Here is short info about post: The Software-as-a-Service (SaaS) landscape continues to grow at a rapid pace, with businesses and developers alike demanding scalable, reliable, and flexible solutions. One key strategy that modern SaaS providers adopt is API-first development. Instead of building the application’s user interface first and adding an API as an afterthought, API-first development starts with designing and implementing the API as the primary product. By treating the API as a first-class citizen, teams ensure that every function of the SaaS product is accessible ... API-First Development in SaaS: A Complete Guide

Here is short info about post: Testing JavaFX applications can be challenging due to the platform’s event-driven nature, the reliance on a graphical user interface (GUI), and the need to handle asynchronous tasks. Developers often encounter recurring issues when writing unit or integration tests for JavaFX apps. Understanding these common errors, their root causes, and how to fix them will save you countless hours of frustration. Below, we explore the most frequent pitfalls in JavaFX testing, complete with explanations and code examples to illustrate both the ... Common Errors When Testing JavaFX Applications, Their Causes and Solutions

Here is short info about post: Kubernetes has emerged as the de facto standard for container orchestration, enabling organizations to deploy, scale, and manage applications with remarkable flexibility. However, as teams embrace Kubernetes for production workloads, many encounter a growing challenge: cluster and tool sprawl. This occurs when organizations deploy multiple Kubernetes clusters—often across different cloud providers, environments, and teams—while simultaneously layering on numerous tools for monitoring, security, CI/CD, and management. What starts as an effort to gain agility can quickly lead to complexity, rising costs, ... Kubernetes Cluster and Tool Sprawl: Driving Complexity, Cost, and Security Risks

Here is short info about post: The increasing adoption of machine learning (ML) in production requires scalable, reliable, and automated deployment strategies. Enterprises need a way to serve models at scale, track experiments, and automate model selection. Kubernetes provides the infrastructure foundation, but the combination of MLflow, KServe, and AutoML elevates this to a fully managed, production-grade AI/ML inference platform. This article explains how to set up scalable and reliable inference on Kubernetes using MLflow for experiment tracking and model registry, KServe for model serving, and ... How To Run Scalable And Reliable AI/ML Inference On Kubernetes With MLflow, KServe, And AutoML

Here is short info about post: In the world of distributed systems, the client-server model forms the foundation of communication between applications. Whether you are building a web application, a mobile app, or a microservice, you are likely interacting with a server that exposes resources over the network. REST (Representational State Transfer) is one of the most popular architectural styles for designing such networked applications. In a synchronous communication scenario, a client sends a request to a server and waits for the server’s response before proceeding. ... Client-Server Synchronous Communication via REST

Here is short info about post: Building a real-time chat application is an excellent way to explore modern reactive programming techniques in Java. By leveraging Spring Boot, WebFlux, and MongoDB, we can create a fully reactive WebSocket-based chat service that handles high concurrency and provides seamless, real-time communication. This article walks you through the architecture, setup, and implementation details of a Reactive Spring Boot WebSocket Chat backed by MongoDB. We will focus on scalability and responsiveness using Project Reactor’s reactive streams. Understanding the Technology Stack Before ... How To Build A Reactive Spring Boot WebSocket Chat With WebFlux And MongoDB

Here is short info about post: Managing cloud infrastructure in a repeatable, secure, and scalable manner has always been a challenging task. With the rising complexity of modern applications and the need to empower development teams with self-service capabilities, platform engineering is emerging as a critical discipline. One of the best approaches to creating a self-service platform for Kubernetes environments is to build a platform abstraction on top of Amazon Elastic Kubernetes Service (EKS) using Crossplane—without writing raw infrastructure code such as Terraform or CloudFormation templates. ... How To Build A Platform Abstraction For EKS Cluster Using Crossplane Without Writing Infrastructure Code

Here is short info about post: What is RAG, Knowledge Graphs, and Graph RAG To understand Graph RAG, we need to define its constituent parts: Retrieval-Augmented Generation (RAG):RAG refers to a family of techniques where, instead of relying solely on what the Language Model “knows” (during training), you retrieve relevant information from external sources, and augment the prompt / input to the LLM with that information. This helps with freshness, domain-specificity, reducing hallucinations, and grounding. Knowledge Graph (KG):A knowledge graph is a structured representation of information ... How Graph RAG Combines Knowledge Graphs With LLMs To Structure Text Into Entities And Relationships, Enabling Explainable Reasoning And More Accurate Insights

Here is short info about post: MQTT is widely used in IoT and messaging systems because of its lightweight protocol and features such as persistent sessions (so that clients can disconnect and later receive messages published during their offline period), QoS (Quality of Service), etc. When you have many devices or clients, persisting session state (subscriptions, unacknowledged messages, last message, etc.) can become a performance bottleneck. TBMQ is an MQTT broker designed by ThingsBoard that in version 1.x used PostgreSQL to store persistent session data for ... How To Use Redis To Scale MQTT Session Persistence In TBMQ By Replacing PostgreSQL And Improving Performance With Lua Scripts And The Lettuce Async Client

Here is short info about post: Running Kubernetes on Amazon Web Services (AWS) gives organizations the flexibility to scale containerized applications quickly while leveraging AWS’s powerful infrastructure. AWS offers several approaches to deploy and manage Kubernetes clusters, ranging from fully managed services to self-managed clusters. Below is a deep dive into these deployment options, complete with practical coding examples and guidance to help you choose the right path. Why Kubernetes on AWS? Kubernetes (K8s) has become the de facto standard for container orchestration due to its ... Kubernetes Deployment Options on AWS

Here is short info about post: In today’s data-driven world, organizations rely on real-time analytics to make faster and more accurate business decisions. Batch processing has its place, but for monitoring live systems such as financial transactions, IoT sensor readings, or website activity, batch updates are not sufficient. Apache Kafka has emerged as the backbone of real-time data pipelines, offering scalability, durability, and high throughput. Coupled with live dashboards, Kafka enables decision-makers to visualize streaming data instantly and take proactive measures. This article explores the end-to-end ... Real-Time Data Streaming From Apache Kafka to Live Dashboards

Here is short info about post: Apache Kafka has become the backbone of modern event-driven architectures, enabling real-time streaming of data across distributed systems. While Kafka offers a wide variety of pre-built connectors through Kafka Connect, organizations often face the need to integrate with custom or niche systems that do not yet have an available connector. One common scenario is the need to consume data from HTTP endpoints, whether from REST APIs, streaming APIs, or custom web services. While there are community connectors for HTTP, building ... How To Implement A Custom Kafka Connect HTTP Source Connector To Integrate With HTTP Endpoints