, let’s talk about security and all the issues that come with it. Do you remember that you always use a billion passwords to access your bank, your apps, your services, your entertainment, and so on? There's two-factor authentication and all that jazz, but emails and accounts still get hacked, stolen, and used in ways we don't understand. It’s unfair, right?

From Runtime Fires to Pre‑Flight Control: A Gatekeeper Model for Spark SQL

Aggregated on: 2025-08-28 13:29:45

A Quick Back‑Story It was 2 a.m., the cluster dashboard was glowing red, and the only thing separating me from a full night’s sleep was a stray comma in a user‑supplied query. Spark had happily fired up a handful of executors before realising the SQL was garbage. Cue wasted compute, angry Slack pings, and a small dent in our budget. After the third “why is the job queue jammed again?” post‑mortem, I decided to build a gatekeeper: something that could shout “Stop!” the moment a query looked fishy — before Spark touched a single core.