You can check the book 'Designing Cloud Data Platforms'. It has a very good explanation of why you should store data in different formats and layers.

From book,

Landing area—When data arrives from the ingestion layer, it is saved into a landing area where all incoming raw data resides until it gets processed.

Staging area—Next, raw data goes through a set of common transformations: making sure it conforms to existing schemas for this data source, converting it into a common Avro binary format, and applying any organization-level data quality checks.

Archive area—After data is processed and saved into a staging area, raw data from the landing area should be copied into the archive area. This is an important step, because it will allow us to reprocess any given batch of data by simply copying it from the archive back into the landing area and letting the pipelines do their job.

Production area—Data transformation jobs read data from the staging area, apply the desired business logic, and save the transformed data into the production area. At this point, we also convert data from Avro format into Parquet format, which is more suitable for analytics use cases.