DBTB INT Greg Rahn
summary
Argues that JSON is self-describing and ubiquitous and surveys the actual ways teams handle it: using relational SQL engines, document databases, or hand‑rolled ETL scripts. Compares concrete tradeoffs — performance vs. flexibility, schema evolution vs. query simplicity — and names Snowflake as an example platform that treats JSON inside a SQL engine. Gives practical guidance on when each approach wins and what costs to expect in maintenance and query complexity.
AI-written summary · about
related talks
47mDealing with JSON in the Relational World
51mDealing with JSON in the relational world
44mWho is Jason, and what’s he doing in my database?
42mHow to Store JSON in a Database Without Attracting the DBA's Attention
48mHow Well Do Relational Database Engines Support JSON?
29mRetire your tables. It's all JSON now.
01h 06mSQL ❤️ JSON: The Best of Both Worlds
45mHow Well Do Relational Database Engines Support JSON?