Building a Modern Data Foundation with dbt: A Practical Guide
I’ve helped dozens of companies build data foundations. The ones that fail almost never fail because they picked the wrong tool. They fail because nobody agreed on what the foundation was supposed to do. This guide covers the decisions that actually matter: how to pick a warehouse, why dbt wins as the transformation layer, which architecture patterns work for mid-size teams, and how to migrate off legacy ETL without losing your mind. If you’re a data leader at a SaaS company between $5M and $100M in ARR, this is written for you.
Read More5 dbt Implementation Mistakes That Kill Data Trust
dbt changed the game for analytics engineering. But like any powerful tool, it can create as many problems as it solves — especially when the implementation is rushed or the team doesn’t have a clear plan. Here are the five mistakes I see most often when companies implement dbt, and what to do instead. 1. No Testing Strategy This is the most common and most damaging mistake. Teams build dozens of models but write zero tests. Then they wonder why stakeholders don’t trust the numbers.
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