To avoid timeouts during large data uploads with the PUT command in Snowflake, it is recommended to:
* Compress files before loading: Compressed files are smaller and upload faster, reducing the risk of timeouts.
* Increase the PARALLEL option value: This option allows more simultaneous upload threads, improving upload speed and efficiency for large datasets.
Semi-structured file formats and table staging do not directly impact timeouts, while enabling overwrite does not prevent timeouts but rather controls overwriting of existing files.

While Snowflake primarily deals with structured and semi-structured data, it also has the capability to handle unstructured data. Unstructured data can be stored in Snowflake using variants of SQL data types in tables, which can be permanent tables. These permanent tables, while traditionally used for structured or semi-structured data (like JSON, Avro, or Parquet), can also accommodate unstructured data in the form of binary formats or strings, offering flexibility in data storage and analysis. However, the management and querying of unstructured data in Snowflake may require additional considerations compared to structured data.
Reference: Snowflake Documentation on Data Types

Snowflake maintains a result cache that stores the results of every query for 24 hours. If the same query is executed again within this time frame and the data has not changed, Snowflake will retrieve the data from the result cache instead of using a virtual warehouse to recompute the results2.

Temporary and transient tables in Snowflake do not have a Fail-safe period. Once the session ends or the tables are dropped, the data is purged and not recoverable1.

Reference:
Separate and distinct from Time Travel, Fail-safe ensures historical data is protected in the event of a system failure or other catastrophic event, e.g. a hardware failure or security breach. Fail safe feature cannot be enabled or disabled from the user end .