In the current modern distributed architecture, this article will explain how to execute a Kubernetes Job with optimized and multiple parallel worker processes.
Yes for most ETL scenarios, ADF handles data movement, basic transformations, and scheduling well, just don't expect it to replace custom code for complex business logic.
This article provides a hands-on tutorial for building AI agents using the Model Context Protocol (MCP) and C#, an open standard that enables Large Language Models (LLMs)
AI is reshaping cybersecurity. Here's how Google Gemini shields consumers on-device, while Microsoft Security Copilot automates enterprise detection and response.
A modern search approach unlocks deeper insights, more relevant results, and boosts productivity across organizations; here's how AWS OpenSearch fits into this landscape.
We'll analyze the performance of PostgreSQL full-text search (FTS) versus pattern and regex searching, highlighting trade-offs and execution efficiency.
AI agents expand attack surfaces, demanding safety by design, advanced red teaming, and shared benchmarks to build secure, trustworthy intelligent systems.
A scalable control architecture for cloud data pipelines using Query Vault, controller procedures, and triggers to enable smart restarts, logging, and automation.
With growing computing power for AI and its misuse in cyberattacks like autonomous exploits, deepfake scams, and smart malware becomes even more worrisome.
Small language models (SLMs) offer 90% of the value of large models at a fraction of the cost. Devs can maximize AI ROI by training SLMs on domain-specific data.
In this article, we will explore the value of AI agents, introduce popular agentic AI platforms, and walk through a hands-on tutorial for building a simple AI agent.
Junior developers are shipping features faster with Cursor and GitHub Copilot, while senior engineers question if AI-assisted code is maintainable at scale.
Software keeps growing in complexity while losing touch with business goals. Domain-driven design brings clarity, making systems scalable, meaningful, and built to last.
In modern cloud-native systems, services often run across multiple pods or nodes for scalability and high availability, introducing challenges in data consistency.