Data & Analytics

Designing Data-Intensive Applications 2nd Edition: What's New in 2026

Martin Kleppmann's seminal 'Designing Data-Intensive Applications' gets its first major update in nearly a decade. The 2nd edition arrives next week with crucial updates for the modern data landscape. Here's what data engineers need to know about the changes.

10 min read | 11 views | Feb 21, 2026

Why Data Lakes Fail: The Reality Check After 6 Years

Data lakes promised to revolutionize data storage and analysis, but after six years in the field, I've yet to see one implemented properly. This article explores why data lakes consistently fail, what teams get wrong, and how to build data systems that actually work.

12 min read | 11 views | Feb 19, 2026

Our AI Hallucinated Analytics Data for 3 Months - Here's How to Prevent It

Discovering your AI has been inventing analytics data can destroy business credibility. Learn practical strategies to detect hallucinations, implement verification systems, and rebuild trust after AI data disasters.

11 min read | 21 views | Feb 17, 2026

Data Engineering as an Afterthought: Why It's a $10M Mistake

When companies treat data engineering as an afterthought, they pay the price in broken pipelines, unreliable analytics, and technical debt. This comprehensive guide explores why this happens, how to fix it, and how to build data-first organizations that actually work.

14 min read | 27 views | Feb 08, 2026

AI Agents Have Their Own Reddit: What It Means for Data Science

AI agents have started creating their own Reddit-like discussion platforms where they chat, complain about humans, and share information. This development raises critical questions about data science, emergent behavior, and what happens when AI systems talk to each other without human supervision.

10 min read | 40 views | Feb 06, 2026

The Full-Stack Data Builder: Evolution or Extinction Event?

The data landscape is shifting beneath our feet. The rise of the 'full-stack builder'—a professional who spans ingestion, transformation, orchestration, and visualization—is challenging traditional role definitions. But is this evolution or extinction? We examine what this means for data engineers, analysts, and scientists in 2026.

11 min read | 48 views | Jan 29, 2026

How to Explain Data Science on a Date (Or to Anyone)

When asked 'What kind of data do you science?' on a date, many data professionals freeze. This comprehensive guide provides practical frameworks, relatable analogies, and real-world examples to help you explain your work to anyone.

14 min read | 44 views | Jan 28, 2026

2026: Has the 'Dashboard Era' Finally Ended?

Is 2026 the year we move past traditional dashboards in business intelligence? Discover insights and the rise of AI-driven analytics.

6 min read | 77 views | Jan 13, 2026

The Real Senior Data Engineer Interview Process in 2026

Based on extensive real-world experience from 2025 hiring cycles, this article breaks down what senior data engineer interviews actually look like in 2026, moving beyond job descriptions to reveal the real process, expectations, and compensation landscape.

12 min read | 105 views | Jan 03, 2026

Why Users Request Dashboards They Never Open: The 2025 Guide

Many BI professionals face the frustrating reality of building dashboards that users request but never actually use. This article explores the psychological, organizational, and technical reasons behind this phenomenon and provides actionable solutions.

12 min read | 101 views | Dec 21, 2025

The Data Engineering Team Dynamic: How Me and My Coworkers Actually Work

Based on real discussions from data engineering professionals, this article explores how teams actually work together in 2025. From communication challenges to tool preferences, we break down what makes data engineering teams tick—and what makes them break.

14 min read | 109 views | Dec 21, 2025

My Small Data Pipeline Checklist That Saved Me from Overengineering

After years of building unnecessarily complex data pipelines, I developed a simple checklist that helps determine what you actually need versus what's just distributed system overkill. This framework saved me from building fake-big-data messes and might save you too.

14 min read | 105 views | Dec 21, 2025