-
ETL vs. ELT vs. Reverse ETL: A Practitioner’s Framework for Choosing the Right Pattern for a Given Workload
As a data engineer, you’ve likely been in a design review where someone says at the very end, “We should just do ELT for everything!”. You are a data engineer, and you’ve probably been in a design review where you heard someone say at the end, “We should just do ELT for everything!”. Or you’ve…
-
Data Engineering ROI: How to Justify Your Data Platform Investment to the Board
Every data engineering leader has sat through the same meeting: the platform work is done, the pipelines are stable, but defending its value to the board is difficult because it rarely appears on a single P&L line. Data platform budgets are often treated as discretionary IT spend, meaning they get cut first during a downturn.…
-
Identifying Hidden Token Costs in Snowflake Cortex AI
The demo works. It always does. You call AI_CLASSIFY on a sample of 10,000 rows, the credits barely move, and someone in the room says “this is so much cheaper than sending data to an external API.” Three weeks later your first real workload hits production — a million rows, five label classes, a moderately verbose model…
-
Snowflake Time Travel vs. Fail-safe: What Gets Recovered and When
3:14 a.m., and a migration script hands off to DROP TABLE orders_staging; against what everyone on the team swore was a permanent table. It wasn’t. Somewhere in the last quarter it got recreated as TRANSIENT to shave storage costs, and nobody updated the runbook. The on-call engineer isn’t worried — Snowflake has Time Travel, Snowflake has Fail-safe, this is…
-
SQL Patterns That Actually Show Up in Interviews (All 10, With Code)
Nobody fails a SQL interview because they don’t know the syntax. They fail because they stare at a question about “users with a 5-day login streak” and don’t recognize it as the exact same problem as “continuous subscription periods” and “consecutive winning games” — three phrasings of one pattern with one trick. The engineers who…
-
Optimizing dbt Models for Modern Warehouses: An Author & Reviewer’s Guide
Two dbt models can be byte-for-byte identical in output, pass every test, and read cleanly in review — and one of them costs $50 a month while the other costs $5,000. The compiler already guarantees the SQL is correct. What it can’t tell you is how much data moved through the model to produce that…
-
Building AI Agents: What Actually Works in Production
Ask ten teams if they’ve built an AI agent and nine will say yes. Look at what they actually shipped and most of it is a workflow with one LLM call in the middle — a fixed pipeline where a model fills in one step, not an autonomous system deciding its own path. That’s not…
-
20 AI Concepts Every Data Engineer Actually Needs
There are a hundred “AI concepts explained for beginners” listicles, and most of them are written for people who will never build anything. This one isn’t. If you’re a data engineer, you already understand pipelines, storage, and cost better than most ML tutorials assume — what you actually need is a map of the AI…
-
Why Larger LLMs Give Incorrect Answers in Production
A team I worked with upgraded their support-ticket triage pipeline to a bigger, newer model expecting fewer mistakes. Benchmarks said it was smarter across the board. Two weeks in, the error rate on a specific ticket category had gotten worse, not better — and worse in a particular way: the new model was wrong just…
-
The Medallion Architecture, Reconsidered: What It Solved and Where It Cracks
Almost every data team says it’s “doing medallion architecture.” Look under the hood and most of them aren’t — they have a Bronze layer that’s a dumping ground, a Silver layer that’s Bronze with nicer column names, and a Gold layer business users technically have access to but can’t actually use. That gap between the…
