SQL
SeqLL Inc. Common stock
stock NASDAQ

I too work at FAANG.
Quoting myself: “I’m not saying IBM has the relevancy it had in the 20th century- it doesn’t.”
In other words: I see it more as a direct comp to the likes of Oracle or SAP than I do FAANG. At the same time, OSS support, whether directly via something like RedHat or indirectly via IaaS that uses OSS at its core, does have its similarities. Let’s not forget just how much revenue Microsoft makes from LTSBs of Windows Server and SQL Server. Not exactly innovating there, and it’s a significant source of revenue.

Usually a mix: object storage/warehouse underneath, then an internal Python/SQL layer with a catalog so researchers aren’t hand-joining vendor feeds. The important bit is point-in-time access, dataset versions, entitlements and lineage. Commodities gets ugly fast once contract mappings and revisions enter the picture.

I’d use Databricks or Snowflake for the plumbing and build the thin domain layer yourselves. Building the whole platform in-house tends to become a permanent data-engineering project.

I too work at FAANG.
Quoting myself: “I’m not saying IBM has the relevancy it had in the 20th century- it doesn’t.”
In other words: I see it more as a direct comp to the likes of Oracle or SAP than I do FAANG. At the same time, OSS support, whether directly via something like RedHat or indirectly via IaaS that uses OSS at its core, does have its similarities. Let’s not forget just how much revenue Microsoft makes from LTSBs of Windows Server and SQL Server. Not exactly innovating there, and it’s a significant source of revenue.

Usually a mix: object storage/warehouse underneath, then an internal Python/SQL layer with a catalog so researchers aren’t hand-joining vendor feeds. The important bit is point-in-time access, dataset versions, entitlements and lineage. Commodities gets ugly fast once contract mappings and revisions enter the picture.

I’d use Databricks or Snowflake for the plumbing and build the thin domain layer yourselves. Building the whole platform in-house tends to become a permanent data-engineering project.