Data Engineering Resources

How to Protect Your AI Agents from Prompt Injection Attacks: An Active Defense Approach

Stop just blocking prompt injections. Learn how to use MIRAGE to trap AI agents in honeypots and force them to burn their own API tokens.

July 29, 2026

by Victoria Fonareva

· 1,832 Views

Why Standard Test Automation Misses the Failures That Matter in AI Agent Systems

Evaluation has real costs (inference spend, latency, storage) — budget for it explicitly, and treat every user-reported regression as a permanent new test case.

July 28, 2026

by Dimple Bajaj

· 2,142 Views · 2 Likes

Microservices Architecture in Production: 7 Engineering Decisions That Determine Success or Failure

Microservices succeed when they're designed with clear service boundaries, reliable communication, independent data ownership, and strong operational practices.

July 28, 2026

by Mahipal Nehra

· 2,057 Views · 2 Likes

AI-Native Developers: Why Fast Authoring Is Creating Bad Debuggers

AI lets juniors write code instantly, but leaves them unable to debug. With coding now being easy, debugging is the ultimate human superpower.

July 28, 2026

by Rakshath Naik

· 1,611 Views

Building Reliable Data Pipelines for Enterprise Analytics Using PySpark

Build reliable PySpark pipelines with techniques for data validation, schema evolution, transformation design, partition management, and operational monitoring at scale.

July 28, 2026

by Harsh Patel

· 1,484 Views

How Does an LLM Request and Response Cycle Work? A Full Walkthrough

Learn how an LLM request and response cycle works, from the second you hit send to the moment the last word lands on your screen.

July 28, 2026

by NaveenKumar Namachivayam

CORE

· 1,739 Views · 2 Likes

Why SQL Server Applications Break on PostgreSQL and How Compatibility Layers Fix It

SQL Server migrations break at the application layer. A compatibility layer like Babelfish keeps T-SQL running while the engine moves to PostgreSQL.

July 27, 2026

by Minesh Chande

· 1,872 Views

From DevOps to AIOps: How Agentic AI Tamed Our Multi-Substrate Chaos

Legacy VMware on-prem, reactive AWS, and a ticket queue that never emptied — we deployed agentic AI across both substrates and changed how the team operates entirely.

July 27, 2026

by Mayank Jain

· 1,412 Views · 1 Like

Stop Fine-Tuning Everything: A Decision Framework for Model Adaptation

When to prompt, when to fine-tune, when to pre-train — a decision framework to get the most out of your data and the most bang for your buck.

July 27, 2026

by Supriya Vijay

· 1,601 Views

Building a Voice-Controlled Graph Assistant With Neo4j, LiveKit, and OpenAI

Build a voice assistant that lets you ask natural-language questions to a Neo4j graph database and hear the answers spoken back.

July 27, 2026

by Akmal Chaudhri

CORE

· 1,713 Views

Securing Model Context Protocol Servers: 4 Gates From Code to Production

Secure MCP servers against prompt injection, data leaks, and denial-of-wallet with four practical, OWASP-aligned gates from code to production. Runnable code.

July 24, 2026

by Viquar Khan

· 3,536 Views · 2 Likes

Building a Runtime Control Plane for Agentic AI: Lessons From Shipping Real Agents in Production

Practical patterns, code examples, and hard-earned lessons from implementing enforceable guardrails around autonomous agents in real enterprise environments.

July 24, 2026

by Geetha Sreenivasa Rao Repalle

· 3,249 Views · 2 Likes

Building Production-Grade Semantic Search With GPT-5 and Microsoft Foundry, From Scratch

Learn key concepts like embeddings, vector search, chunking, hybrid search, semantic ranking, and how these pieces fit into a scalable, enterprise-grade AI application.

July 24, 2026

by Jubin Soni, FBCS

CORE

· 2,244 Views

One of Waterfall's Most Resilient Artifacts

Learn about why QA-as-a-phase persists, the costs it creates, and how teams can transition to continuous quality across the software development lifecycle.

July 24, 2026

by Stelios Manioudakis

CORE

· 3,469 Views · 3 Likes

Engineering Production Agentic Systems: An Introduction

Explore a three-part field manual on building reliable production AI agents through context engineering, guardrails, and human-in-the-loop architecture.

July 24, 2026

by Ram Ravishankar

· 2,457 Views · 1 Like

From APIs to Agents: How Back-End Engineering is Evolving in 2026

Back-end engineering is moving from static APIs to autonomous, AI-driven agents that can plan, decide, and act—not just respond to requests.

July 24, 2026

by Nathan Smith

· 2,360 Views · 1 Like

How to Build Living AI Coding Assistants With Quarkus Agent MCP

Supercharge local development with the standalone Quarkus Agent MCP server, allowing AI assistants to run, monitor, and debug Java applications.

July 23, 2026

by Daniel Oh

CORE

· 3,104 Views · 3 Likes

Why AI-Generated Code Fails Security Reviews 45% of the Time

AI coding tools produce security flaws in 45% of outputs, yet developers trust the code more despite no security gains in two years.

July 23, 2026

by sunil paidi

· 2,757 Views

AI and Agentic: Promise, Peril, and Predictability

As the use of AI and agentic AI grows, organizations need to implement strong governance and guardrails to protect their users and themselves.

July 23, 2026

by Bikram Sinha

· 3,709 Views

Treat Your AI's Output Like User Input

AI's output is untrusted input. Validate it, fence it in, and never hand it raw to anything that can do damage — the same reflex you already have for form data.

July 23, 2026

by Naveen Birru

· 1,996 Views

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