Data Engineering Resources

Why Databricks and Snowflake Speak the Kafka Protocol: Ingestion vs Architecture

Databricks and Snowflake speak the Kafka protocol, but Kafka for lakehouse ingestion is not Kafka as an event-driven architecture.

September 30, 2026

by Kai Wähner

CORE

· 3,521 Views · 1 Like

Git Blame Isn’t Enough: Building Verifiable Provenance for AI-Generated Code

AI-generated code needs verifiable provenance linking intent, context, models, edits, approvals, commits, and artifacts across the software lifecycle.

September 30, 2026

by Uthej Mopathi

CORE

· 1,782 Views · 3 Likes

Meta Wants to Run Your Business With AI — Microsoft and Salesforce Have a New Rival

Meta launches Enterprise Platform, bringing Muse, Business Agent and AI tools to companies as it takes on Microsoft and Salesforce.

September 30, 2026

by Ai Cerrudo

· 1,617 Views · 1 Like

Engineering Self-Healing SQL Pipelines With LLMs: Validation, Guardrails, and Safe Recovery

Build self-healing SQL pipelines where LLMs propose repairs while deterministic validation, guardrails, and execution controls protect production systems.

September 30, 2026

by Uthej Mopathi

CORE

· 1,297 Views · 3 Likes

OpenAI ‘o’ Leak: What We Know About ChatGPT’s Always-On Assistant Before DevDay

OpenAI may be testing an always-on ChatGPT assistant called “o,” with leaked references pointing to possible email functionality and ChatGPT Pro placement.

September 29, 2026

by DZone Staff

· 2,930 Views · 2 Likes

A Deep Dive into the Microsoft Foundry Document Intelligence SDK: From PDF to Structured Data

A hands-on guide to Microsoft Foundry Document Intelligence SDK for extracting text, structured fields, and document data from PDFs for RAG and AI pipelines.

September 29, 2026

by Jubin Soni, FBCS

CORE

· 6,676 Views · 3 Likes

How to Test POST API Requests With Playwright TypeScript

Learn how to test POST API requests in Playwright with TypeScript using static JSON objects and arrays, JSON.stringify(), JSON files, and the Faker library.

September 29, 2026

by Faisal Khatri

CORE

· 1,076 Views · 1 Like

Mistaking Code Production for Engineering Progress: AI Productivity Myths Part 1

In this article, you will learn why lines of code generated are almost meaningless as a measure of AI-assisted development.

September 29, 2026

by Gaurav Gaur

CORE

· 1,455 Views · 2 Likes

Jakarta Batch in Practice: Reliable Chunk-Oriented Processing for Enterprise Workloads

Jakarta Batch gives enterprise apps a standard model for long-running data processing with jobs, steps, readers, processors, writers, checkpoints, and tunable execution.

September 29, 2026

by Otavio Santana

CORE

· 1,656 Views · 3 Likes

The Warning That Never Stops the Agent

Deepagents tracks real session cost but only shows a toast. Nothing stops the loop from spending past a limit. A before_model hook that can jump to "end" closes that gap.

September 29, 2026

by Ninaad Rao

CORE

· 1,082 Views · 1 Like

AI Coding Is Moving From Trusting the Model to Constraining What It Can Do

AI coding is shifting from trusting models to constraining them with permissions, tools, and deterministic checks. BUBAS applies the same idea to business logic.

September 28, 2026

by Peter Verhas

CORE

· 1,276 Views · 1 Like

From Giant Prompts to On-Demand Skills: Build an Extensible AI Agent With Progressive Disclosure

Progressive disclosure replaces giant prompts with lightweight skill summaries and on-demand instructions, keeping AI agents focused and extensible.

September 28, 2026

by Akhil Madineni

CORE

· 1,633 Views · 3 Likes

Beyond Screenshots: Building Replayable Production Diagnostics for Hard-to-Reproduce Bugs

Capture privacy-safe production event timelines to reconstruct failures, correlate backend activity, and diagnose bugs that screenshots cannot explain reliably.

September 28, 2026

by Uthej Mopathi

CORE

· 825 Views · 3 Likes

The Agent Changed Its Plan Mid-Run: Reconciling AI Decisions With Completed Temporal Activities

Reconcile AI plan changes with completed Temporal Activities using versioned plans, idempotent execution, and compensation rather than retroactive rollback.

September 28, 2026

by Akhil Madineni

CORE

· 1,081 Views · 3 Likes

Building a Product Recommendation Engine With Neo4j — No ML Library Required

A graph models customers, products, categories and tags, making collaborative filtering, co-purchase analysis, content similarity, and trending queries graph traversals.

September 28, 2026

by Akmal Chaudhri

CORE

· 1,205 Views · 1 Like

Why Incident Response Needs Memory, Not Just Intelligence

LLMs are excellent at reasoning, but effective incident response depends just as much on remembering previous incidents and organizational operational context.

September 28, 2026

by Akshay Pratinav

· 1,089 Views · 1 Like