[hodgesrm]

> Encoding your business logic in the database schema is rarely a good idea.

If by this you mean not defining tables with explicit columns in general I would disagree. It's one of the most effective ways to get speed in analytic applications because it optimizes compression. And in OLTP applications it's the best way to ensure consistency of data.

But perhaps you were referring to a narrower scope like just JSON & upstream changes?

[jd_mongodb]

No typing is great whether its BSON types in MongoDB or column types in RDBMS. Anything past that generally gets in the way of flexibility to respond to new business requirements. Business changes rarely originate at the schema level so its a bad place to encode business semantics.

[hodgesrm]

This just isn't true for a lot of use cases. If your requirement is to get low latency response on large data sets you need to cluster data carefully and compress it to reduce I/O. That's why data warehouses use column storage and strong typing.

Compressed storage size for uniform datatypes can be phenomenally efficient. I've seen 10,000x size reduction in ideal cases like monotonically varying integers stored using double delta codec + ZSTD compression.

[manigandham]

There's a vast difference between record shape and property types vs business logic.

Storing a record with strong typing does not get in the way of business, and in fact it often helps by maintaining data consistency and integrity.