What is the potential for consolidation in the modern data stack market and how does reverse ETL feature in the evolution of the value chain?

There's value for all. I think that's probably the most interesting question in this modern data stack: where does the value accrue? The way I think about it is, one dimension is: what's hard to replicate? Then the other dimension for the end user is: what actually provides the most value?

I think a ton of value accrues at the actual data warehouses that all of this runs on top of, because literally every operation you're doing in forward ETL, reverse ETL and dbt is money for Snowflake and Redshift -- it's all about that compute cost.

Then later, further in the stack, I think a lot of value will accrue to whatever is the tool that helps you assign semantic meaning to the data. Just because you have all this raw data in one place doesn't make it valuable. You need to be able to draw insight and to structure that information such that people can then act on it. What is doing that? Today, transformation tools like dbt are doing that, if you take the lens of the data team really owning everything end-to-end, but I think also applications that are able to plug into the data warehouse, consume this raw information and then turn that into actionable insights. That's also assigning meaning to the raw data. The tools that are less about moving data around and more about, how do you take all this crude oil and refine it -- I think those refineries are where a lot of the value will accrue.