Explore principles and practices for building data foundations that remain resilient, fair, and performant through years of growth and change.
You inherit a data pipeline. The docs are stale, the original engineer left two years ago, and a critical field called source_transaction_id appears i...
So you're sitting on decades of user data. Old backups. Abandoned accounts. That CRM from 2004 you forgot to decommission. Feels heavy, doesn't it? Bu...
Money's tight. You've got a mainframe that's older than some of your developers, a regulatory deadline looming, and a board that wants both lower cost...
You built the roadmap. The compliance team signed off. The board approved the budget. But when you try to execute, you hit a wall that no project plan...
Legacy decommissioning looks like a technical off-switch. Flip it, and the old system goes dark. But data doesn't disappear when the server does — it ...
You're six months into a migration. The old system is still running in read-only mode, the new one has a few happy paths working, and your team is alr...
The call comes in on a Thursday afternoon. Your model, just deployed, is already showing strange behavior—refusing to answer certain queries, locking ...
You're building a schema. Maybe it's a database, a taxonomy, or a governance model. You think about normal forms, performance, scalability. But do you...
So you built an ethics layer. Good for you. But what if that layer, intended to stop bias, starts sorting people into digital castes? It happens more ...
You're building a feature that could be misused. Maybe it's a delete button that wipes years of data, or a share prompt that could spread misinformati...
So you built an ethical schema. Maybe it's a set of data principles, a consent framework, a governance charter—something you and your team sweated ove...
Every database starts as a clean slate. But within months — sometimes weeks — that clean schema turns into a tangle of workarounds, missing indexes, a...