Helping data teams preview, validate, and ship data changes with confidence.
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A data review flagged 99.999% row-count variance. The PR was two lines.
Base was five years of production history. Current was a one-hour CI build. Neither was wrong. They were built for different jobs.
False alarms like this train reviewers to scroll past real variance. That is the damage.
https://blog.reccehq.com/session-base-per-pr-why-data-reviews-lie
#dbt #DataEngineering #AnalyticsEngineering
The demos show new code being generated from scratch. The hard, high-value problem is navigating systems that have been migrated, patched, and extended by dozens of people over 30 years.
That's the opportunity. It's just not the one getting the press.
90% of enterprise programmers spend their time on maintenance, not greenfield development.
Michael Stonebraker's take on where AI actually earns its keep inverts the marketing narrative completely.
Michael Stonebraker was right about CODASYL. Right about NoSQL. Now he's run text-to-SQL on a real enterprise warehouse and got 10% accuracy against an 80% benchmark.
The pattern is hard to ignore.
Before you let agents touch your codebase, build these gates.
Not because you don't trust the agent - but because you wouldn't trust anyone without them. Including yourself.
https://blog.reccehq.com/before-you-let-agents-touch-your-codebase-build-these-gates #ClaudeCode #AIAgents #DevWorkflow
A wiki is something you look at. A shared AI system is something you work through.
When knowledge lives inside the workflow, it stays current. Every time someone runs a skill, outdated entries get noticed. Gaps get filled.
https://blog.reccehq.com/we-didnt-set-out-to-build-a-team-ai-plugin
"Arrow has the intricacy of a fine Swiss watch." The co-creator of Apache Arrow on why AI agents cannot replicate decade-long infrastructure design.
AI coding tools generate plausible but wrong SQL constantly. The fix isn't waiting for a smarter model.
AI skills are markdown files that encode domain knowledge into coding tools. No framework, just structured text in a repo.
The loop: code → review → handoff → skills update. Every session makes the next one smarter. One aggregation bug became a permanent rule enforced automatically.
Dori Wilson broke it all down at Data Debug SF. Full writeup:
https://blog.reccehq.com/ai-skills-for-analytics-eng
