100% agree. > The trick is figuring out which way fits your business needs. Rule of thumb - if you don't control the database, use an ORM; if you do control t

100% agree. > The trick is figuring out which way fits your business needs. Rule of thumb - if you don't control the database, use an ORM; if you do control the database, work directly with it. For example, let's say that your business is a software company that sells an on-prem product. Some of your customers have Postgres expertise, some have MySQL expertise, some MSSQL, some people are stuck on Oracle. Forcing customers to develop DBA expertise in a database they're not familiar with just for the privilege of buying your product is a sales disaster in the making. So you go with an ORM and set up QA testing that tests releases across all of the databases that you support, and the ORM helps you by making it much more likely that your development efforts will automagically succeed in working with each of the supported databases. In most other situations, though, it makes much more sense to start with the data design. If your business grows, your databases are going to grow. You are almost guaranteed not to switch databases (absent overwhelming financial need, see: Oracle) over the lifetime of your company. Data analysts (data scientists now?) can extract serious value from your databases by getting into the weeds of the database schema, indexes, and queries and working with developers and DBAs to optimize them for business reporting. If you give up control to an automated tool that knows nothing about your business, your business will be less competitive as a result. Data is far too valuable these days to refuse to develop expertise with the underlying databases.