Sure, but the cost of schema maintenance is grossly overstated by those who simply aren't familiar with the tooling to do so. Furthermore, the cost of dealing with the corrupt data down the line will absorb all the gains you had in a matter of months, unless of course you've reinvented strong schema control at the application layer.

A lot of inexperienced devs look at NoSQL as being simpler than SQL because the dev documentation is shorter and has a more stylish theme. They then assume that all the touted benefits of SQL are YAGNI and they'll cross that bridge when they come to it.

The mistake they make is assuming that the benefits of SQL databases are something that only is needed for a mature application. To the contrary, SQL gives an incredible hedge against the uncertainty of change in the early days of an app when churn is high. A normalized data structure allows much more query flexibility; you should only denormalize for performance once you realize what the workload and logic will look like for the long haul.

Of course if you have a known workload that you want to scale up huge then that's a legitimate use case for NoSQL, but the purpose is not flexibility.

Change management is not something that should be feared if given careful consideration!

I don't always know the "perfect" schema design up front but there are great tools in the modern SQL tool belt for managing abstractions and evolving schemas.

In PostgreSQL:

    CREATE SCHEMA crm;

The "crm" schema can store all of my customer relationship management related tables. I may not know all of the data I need for them right now and will need to evolve them over time. However if I start with a solid design for my public schema I can use views in the public schema:

    CREATE VIEW public.customers AS
        SELECT c.id, c.contact_name, c.email
        FROM crm.customers AS c;

This is a simple view. You applications can CREATE/UPDATE/INSERT against the view in the public schema with no changes to your code. However if you want to evolve the schema of customers you can do so in the `crm` schema adding tables, columns, etc.

    CREATE VIEW public.customers AS
        SELECT c.id, c.contact_name, c.email
        FROM crm.customers AS c
        WHERE deleted = false;

You can then evolve your public schema to include more information as needed by updating the view. The caveat is that if your view starts materializing data from more than one table you'll have to intercept INSERT/UPDATE/DELETE and replace them with your own functions to put the data in the right place. This works quite well for implementing nice features like administrative flags without having to update your application code or spread that logic out: it's all kept in the database.

You still get the power of indexes, a strong query language, and structured data. PostgreSQL is great as an application server allowing you to comment all of your objects and having deep introspection facilities allows you to not only be flexible with your data models but also consistent.

Update: Added an example of changing the public schema to add a filter to the query for an administrative flag

I don't really get this. ALTER TABLE ADD COLUMN X is a cheap operation when your database is small, at least on decent hardware. It's when you get up to scale that it starts being a burden. During the rapidly-evolving stage is when it should be the easiest to alter database schemas, because you shouldn't have accumulated all that much data yet.

And without the adult supervision that "ALTER TABLE" theoretically implies, you end up with 15 columns named "t1" and the like, no idea what any of it means, and source code to match (assuming its not gone and you're trying to reverse engineer the schema-less data)

There is a place for unorganized, disorganized, unengineered, unplanned data structures, but its not in the DB.