# datagod

> A community about the problems in data engineering — not the tools. Field notes on bad data, reliable sourcing, scaling intuition, and the tradeoffs that actually cost you.

Canonical URL: https://datagod.dev/
Sitemap: https://datagod.dev/sitemap-index.xml
RSS: https://datagod.dev/rss.xml
Author: datagod - Data engineer with ~10 years building large-scale big-data and warehousing systems from scratch.

## Core pages

- [Home](https://datagod.dev/): Big data vs. bad data.
- [Blog](https://datagod.dev/blog/): Field notes on data engineering, bad data, scaling, sourcing, and engineering judgment.
- [About](https://datagod.dev/about/): Author background and editorial point of view.
- [Community](https://datagod.dev/community/): Participation and contact page.

## Posts

- [LLD and HLD in the Age of AI: Judgment Over Recall](https://datagod.dev/blog/lld-hld-ai-engineering-interviews/): AI is lowering the cost of writing software, making judgment, code reading, production experience, and problem selection stronger engineering signals. Tags: artificial-intelligence, engineering-interviews, architecture, design-patterns, engineering-judgment.
- [Microservices Scale Your Code. Your Data Is What Breaks.](https://datagod.dev/blog/microservices-scaling-patterns/): Microservices are a scaling pattern for code. But the moment you split a service, you split its data — and distributed data is where scaling actually gets hard. A tour of the Scale Cube, database-per-service, the dual-write trap, the outbox + CDC fix, sagas, and sharding, with the cost of each named. Tags: microservices, scalability, distributed systems, data consistency, change data capture, saga pattern, cqrs, sharding.
- [The Terrarium Benchmark: On LLMs, Intuition, and Intelligence](https://datagod.dev/blog/terrarium-benchmark/): We celebrate AI for the easy, well-documented work — apps, websites, CI/CD, boilerplate. The real test is the messy, under-documented problems with slow feedback: keeping something alive. A reflection on intuition over idealism, the limits of a probabilistic model, and a benchmark you can run with five money plants. Tags: llms, agi, superintelligence, critical thinking, ai limitations.
- [Nobody Cares About Your Design Patterns — Real Engineers Think in Money](https://datagod.dev/blog/real-engineers-think-in-money/): When a senior tells a junior to 'focus on the basics,' this is what they actually mean: engineering is the management of constraints — people, budget, deadline. The pointers I'd hand a junior instead of the cliché, all of which come down to translating decisions into money and chasing the thought process, not the solution. Tags: engineering-intuition, architecture, cost, tradeoffs, design-patterns, career.
- [Building Scaling Intuition in the Age of AI](https://datagod.dev/blog/scaling-intuition/): AI collapsed the cost of producing code. What's scarce now is the intuition to know what a system must do and what will quietly break — correctness first, scale second, and the judgment to direct AI well.
- [In the age of AI, the problem takes center stage — not the tools](https://datagod.dev/blog/problem-over-tools/): Spark, Flink, Debezium — tools get you 0 to 1; maintaining them is the headache. In the age of AI, judgment and a sacred problem statement are everything.
- [Big data vs. bad data](https://datagod.dev/blog/big-data-vs-bad-data/): My first post. After years building big-data platforms from scratch, the hard part was never the volume — it's bad data, reliable sourcing, and asking the dumb questions first.

## Editorial stance

- The site focuses on data-engineering problems and tradeoffs, not tool hype.
- Posts should be answer-first, practical, and explicit about operational cost.
- Content is human-authored and may be AI-assisted for editing or organization.