There's a lot of confusion around data lakes. One source of confusion is that "data lake" versus "data warehouse" is often presented as a choice, where you can have either:

1. A data lake, where all data is stored in its native format (CSV, JSON, ...), in an object store (S3, GCS, ...), with the schema defined on read (Hive, Presto, ...).

2. A data warehouse, where all the data is organized in a highly structured tables (star schema) in a commercial database (Snowflake, Redshift, ...).

This is a false choice! Modern data warehouses, particularly Snowflake and BigQuery, are fully capable of storing semi-structured data.

Furthermore, you do not need to curate your data into a star schema before loading it. The ideal way to set up a modern data warehouse is to establish a "staging" schema that matches the source, and then transform that data into a star schema or data marts using SQL. In this scenario, your "data lake" and "data warehouse" are just two different schemas within the same database.

There are still some scenarios where it makes sense to build a data lake in addition to a data warehouse, primarily future-proofing. I wrote a blog post where I tried to outline these scenarios: https://fivetran.com/blog/when-to-adopt-a-data-lake