Data Engineering Resources

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

· 2,702 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

· 3,877 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

· 3,528 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

· 2,355 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

· 3,753 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

· 2,872 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

· 2,615 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

· 4,343 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

· 3,027 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

· 3,799 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

· 2,135 Views

API Testing Frameworks: How to Pick the Right One and Actually Use It Well

Learn how to choose the right API testing framework, including REST Assured, Supertest, pytest, Postman, Karate, and Keploy, for better API test automation.

July 23, 2026

by Himanshu Mandhyan

· 3,008 Views · 1 Like

How to Build a Solid Test Pipeline in the Era of Agentic AI Development

Learn to build a strong testing pipeline to ensure code quality, as in enterprises today most code is generated by AI agents at a huge volume and scale.

July 22, 2026

by Sai Rakshit Yerram

· 5,238 Views · 2 Likes

How Agentic AI Is Turning Traditional Automation Into a Tool Layer?

Agentic AI doesn’t replace traditional automation. It uses existing workflows, APIs, and systems as tools while handling decisions, exceptions, and complex tasks.

July 22, 2026

by Sagar Sahay Bhatnagar

· 3,318 Views

Tracing the Agentic Loop: Monitoring Multi-Round-Trip MCP Calls With OpenTelemetry

The latest MCP spec mandates W3C tracing. Use Quarkus and OpenTelemetry to easily visualize disjointed, multi-round-trip AI agent workflows in production.

July 22, 2026

by Daniel Oh

CORE

· 3,182 Views · 1 Like

Building Production-Ready AI Vector Search in Databricks: Chunking, Embeddings, ML Pipelines, and RAG

Build production-ready Databricks vector search with chunking, embeddings, Delta sync, index freshness, version control, and governance.

July 22, 2026

by Seshendranath Balla Venkata

· 2,249 Views · 1 Like

The Production-Readiness Gap in AI-Generated Full-Stack Apps

AI-generated apps can look complete quickly, but production readiness still depends on security, backend design, data quality, and observability

July 22, 2026

by Satyam Nikhra

· 3,326 Views

Beyond the Model: Building Real-World Machine Learning

Learn how MSOE bridges the gap between machine learning theory and production systems through data pipelines, deployment, monitoring, and automation.

July 21, 2026

by Craig Hardy

· 2,809 Views

Will AI Keep Us Stuck in 2020 Architectures?

AI coding assistants are deeply fluent in current Spring conventions, which raises a real question: does that lock architecture into 2020-era patterns? It turns out, no.

July 21, 2026

by Daniel Sagenschneider

· 3,281 Views · 1 Like

Why Do Some Proxies Work Fine for Search But Fail Once You Start Filtering Results?

Root searches are cached and easy to access. Filtering forces requests to hit the backend database directly, triggering stricter anti-bot checks that block basic proxies.

July 21, 2026

by xiyun chen

· 2,427 Views

The Latest Data Engineering Topics