Architecture principles in depth

The following principles will help guide your decisions when architecting and engineering solutions are built into a modern data platform.

Principle #1 – Data lake as a centerpiece? No, implement the data journey!

This may sound shocking and really an anti-follow-the-herd mentality, but it is true! Thinking that the data lake was envisioned as a source for all data that can be miraculously understood and repurposed over time leading to great insights is naïve. It can become a data swamp and a costly liability without semantics, context, time series structures, and a clear metadata pattern with governance principles aligned with the data mesh and operational data fabric capabilities.

Data needs to be curated in the factory from raw form to consumable form, and it needs structure and life cycle along its assembled journey through various zones (such as a number of logical data lakes) until ready for consumption. Data needs to be released...