With genomics, your data is probably write ~once, almost entirely numeric, and is most likely used for single-client offline analysis. This differs a lot from what most SQL databases are optimizing for.

My best experience has been ignoring SQL and using (sparse) matrix formats for the genomic data itself, possibly combined with some small metadata tables that can fit easily in existing solutions (often even in memory). Sparse matrix formats like CSC/CSR can store numeric data at ~12 bytes per non-zero entry, so a single one of your servers should handle 10B data points in RAM and another 10x that comfortably on a local SSD. Maybe no need to pay the cost of going distributed?

Self plug: if you're in the single cell space, I wrote a paper on my project BPCells which has some storage format benchmarks up to a 60k column, 44M row RNA-seq matrix.