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11 Articles

Data Engineering

Posts about data engineering practices, tools, and workflows

•June 24, 2026•6 min read

AI Agents for Data Teams: Separating Hype from Production-Ready Tools

Every vendor now has an 'AI agent.' After evaluating dozens of them, here's what actually works and what's still a demo-only fantasy.

•June 6, 2026•6 min read

Three AI Agents Worth Building Once Your Data Stack Doesn’t Suck

Three AI agents worth building on top of a modern data stack: analytics, incident triage, and internal knowledge assistants.

•May 25, 2026•9 min read

From Modern Data Stack to AI Agents: A Practical Stack for 2026

How to add AI on top of a modern data stack: the practical layers, tools, and guardrails for building useful agents, retrieval, and internal copilots.

•May 11, 2026•9 min read

How to Build a Modern Data Stack in 2026: A Practitioner's Guide

The modern data stack promised simplicity, but in practice it often feels like IKEA furniture. Here’s how to build one that actually works, layer by layer.

•May 3, 2026•6 min read

We Built an AI Product. The Hard Part Wasn't the AI.

Everyone's obsessed with AI models. After building a 500-tool data directory, we learned the model is 10% of the work. The other 90% is data collection, validation, and quality — and that's the real moat.

•April 29, 2026•10 min read

Best Open-Source Alternatives to Snowflake, Databricks, and Fivetran

Save 84% on your data stack with open-source alternatives. ClickHouse instead of Snowflake, Airbyte instead of Fivetran, Metabase instead of Looker — with real cost comparisons.

•April 23, 2026•8 min read

Keep Your Warehouse from Drowning in Cold Data: A Practical Baselining Playbook

A three-layer playbook — lifecycle policies, usage-based cleanup, and team rituals — to stop warehouse storage from compounding silently.

•April 6, 2026•7 min read

I Reviewed 500+ Data Tools. Here Are the 10 Things the Best Ones Get Right.

After scoring 500+ data tools on a 100-point framework, clear patterns emerge. Here are the ten that separate great tools from forgettable ones.

•March 27, 2026•6 min read

ETL vs ELT in 2026: What's the Difference and Which Should You Choose?

ETL transforms data before loading; ELT loads first and transforms in the warehouse. Learn when to use each approach with real examples, tool comparisons, and a decision framework.

•March 21, 2026•11 min read

The Modern Data Stack in 2026: Complete Guide

A comprehensive guide to every layer of the modern data stack — ingestion, warehousing, transformation, orchestration, BI, data quality, reverse ETL, and streaming — with real tool recommendations and pricing.

•March 20, 2026•5 min read

The Hidden Bugs in Data Pipelines That No One Tests For

Data pipelines pass all tests but silently lose millions in revenue. Discover Automated Data Tests (ADT)—lightweight checks that catch join drops, sum errors, and aggregation glitches across billions of rows. Python and SQL solutions coming next.