It's a tech talk so I watched it and made some notes. It's an argument for using in-process databases when doing data science, rather than an external DB. The speaker pitches DuckDB as a concrete example, which seems to be an in-process DB for Python data frames.

The speaker presents measurements showing how much overhead the wire protocols for various DBs have. MySQL is the best, Postgresql is orders of magnitude worse due to a very inefficient binary format design. The best is still 10x worse than netcat.

Apache Arrow is trying to design a universal protocol for DB access that's more efficient than what's out there currently.

Speaker asserts that scale-out is usually not needed in data analytics, no need to use Spark etc unless you want it on your CV.

Audience member asks "what about multi-user/multi-process access", speaker admits DuckDB basically doesn't do that.

Speaker pitches for using embedded in-proc DBs inside AWS Lambda functions. Not practical to install Oracle RDBMS in something that only runs for 100msec.

A web shell for DuckDB is demonstrated, it uses WASM.

Decentralization is pitched as a reason to avoid 2-tier architecture (separate db engine w/ client protocol).