I have been thinking a lot lately about how much there is to learn in data. Even after spending years with SQL Server, T-SQL, and all the usual admin tools like performance tuning, backup strategies, and index design, I keep running into tools that force me to rethink how I build and manage data systems.
Two tools have stood out recently: Snowflake and dbt. For a long time, I kept them at a distance. There is always a new “next big thing,” and it is easy to stay skeptical. But the more I see companies moving toward cloud-native warehouses and modern transformation frameworks, the more I understand why.
Snowflake removes so many operational headaches I am used to dealing with in SQL Server, such as worrying about storage sizing, constantly tuning indexes, and fighting with hardware limitations. The ability to separate storage from compute and instantly scale without major reconfigurations feels almost unfair in the best possible way. I have spent enough time staring at slow query plans and worrying about IOPS to know how big of a shift this is. I still enjoy performance tuning, but when you know your data structure and the cost model, it is refreshing to just resize a warehouse and focus on delivering insights instead of babysitting infrastructure.
Then there is dbt. The idea of treating transformations as code, version-controlling them, adding automated tests, and generating documentation by default is exactly what I always wanted from classic ETL tools. Stored procedures always felt scattered and hard to track. dbt makes it clear and explicit. Transformations live in SQL files, you commit them, you test them, and they run directly in your warehouse. No hidden drag-and-drop logic, no mysterious job configs buried in a GUI. It pushes you to think like a software engineer instead of a traditional data developer, which is intimidating but also exactly what I have been looking for.
I am still learning. My background in T-SQL means I mostly need to adjust to new orchestration patterns and the cloud scaling mindset. Maybe I am still in the honeymoon phase, but I do not run into the same pain points I had trying to scale ELT workflows in SQL Server. I understand Snowflake and SQL Server have different strengths, but for data warehousing, I am enjoying Snowflake much more than I expected.
That uncertainty is what makes this interesting. I like the feeling of not having it all figured out yet. It reminds me that there is always another layer to understand and more ways to improve.
So here is to learning Snowflake and dbt, probably slowly and awkwardly. If you have already gone down this path, I would love to hear what surprised you or what you wish you had known early on. And if you are starting now too, maybe we can figure it out together.