- Tags:: #📝CuratedNotes, [[Data engineering|Data engineering]] ## Interesting refs - [[The cost of cloud a trillion dollar paradox|The Cost Of Cloud A Trillion Dollar Paradox]] - [The Two Philosophies of Cost in Data Analytics](https://www.holistics.io/blog/the-two-philosophies-of-cost-in-data-engineering/) - [Why rising cloud costs are the silent killers of data platforms | by Kris Peeters | Jul, 2022 | datamindedbe](https://blog.dataminded.com/why-rising-cloud-costs-are-the-silent-killers-of-data-platforms-52a98b371f28) >The solution is close monitoring and good governance rules. But those things are often implemented too late. >No executive who wants to get a next promotion dares to challenge the ever-increasing expenses in data. They are labeled as old-fashioned, and they “don’t get it”. In reality, most executives don’t get it. The data emperor wears no clothes. >If you prefer to manage each low level component yourself, to keep full control and reduce cloud costs to the max, then make sure to invest in a stellar data team that can focus on building the platform and offer a great developer experience to the users of the platform. >3. Standardise on something that you can easily move from platform to platform. Today, for all processing code, the humble [[docker|Docker]] container is a great abstraction layer. It’s universally supported across clouds. And you can run it yourself or go for managed solutions. >4. Keep a close eye on cloud costs, and pro-actively optimise where it hurts the most. Don’t wait for the executives to do this exercise for you. Cloud vendors have tools and dashboards to do exactly this. Use them! >5. Whatever you do, iterate. Don’t make 5 year plans. Don’t spend 1 year on architecture alone. Ship useful stuff. Collect feedback. Iterate. A modern data platform evolves over time.