Snowflake's architecture allows for the use of a query result cache that stores the results of queries for a period of time. If the same query is run again and the underlying data has not changed, Snowflake can retrieve the result from this cache without needing to re-run the query on an active warehouse, thus saving on compute resources.

Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row-oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.
References: Apache Parquet Documentation

An outer join, specifically a left outer join, will list all rows from the left table and match them with rows from the right table. If there is no match, the result will still include the row from the left table, with NULLs for columns from the right table.References:Based on general SQL knowledge as of 2021.

The Search Optimization Service in Snowflake is designed to improve the performance of specific types of queries, particularly those involving extensive sets of WHERE conditions. By maintaining a search index on tables, this service can accelerate lookup and analytical queries, making it a valuable feature for optimizing query performance and reducing execution times for complex searches.
References:
Snowflake Documentation: Search Optimization Service

Snowflake recommends automating key pair rotation to improve security and comply with industry best practices. SSH key pairs are used for key-pair authentication with clients like SnowSQL or Snowpipe.
Reference:
Snowflake Docs: Key Pair Authentication & Management