Metadata
- Author: Iliana Iankoulova
- Full Title:: 7 Antifragile Principles for a Successful Data Warehouse
- Category:: 🗞️Articles
- URL:: https://blog.picnic.nl/7-antifragile-principles-for-a-successful-data-warehouse-574b655f0bc6
- Read date:: 2025-03-29
Highlights
We have always strived for low entropy DWH tech, and when we decide to let go of a tool we are merciless in migrating all the code away. (View Highlight)
every PR is reviewed by 2 Data Engineers. (View Highlight)
New highlights added 2025-03-29
We do very limited patching in the DWH code. We take measures to have proper data migration, compared to keeping dead logic in our codebase forever. At the same time, we challenge statements such as “those classification fields are of no concern to the operational system, so we won’t store them”. It is a fairly simple decision rule: if there is value in having a piece of master data that will improve the business, it should be considered in the scope of the operational system. As Data Engineering professionals, we can’t take it on our shoulders to solve data quality issues or invent data. This only leads to failures that are later used as cautionary tales of why DWHs are bad, and data lakes are a better alternative. (View Highlight)
The Data Vault is a living, breathing example of antifragility, where all relationships (links) are many to many, and new systems are easy to add to the existing model. (View Highlight)