How to: Data science/data engineering
- [A dirty dozen: twelve common metric interpretation pitfalls in online controlled experiments](https://blog.acolyer.org/2017/09/25/a-dirty-dozen-twelve-common-metric-interpretation-pitfalls-in-online-controlled-experiments/)
- [datastacktv/data-engineer-roadmap](https://github.com/datastacktv/data-engineer-roadmap): roadmap to becoming a data engineer
- [Awesome Data Engineering Learning Path](https://awesomedataengineering.com/)
- [Emerging Architectures for Modern Data Infrastructure](https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/)
- [How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh](https://martinfowler.com/articles/data-monolith-to-mesh.html)
  - Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale.
  - We need to consider domains as the first class concern, apply platform thinking to create self-serve data infrastructure, and treat data as a product.
- [MLOps](https://madewithml.com/courses/mlops/)
- [Uber's Big Data Platform: 100+ Petabytes with Minute Latency](https://eng.uber.com/uber-big-data-platform/)
- [SQL should be the default choice for data transformation logic](https://www.robinlinacre.com/recommend_sql/)