But data lakes have arisen from the enterprise where the centralised data warehouse was the standard for the last few decades. They know how to use a database. They know how to model and schema the data. And they know about all of the problems it has. They didn't buy into the data lake concept because it's trendy.
Fact is that for large enterprises and for those with problematic data sets e.g. telemetry databases simply don't scale. You will always have priority workloads e.g. reporting during which time users and non-priority ETL jobs come second. And often Data Science use cases are banned altogether.
The reason data lakes make sense is because it is effectively unlimited scalability. You can have as many crazy ETL jobs, inexperienced users, Data Scientists all reading/writing at the same time with no impact.
Generally you want a hybrid model. Databases for SQL users and data lake for everything else.