Data Engineering Resources

Consumer lag proves movement, not recovery. Real Kafka recovery needs outcome accounting, reconciliation, and an explicit verdict.

October 13, 2026

by Ishan Shah

· 1,718 Views

Who Should Grade Your AI? Rules vs LLM Judges vs Humans

Learn when to use rule-based graders, LLM-as-a-judge, or human review to build more accurate, scalable, and reliable AI evaluation systems.

October 13, 2026

by Yakaiah Bommishetti

· 1,323 Views · 1 Like

Building a Scope-Constrained AI Agent for Automated CI Failure Repair

Build a scope-constrained AI agent that diagnoses CI failures, limits code changes, generates minimal patches, and validates repairs through targeted tests and CI gates.

October 12, 2026

by Uthej Mopathi

CORE

· 1,540 Views · 1 Like

The Jobs Matrix for AI: Four Boxes Instead of 40 Tools

Learn how a 2×2 AI matrix helps Agile teams classify AI use cases as chatbots or agents based on authority, scope, ownership, and review needs.

October 12, 2026

by Stefan Wolpers

CORE

· 1,787 Views

Columnar Formats for AI Telemetry: Parquet vs Vortex With DuckDB

This article compares Parquet and Vortex with DuckDB for AI inference telemetry, showing how file format and row ordering affect query speed and storage.

October 12, 2026

by Bhala Ranganathan

CORE

· 2,135 Views

Kafka With Spring Boot: Introduction

An introduction to the internal architecture of Apache Kafka, covering event streaming and the end-to-end communication flow between producers and consumers.

October 12, 2026

by Sanjoy Kumer Deb

CORE

· 1,484 Views · 1 Like

Running TypeSafe AI's Jev Inside Neo4j for Confidence-Aware Sentiment Decisions

Call TypeSafe AI's Jev from inside a Java UDF, returning a typed sentiment decision with explicit uncertainty information directly from Cypher.

October 12, 2026

by Akmal Chaudhri

CORE

· 2,177 Views

The Code Generation Bottleneck Is Gone. The Maintenance Bottleneck Isn't.

AI removes code-generation bottlenecks, but maintainability, ownership, review, and governance become software engineering’s new constraints.

October 12, 2026

by Uthej Mopathi

CORE

· 1,487 Views · 3 Likes

Production RAG and LLM Systems: A Simple Q&A Guide

Building a small RAG demo is easy. Making it work at production scale — millions of documents, real users, tight latency — is a different job entirely.

October 12, 2026

by Ram Ghadiyaram

CORE

· 1,256 Views

From Legacy ETL to Modern ELT: Engineering a Governed dbt and Databricks Architecture

Modern ELT isn't just replacing legacy ETL with dbt and Databricks. Separating ingestion, transformation, testing, and governance makes business logic easier to trust.

October 12, 2026

by SUNILKUMAR REDDY Eraganeni

· 1,464 Views · 1 Like

Why Standardized Connectivity Is Only Half the Enterprise AI Equation

Model Context Protocol can eliminate substantial integration friction, but production AI agents still need governance, tool discipline, and orchestration.

October 9, 2026

by Grant Spradlin

· 1,417 Views · 1 Like

Does a Bigger Context Window Mean Better Memory: Understanding Context Windows, Model Memory, and RAG

Context is temporary, model memory is learned, and RAG retrieves external knowledge. A larger context window means more input, not perfect recall or permanent memory.

October 9, 2026

by Yakaiah Bommishetti

· 1,897 Views · 2 Likes

Multi-Agent Orchestration on AWS With AgentCore Runtime and A2A

Multi-agent systems are common. Here we build a small multi-agent system on Amazon Bedrock AgentCore Runtime using the Agent-to-Agent (A2A) protocol.

October 9, 2026

by Purnanga Borah

· 1,839 Views · 2 Likes

How Cache Invalidation Caused Memory Pressure and API Failures at Scale

Aggressive cache invalidation caused memory pressure, latency spikes, and request failures. Reducing invalidations with time-based expiry restored success to 99.9%.

October 9, 2026

by Semyon Slepov

· 1,304 Views · 2 Likes

Member Spotlight: Raghava Dittakavi

DZone Core Member Raghava Dittakavi shares why great engineers question assumptions, understand the impact behind every change, and optimize AI to accelerate their work.

October 9, 2026

by Dominique Pugh

· 1,561 Views · 3 Likes

Threat Modeling Context-Aware AI: A STRIDE Walkthrough

How to secure enterprise AI pipelines: a hands-on walkthrough of six threat categories, one reference architecture, and a prioritized list of fixes.

October 9, 2026

by Sandeep Gadde

· 1,694 Views · 4 Likes

Why Your GPU Fleet Is Both Full and Idle: Building a Capacity Orchestration Layer

Managing GPU capacity manually produces fleets that are simultaneously fully allocated and poorly utilized. A capacity orchestration layer can fix the problem.

October 9, 2026

by Ankit Sinha

· 1,884 Views · 1 Like

Production-Grade LLM Regression Testing: Contracts, Evals, and Quality Gates

AI regression testing combines deterministic contracts, behavioral evaluations, production failures, and baseline comparisons to prevent silent LLM regressions.

October 9, 2026

by Uthej Mopathi

CORE

· 1,401 Views · 1 Like

AI Governance Belongs in Your Pipeline, Not in Spreadsheets

Shift AI governance from spreadsheets to code by embedding automated Risk Appetite Statements into CI/CD pipelines as Architectural Fitness Functions.

October 8, 2026

by vikram isanaka

· 1,407 Views · 1 Like

Supercharging AI Agents with Azure Context: A Hands-On Guide to Azure MCP

Here’s a step-by-step guide for cloud engineers on how to bridge local AI assistants with live Azure infrastructure using the Model Context Protocol.

October 8, 2026

by Ammar Ekbote

· 1,692 Views · 3 Likes

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