Can You Out-Architect Bad Data?

Even with the most well-designed data platforms, systems will break. Without embracing risk, you’re playing with fire.

Say it with me: broken data is inevitable.

It doesn’t care about how proactive you are at writing dbt tests, how perfectly your data is modeled, or how robust your architecture is. The possibility of a major data incident (Null value? Errant schema change? Failed model?) that reverberates across the company is always lurking around …

data data engineering data-observability data quality notes-from-industry