News Aggregator

Designing a Java Connector for Software Integrations

Aggregated on: 2025-05-15 17:13:41

IT systems need integration to achieve seamless data flow alongside enhanced operational efficiency. The expansion of businesses creates independent operational systems that restrict performance through object isolation and restrict time-sensitive information acquisition. The system's functions become oriented for better workflow coordination, which minimizes both repetition and creates unified operations. The creation of such connections necessitates comprehensive knowledge about technical and business needs, which include format patterns along with protocol standards. Java provides robust libraries alongside outstanding frameworks that lead organizations to choose Java when building scalable, customized solutions. A strategically developed connector fulfills present requirements while enabling future technology adjustments, thus becoming a sustainable factor that supports successful IT system integration.

Integration Isn’t a Task — It’s an Architectural Discipline

Aggregated on: 2025-05-15 16:13:41

Let’s talk about integration. Not the glossy vendor slide-deck version, but the messy, deeply architectural reality of connecting systems in the enterprise. Despite all our advances in tooling and frameworks, the way many organizations approach integration still hasn’t changed. Too often, we default to short-term fixes — point-to-point links, overstuffed middleware, or bespoke connectors — because they’re “fast.” But that speed comes at a price: brittle systems, tight coupling, and long-term technical debt that can paralyze change.

Vibe Coding With GitHub Copilot: Optimizing API Performance in Fintech Microservices

Aggregated on: 2025-05-15 15:28:41

In fintech, APIs power everything. Starting from payments to trading to real-time customer experiences, the API takes care of everything. Performance isn't optional, but it's critical for user trust and business success. As a fintech API and cloud optimization expert, I constantly face the challenge of balancing quick development with high performance. When Microsoft announced GitHub Copilot for free, I asked myself a real-world question: Can GitHub Copilot go beyond writing boilerplate code and help optimize fintech Microservice APIs?

Memory Leak Due to Time-Taking finalize() Method

Aggregated on: 2025-05-15 14:28:41

All objects in Java implicitly inherit from java.lang.Object. This class contains multiple methods, which can be overridden in the child class. One such overridable method is finalize(). Before objects get garbage collected from memory, the JVM will execute the finalize(). So, if you need to close any resources that were opened in the class (like backend connections, Files…), it can be done in this method.

Customer 360: Fraud Detection in Fintech With PySpark and ML

Aggregated on: 2025-05-15 13:13:41

Every bank uses Customer 360 to maintain its customer records in a unified way, and it can also be used for fraud detection. What Is Customer 360? Customer 360 is like creating a complete picture of a customer by pulling together all the data you have about them — think of it as a "comprehensive profile." Imagine a bank with data from accounts, transactions, and customer service calls. Instead of having different/diverse data of the same customer, Customer 360 links them to say, “ This data in various ways belongs to customer John Doe.” It helps businesses understand customers better, personalize services, and figure out customer data patterns.

Mastering Advanced Aggregations in Spark SQL

Aggregated on: 2025-05-15 11:13:41

In data analytics, efficiently aggregating large datasets is a basic need. For example, when working with retail inventory data, tracking products shipped to stores each month, the standard GROUP BY clause in SQL can handle basic aggregations. However, it falls short when you need multiple levels of aggregation in a single query. This is where Spark SQL’s advanced GROUP BY extensions, GROUPING SETS, ROLLUP, and CUBE, come into the picture to compute multiple groupings efficiently.

Cosmos DB Disaster Recovery: Multi-Region Write Pitfalls and How to Evade Them

Aggregated on: 2025-05-14 22:28:40

Introduction Azure Cosmos DB is an excellent global distributed, multi-model database service for high availability, low-latency access, and straightforward scalability. One of its most prominent characteristics is multi-region writes, whereby your applications write to the nearest regional replica, which greatly boosts performance and resilience. But here's the catch: enabling multi-region writes also introduces new challenges, especially when you're architecting for disaster recovery (DR). Without careful planning, you can end up with data conflicts, unplanned downtime, or even data loss.

Developers Beware: Slopsquatting and Vibe Coding Can Increase Risk of AI-Powered Attacks

Aggregated on: 2025-05-14 21:43:41

Security researchers and developers are raising alarms over “slopsquatting,” a new form of supply chain attack that leverages AI-generated misinformation commonly known as hallucinations. As developers increasingly rely on coding tools like GitHub Copilot, ChatGPT, and DeepSeek, attackers are exploiting AI’s tendency to invent software packages, tricking users into downloading malicious content. What is Slopsquatting? The term slopsquatting was originally coined by Seth Larson, a developer with the Python Software Foundation, and later popularized by tech security researcher Andrew Nesbitt. It refers to cases where attackers register software packages that don’t actually exist, but are mistakenly suggested by AI tools; once live, these fake packages can contain harmful code.

FIPS 140-3: The Security Standard That Protects Our Federal Data

Aggregated on: 2025-05-14 21:28:40

FIPS (Federal Information Processing Standards) [1] defines a set of public security standards developed by NIST (National Institute of Standards and Technology) [2] that govern the security requirements for cryptographic modules used in government systems. FIPS 140-3 is the latest federal security standard, which includes state-of-the-art protection for deployment environments, such as the cloud, where the system could potentially be physically accessed by untrusted parties. Why is this important (or even relevant) for common (non-federal) workloads? Different security frameworks cater to specific domains or regions, focusing on safeguarding sensitive individual data used in those domains (e.g., name, age, and medical history in the healthcare domain or name, credit, loan, and tax records in the financial domain).

Why Database Migrations Take Months and How to Speed Them Up

Aggregated on: 2025-05-14 20:28:40

Today, where information is unequivocally the cornerstone of everything and a core competitive advantage, our databases often hold our data hostage. It's 2025, yet it's not uncommon for a database migration project to span many months, even when the migration is considered like-for-like (e.g., between MongoDB and Cosmos DB with MongoDB API). These long projects take everyone's attention, create uncertainty, and slow down developer productivity.

MySQL to PostgreSQL Database Migration: A Practical Case Study

Aggregated on: 2025-05-14 19:13:40

Migrating a MySQL database to PostgreSQL is a complex process that involves schema conversion, data transfer, query optimization, and performance tuning. In this case study, we document a real-world migration of e-commerce platform's database from MySQL 8.0 to PostgreSQL 15. The study includes performance benchmarks before and after migration. Project Overview Industry E-commerce Database Size ~120 GB Number of Tables 200+ Queries per Second (QPS) ~350 Replication Setup MySQL Master-Slave is switched to PostgreSQL Streaming Replication Primary Use Case OLTP (Online Transaction Processing) Goal Improve read-heavy performance, complex query execution and JSON processing Pre-Migration Performance Benchmarks Before migration, we collected key performance metrics on MySQL 8.0 using sysbench and EXPLAIN ANALYZE.

Understanding IEEE 802.11(Wi-Fi) Encryption and Authentication: Write Your Own Custom Packet Sniffer

Aggregated on: 2025-05-14 18:43:40

Introduction As of 2023, it’s estimated that 42 billion cumulative Wi-Fi enabled devices have been shipped (Wi-Fi® by the Numbers: Technology Momentum in 2023, n.d.). Every new device adds to the increasing wireless attack surface, and it's important for anyone working in the security software industry to have a basic understanding of how wireless networks encrypt traffic and authenticate users. In this article, we’ll go through the 4 currently supported and ratified security standards for Wi-Fi networks. We’ll also look at the IEEE 802.11 packet and header structure. Then we will look at a C program to write your custom packet sniffer linking with libpcap. We will also look at a real-life packet capture using Wireshark GUI. Outline: Common encryption and auth supported by IEEE 802.11 standards <- Theoretical Foundation IEEE 802.11 mac header, frame types, Information elements <- Theoretical Foundation Custom C program linked with libpcap to sniff packets <- Practical Application Wireshark based sniffing of nearby APs <- Practical Application Common Encryption and Authentication Methods Supported by the Standards WEP (1997), WPA (2003), WPA2(2004), WPA3 (2018) are the 4 officially ratified IEEE 802.11 security standards by the Wi-Fi Alliance. WPA4 is expected to be rolled out with Wi-Fi 7 but no standard has been formally ratified.

Unlocking AI Coding Assistants: Generate Unit Tests

Aggregated on: 2025-05-14 17:28:40

In this part of this series, you will try to create unit tests for a Spring Boot application using an AI coding assistant. The goal is not merely to create working unit tests, but to create qualitative unit tests. Enjoy! Introduction You will try to generate unit tests for a basic Spring Boot application with the help of an AI coding assistant. The responses are evaluated, and different techniques are applied, which can be used to improve the responses when necessary. This blog is part of a series; the previous parts can be read here:

How To Develop a Truly Performant Mobile Application in 2025: A Case for Android

Aggregated on: 2025-05-14 16:28:40

To develop a high-performance mobile application, you need functional code—and a responsive, intuitive user experience. As a mobile developer who has spent years working with Android, I’ve learned that performance optimization is both art and science. Balancing technical precision with user-centric design has become a key principle in my career. Performance is Key Today's users expect quick responses and seamless UIs as the standard, not the exception. Delays of even a few seconds often leads users to stop using the application altogether. I still remember, early on in my career, how milliseconds of latency hurt user engagement. That understanding formed my belief that performance optimization is not about technical metrics but rather about creating an experience that’s "invisible." This looks as if an app just works, without the user realizing the complexity underneath.

Beyond Code Coverage: A Risk-Driven Revolution in Software Testing With Machine Learning

Aggregated on: 2025-05-14 15:28:40

Modern systems require more than high code coverage because they are complex and interconnected. Through machine learning development, companies can create advanced tools that link risk-based planning methods to enhance their software testing outcomes. With the focus on the possibility of failure and its effects, risk-based testing provides both improved efficiency and better effectiveness. RBT selects its execution focus on core features and often modified code and components known for producing defects.

A Simple, Convenience Package for the Azure Cosmos DB Go SDK

Aggregated on: 2025-05-14 14:28:40

When using the Go SDK for the Azure Cosmos DB NoSQL API, I often find myself writing boilerplate code for various operations. This includes database/container operations, querying, and more. The cosmosdb-go-sdk-helper (I know, not a great name!) is a package with convenience functions for some of these tasks. In this blog post, I will go over the packages in the repository with examples on how (and when) you can use them. It's early days for this project, but I hope to keep adding to it gradually.

Intro to RAG: Foundations of Retrieval Augmented Generation, Part 1

Aggregated on: 2025-05-14 13:13:40

Retrieval-augmented generation (RAG) may sound complex, but it accurately represents the process of the system. RAG is a method that enhances the capabilities of large language models (LLMs) by integrating them with external knowledge sources. Each term represents a piece of the puzzle:

Performing and Managing Incremental Backups Using pg_basebackup in PostgreSQL 17

Aggregated on: 2025-05-14 12:13:40

With the release of PostgreSQL 17, the introduction of incremental backups using the trusted pg_basebackup tool marks a significant leap forward in database backup and restoration processes. Traditionally, database backups often involved taking periodic full backups, which, while comprehensive, are time-consuming and storage-intensive. Incremental backups offer a smarter alternative by capturing and storing only the changes made since the last backup, dramatically reducing both backup size and the time required to create backups.

Ethical AI in Agile

Aggregated on: 2025-05-14 11:43:40

TL; DR: Ethical AI in Agile Agile teams face ethical challenges. However, there is a path to Ethical AI in Agile by establishing four pragmatic guardrails: Data Privacy (information classification), Human Value Preservation (defining AI vs. human roles), Output Validation (verification protocols), and Transparent Attribution (contribution tracking). This lightweight framework integrates with existing practices, protecting sensitive data and human expertise while enabling teams to confidently realize AI benefits without creating separate bureaucratic processes.

A Deep Dive Into Firmware Over the Air for IoT Devices

Aggregated on: 2025-05-13 22:58:40

An Overview of IoT Devices and FOTA IoT devices are a combination of hardware and software which are programmed to collect and transmit the data over to the internet, other networks, or a variety of applications. There are various categories of IoT devices such as sensors, actuators, machines, gateways, and more. These IoT devices can be used independently, or can be attached to any other devices such as appliances, doors, industrial machinery, medical equipment, and other important assets. In today’s fast-paced environment, these devices offer the capability to provide all important sensory information without any manual intervention to their users via integrations with mobile apps or analytical platforms. These sensors solve various real-world problems such as asset tracking, infrastructure management, condition-based monitoring, industrial automation, and more. Companies that offer IoT solutions have to deploy and manage a large number of IoT devices in globally-dispersed locations. To make sure that all the devices are working and transmitting the telemetry data as expected without any failure, it is very important to monitor these devices and keep them updated with the latest version of their firmware or software. These new versions of the firmware may contain bug fixes, patches for security vulnerabilities, or new functionalities throughout the device’s lifecycle. Since IoT devices are low powered, low memory constrained devices, and deployed at distributed locations, it is crucial to do firmware upgrades over the air (FOTA) for these devices. FOTA happens remotely, and users do not need to be physically available at the same location. FOTA is an essential process for any IoT solution offerings as it enables IoT devices to provide uninterrupted flow of data to the users.

Agentic AI for Automated Application Security and Vulnerability Management

Aggregated on: 2025-05-13 21:43:40

It was not so long ago that I was having a much closer look at how AI is becoming embedded in our everyday developer work. I have watched more intelligent code suggestions, automated testing routines, and those ubiquitous chatbots become a normal part of the everyday toolkit. They are useful, naturally, but at their core, they are still fundamentally reactive, they still wait for you to inquire of them before taking action. What has really blown me away recently is the emergence of agentic AI. It is completely different, it does not just sit around and wait to be told what to do, but instead it takes charge itself. It can decide what should be done, strategize the steps, and even adjust if something unexpected happens. It's sort of like having a colleague who is always planning ahead, as opposed to merely carrying out orders.

Ensuring Configuration Consistency Across Global Data Centers

Aggregated on: 2025-05-13 20:28:40

Abstract Today, nearly every large company operates several data centers distributed across the globe. Each of these data centers hosts a variety of infrastructure services such as MySQL, Redis, Hive, and message queues. For scalability and adherence to the DRY (Don't Repeat Yourself) principle in system design, it's crucial that the same codebase is used across all regions. However, engineers responsible for writing and maintaining this code are often spread across different geographies. As a result, teams may not always be aligned on system configurations—even within the same region. This misalignment leads to discrepancies in configuration across data centers.

Fraud Detection Using Artificial Intelligence and Machine Learning

Aggregated on: 2025-05-13 19:28:40

Introduction For industries such as e-commerce, finance, and insurance where transactional security is paramount, fraud detection is a pressing challenge. Until a decade ago, organizations were relying on predefined rules and thresholds which are often not sufficient to detect fraudulent activities. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), organizations are enabling automated, adaptive, and real time anomaly detection techniques for fraud detection. In this article we will do a detailed review of AI and ML methodologies in fraud detection, including supervised, unsupervised, and deep learning techniques. Furthermore, this article also delves into the challenges of using AI in fraud detection.

Agile and Quality Engineering: A Holistic Perspective

Aggregated on: 2025-05-13 18:43:40

Introduction Agile has emerged as a widely adopted and effective software development methodology, enabling teams to deliver high-quality products to end-users with increased speed and efficiency. Within Agile frameworks such as Scrum, high-level software requirements or business needs are systematically decomposed into smaller, manageable units known as epics, which are further refined into user stories. Each user story is defined with specific acceptance criteria to ensure clarity in implementation and validation. Collaboration is a fundamental principle of Agile software development, emphasizing collective ownership and teamwork over individual contributions. Agile methodologies prioritize a "we" mindset, fostering a cohesive Scrum team that works iteratively to achieve project goals. Agile projects are executed in time-boxed iterations known as sprints, typically lasting two to four weeks. At the end of each sprint, the team produces a potentially shippable increment of the software. Various ceremonies, such as sprint planning, daily stand-ups, sprint reviews, and retrospectives, facilitate continuous improvement and alignment within the Scrum team.

How AI Agents Are Transforming Enterprise Automation Architecture

Aggregated on: 2025-05-13 17:28:40

Imagine someone in the early 20s predicting that, in the future, intelligent autonomous systems would be capable of executing tasks and solving problems independently. Fast forward to today, and with the rise of machine learning and natural language processing, humans can now build intelligent agents that seamlessly blend both machine and human capabilities. With this exclusive guide, we will examine the nature of AI agents, how these AI Agents for enterprise automation function, and their revolutionary effects across various sectors.

Unmasking Entity-Based Data Masking: Best Practices 2025

Aggregated on: 2025-05-13 16:13:40

Masking data to protect confidentiality has become a mandate more than ever before. As enterprises attempt to mitigate their workloads to multi-tenant cloud environments and data lakes etc., it makes perfect sense to revisit the masking strategies and be prepared for the future. That, too, when compliance regulations have tightened their grip over potential defaulters. That's exactly why entity-based masking is gaining acceptance across industry facets. It moves sensitive data across the landscape without exposing it. This enables organizations to comply with privacy regulations such as the GDPR, CCPA, and HIPPA.

Scaling DevOps With NGINX Caching: Reducing Latency and Backend Load

Aggregated on: 2025-05-13 15:13:40

In large-scale companies with huge DevOps environments, caching isn’t just an optimization — it’s a survival strategy. Applications teams working with artifact repositories, container registries, and CI/CD pipelines often encounter performance issues that aren’t rooted in code inefficiencies, but rather in the overwhelming volume of metadata requests hammering artifact services or in short binary storage systems, which are key to the functioning of any application or batch. "A well-architected caching strategy can mitigate these challenges by reducing unnecessary backend load and improving request efficiency."

The Role of Retrieval Augmented Generation (RAG) in Development of AI-Infused Enterprise Applications

Aggregated on: 2025-05-13 14:28:40

Introduction Artificial Intelligence (AI) is transforming enterprise applications, enabling businesses to enhance efficiency, improve decision-making, and unlock new opportunities. However, AI adoption is not a one-size-fits-all approach—organizations integrate AI at different levels depending on their needs, existing infrastructure, and strategic goals. This article explores three categories of AI-infused applications, their enterprise use cases, and how Retrieval-Augmented Generation (RAG) is revolutionizing AI adoption by improving accuracy, relevance, and contextual understanding in AI-driven applications.

5 Subtle Indicators Your Development Environment Is Under Siege

Aggregated on: 2025-05-13 13:13:40

Think your organization is too small to be a target for threat actors? Think again. In 2025, attackers no longer distinguish between size or sector. Whether you’re a flashy tech giant, a mid-sized auto dealership software provider, or a small startup, if you store data someone is trying to access it. As security measures around production environments strengthen, which they have, attackers are shifting left—straight into the software development lifecycle (SDLC). These less-protected and complex environments have become prime targets, where gaps in security can expose sensitive data and derail operations if exploited. That’s why recognizing the warning signs of nefarious behavior is critical. But identification alone isn’t enough—security and development teams must work together to address these risks before attackers exploit them. From suspicious clone activity to overlooked code review changes, subtle indicators can reveal when bad actors are lurking in your development environment.

DGS GraphQL and Spring Boot

Aggregated on: 2025-05-13 12:28:40

Why GraphQL? GraphQL provides a powerful layer to unify data from various backend sources, including databases, REST APIs, and microservices, into a single, consistent API. It enables the creation of flexible APIs that can evolve with changing client applications. Introduction This article will show you how to create an effective application that implements GraphQL using the Netflix DGS framework in the Spring Boot application.

Concourse CI/CD Pipeline: Webhook Triggers

Aggregated on: 2025-05-13 11:28:40

Concourse is an open-source continuous integration and delivery (CI/CD) automation framework written in Go. It is built to scale to any automation pipeline, from minor to complex tasks, and offers flexibility, scalability, and a declarative approach to automation. It is suitable for automating testing pipelines and continuously delivering changes to modern application stacks in various environments. This article will discuss setting up a Concourse pipeline and triggering pipelines using webhook triggers.

Scalable, Resilient Data Orchestration: The Power of Intelligent Systems

Aggregated on: 2025-05-12 20:28:39

Data is the key driver for any intelligent solution, including AI/ML. The accuracy and quality of any AI/ML model are directly proportional to the quality of data, regardless of whether it takes the form of input data, a prompt, or a pre-trained knowledge dataset. Often, the training datasets for AI/ML models originate from multiple sources and undergo various stages of data processing before being transformed into useful information that models can rely on for training. To achieve a reliable and continuous flow of data from diverse sources, transform data based on business rules, and extract insights and recommendations, we need a data orchestration solution. This article discusses the unsung hero that orchestrates data flow across multiple components, including pipelines, to deliver the intended outcome. This article intentionally focuses on the characteristics and principles of a data orchestrator that are architecturally stable and resilient over time. The topics covered in this article are technology-agnostic and applicable to any industry-recognized data orchestration tools. As an engineer, the solution architecture is influenced by the capabilities or non-functional requirements, not purely by the tools available in the market. This article offers a refreshing take on the topic of data orchestration and shares my experience with the distinguished community of software professionals on designing large-scale data orchestration systems that ultimately bring insights and power intelligent systems. What Is Data Orchestration? Data orchestration is a set of related tasks executed in an order driven by specific use case needs. Data orchestration can have many names, such as workflow or state machine. Essentially, they are represented as Directed Acyclic Graphs (DAG), composed of nodes and edges. The nodes are represented as individual tasks, while the edges are depicted as triggers. A DAG can compose multiple subsystems and connect them through events that trigger actions, share data payloads along with execution context, thereby making the orchestration well-informed to chart a course of action.

Comprehensive Guide to Property-Based Testing in Go: Principles and Implementation

Aggregated on: 2025-05-12 19:13:39

Traditional unit testing often leaves critical edge cases undiscovered, with developers manually crafting test cases that may miss important scenarios. Property-based testing with Go offers a more robust approach, automatically generating hundreds of test cases to validate your code’s behavior across a wide range of inputs. Rather than writing individual test cases, you define properties that your code should always satisfy. The testing framework then generates diverse test scenarios, helping you uncover edge cases and bugs that might otherwise go unnoticed. This comprehensive guide explores how to implement property-based testing using popular Go libraries like gopter and rapid, along with practical examples and best practices for test automation.

AI-Based Threat Detection in Cloud Security

Aggregated on: 2025-05-12 18:13:39

Abstract This article explores how artificial intelligence (AI) is enhancing threat detection in cloud certificate environments. It explicates how dissimilar AI modeling, such as supervised, unsupervised, and reinforcement learning, is used to describe and respond to security measures and threats in cloud environments. The article also wraps up the architecture of AI-powered security systems, including data compendium, model training, and feedback loops. It highlights real-world use cases like insider threat spotting and explains how emerging technologies like large language models (LLMs) and self-healing systems determine the future of cloud security. Technical, simple, and concise, the article offers deep insight into the practical deployment and limitations of AI in real-time cloud defense systems.

Is Agile Right for Every Project? When To Use It and When To Avoid It

Aggregated on: 2025-05-12 17:13:39

Agile has completely changed how teams think about project and task management by introducing flexibility, collaboration, and continuous evolution toward goals. Furthermore, its applicability stretches to software development, product management, and even non-technical fields. With such a multifaceted environment, it is crucial to determine the exact point where Agile can be applied or when it becomes more of a burden. Knowing where to balance the pros and cons of Agile helps in coming up with the best decisions aligning with your team and organizational objectives.

Blue Skies Ahead: An AI Case Study on LLM Use for a Graph Theory Related Application

Aggregated on: 2025-05-12 16:43:39

Research activities necessitate the exploration of ideas which may involve significant software development, experimentation, and testing effort. The ability of Large Language Models (LLMs) to generate executable software has been demonstrated in various use cases. This study highlights the positive outcomes of a use case involving a graph theory application. Through a series of successful prompts, functional software was produced within minutes, a task that would otherwise have taken several hours or days to complete. Rather than focusing solely on cost savings from reduced engineering effort, this study aims to highlight the substantial opportunity benefits provided by this technology, especially since research typically involves evaluating multiple ideas before selecting the optimal solution. Introduction When considering great systems thinking quotes, Donnella H. Meadows stands out: “Remember, always, that everything you know, and everything everyone knows, is only a model. Get your model out there where it can be viewed. Invite others to challenge your assumptions and add their own.”

Unlocking Data with Language: Real-World Applications of Text-to-SQL Interfaces

Aggregated on: 2025-05-12 15:43:39

Introduction It has long been a difficult task to generate precise SQL queries from customers' natural language inquiries (text-to-SQL). Understanding user queries, appreciating the structure and semantics of a particular database schema, and accurately producing executable SQL statements are some of the elements that contribute to the complexity. Large language models (LLMs) have opened up new avenues for text-to-SQL research. Superior natural language understanding skills are demonstrated by LLMs, and their scalability offers special chances to improve SQL creation.

How Trustworthy Is Big Data?

Aggregated on: 2025-05-12 14:28:39

Businesses and individual users now employ big data analysis to support decision-making, engineering innovation, and productivity levels. However, the surge in the reliance on big data leads to growing concerns regarding its accuracy and trustworthiness. Although big data provides unprecedented insights and opportunities across all industries, you should be aware of concerns, such as loss of trust in big data, and address them as well. This article explores the perils of bad big data, reasons for the lack of trust in big data, and strategies that can be adopted to combat it.

Why High-Performance AI/ML Is Essential in Modern Cybersecurity

Aggregated on: 2025-05-12 13:28:39

Today, organizations thrive in a complex and evolving threat landscape, and cybersecurity has become pivotal for digital resilience. Various sophisticated attacks, such as supply chain breaches, ransomware, phishing via malicious email campaigns, and vulnerabilities in cloud and Internet of Things (IOT) environments, need advanced, performance-sensitive solutions that can detect in real time and also respond to and mitigate these attacks. Artificial intelligence and machine learning (ML) play a frontal role in these transformations, providing the necessary capabilities to secure digital systems effectively.

From Zero to Production: Best Practices for Scaling LLMs in the Enterprise

Aggregated on: 2025-05-12 12:28:39

AI adoption is no longer a future trend—it's happening now. The 2024 Work Trend Index reports that 75% of knowledge workers already use AI on the job. At the forefront of this revolution are Large Language Models (LLMs) which are transforming the way businesses handle natural language tasks—from automating customer support and information retrieval to generating content. Foundational models are versatile, pre-trained architectures that form the backbone of the Generative AI movement. Trained on vast and diverse datasets—including text and multimodal content—they enable businesses to fine-tune AI capabilities for specific tasks like conversational agents, document summarization, and code generation. The Generative AI landscape features several key players, each contributing unique strengths to foundational model development:

Automating Data Pipelines: Generating PySpark and SQL Jobs With LLMs in Cloudera

Aggregated on: 2025-05-12 11:28:39

With the rise of generative AI and large language models (LLMs), data engineers and analysts can now translate natural language directly into working PySpark or SQL jobs. By integrating LLMs into Cloudera Machine Learning (CML) and executing workloads on Cloudera Data Engineering (CDE) with Iceberg table formats, enterprises can accelerate data pipeline development, improve collaboration, and simplify access to large-scale analytics.

How to Convert XLS to XLSX in Java

Aggregated on: 2025-05-09 21:28:38

Why Upgrading XLS to XLSX Is Worth Your Time Ask any seasoned Java developer who's worked with Excel files long enough, and you’ll probably hear a similar refrain: the old XLS Excel format is clunky and annoying. It’s been around since the late ’80s, and while it’s still supported in a lot of systems, it's not doing us many favors today. It was, after all, replaced with XLSX for a reason. Unfortunately, there’s still a lot of important data packed in those old binary XLS containers, and some developers are tasked with making clean conversions to XLSX to improve the usability (and security) of that data for the long run.

Automatic Code Transformation With OpenRewrite

Aggregated on: 2025-05-09 20:28:38

Code Maintenance/Refactoring Challenges As with most problems in business, the challenge with maintaining code is to minimize cost and maximize benefit over some reasonable amount of time. For software maintenance, costs and benefits largely revolve around two things: the quantity and quality of both old and new code. Quantity SonarQube suggests our organization maintain at least 80 million lines of code. That’s a lot, especially if we stay current with security patches and rapid library upgrades.

Accelerating AI Inference With TensorRT

Aggregated on: 2025-05-09 19:28:38

Introduction Deep learning models can be computationally expensive, especially when they're deployed in latency-sensitive scenarios like autonomous vehicles. Having a powerful GPU does not guarantee quick predictions if your model isn't optimized for inference. This is where NVIDIA TensorRT comes into play, an SDK that refines, quantizes, and accelerates deep learning models so they run significantly faster. This article provides a deep technical overview of converting PyTorch models to TensorRT, covering the tools, configurations, and best practices. We'll also examine how TensorRT reduced latency by more than 70% in an autonomous driving system.

A Complete Guide to Modern AI Developer Tools

Aggregated on: 2025-05-09 18:28:38

Based on my previous articles exploring AI, machine learning, and generative AI, many developers have reached out to understand how these technologies can enhance their workflows, from improving coding skills and streamlining model training to deploying APIs and beyond. The rapid evolution of artificial intelligence (AI) has led to a surge in specialized tools designed to streamline development, collaboration, and deployment. This guide explores the most impactful AI developer tools, highlighting their features, installation steps, strengths, and limitations. Whether you’re training models, deploying APIs, or debugging workflows, this article will help you choose the right tool for your needs.

The Human Side of Logs: What Unstructured Data Is Trying to Tell You

Aggregated on: 2025-05-09 17:28:38

It’s Friday afternoon, and your dashboards look great. Charts are green. CPU usage is stable. Database query times are within your SLA. You’re feeling great and ready for the weekend. But little do you know, there’s a significant issue being overlooked by all your metrics — and it’s about to ruin your weekend.

AI’s Role in Everyday Development

Aggregated on: 2025-05-09 16:28:38

Introduction From automated code generation to intelligent debugging and DevOps optimization, AI-powered tools are enhancing efficiency and improving software quality. As software engineering evolves, developers who leverage AI can significantly reduce development time, minimize errors, and improve productivity. Can software engineers be really replaced? Let’s explore briefly.

Immutable Secrets Management: A Zero-Trust Approach to Sensitive Data in Containers

Aggregated on: 2025-05-09 15:28:38

Abstract This paper presents a comprehensive approach to securing sensitive data in containerized environments using the principle of immutable secrets management, grounded in a Zero-Trust security model. We detail the inherent risks of traditional secrets management, demonstrate how immutability and Zero-Trust principles mitigate these risks, and provide a practical, step-by-step guide to implementation. A real-world case study using AWS services and common DevOps tools illustrates the tangible benefits of this approach, aligning with the criteria for the Global Tech Awards in the DevOps Technology category. The focus is on achieving continuous delivery, security, and resilience through a novel concept we term "ChaosSecOps." Executive Summary This paper details a robust, innovative approach to securing sensitive data within containerized environments: Immutable Secrets Management with a Zero-Trust approach. We address the critical vulnerabilities inherent in traditional secrets management practices, which often rely on mutable secrets and implicit trust. Our solution, grounded in the principles of Zero-Trust security, immutability, and DevSecOps, ensures that secrets are inextricably linked to container images, minimizing the risk of exposure and unauthorized access.

AI's Dilemma: When to Retrain and When to Unlearn?

Aggregated on: 2025-05-09 14:13:38

A Growing Need for Data Privacy Solutions In recent times, data privacy has become a central focus, with laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) playing a key role. Organizations are under increasing pressure to comply with user data deletion requests. One significant requirement is the right to data deletion. Where user can request their personal information to be removed from company’s databases.

Revolutionizing Financial Monitoring: Building a Team Dashboard With OpenObserve

Aggregated on: 2025-05-09 13:28:37

After a particularly grueling Thursday spent troubleshooting a publish API outage last year, I remember turning to my colleague and saying, "There has to be a better way." Four years into my software engineering career in fintech, and we were still piecing together information from disparate monitoring tools whenever something went wrong. That frustration kickstarted my two-month journey to build what's now become our team's most valuable asset: a comprehensive OpenObserve dashboard that's transformed how we monitor our services. Finding the Right Tool After spending a few weekends researching options, I narrowed down our choices to three observability platforms. OpenObserve won out because it offered: