To optimize parallelism and maximize performance, large files should be split into multiple smaller files (ideally 100-250 MB compressed) before loading. This allows Snowflake to load files in parallel using multiple threads or clusters.
Reference:
Snowflake Docs: Best Practices for Data Loading

For minimizing the cost of large tables that are used to store transitory data, which does not need to be protected against failures because it can be reconstructed outside of Snowflake, the best table type to use is Transient. Transient tables in Snowflake are designed for temporary or transitory data storage and offer reduced storage costs compared to permanent tables. However, unlike temporary tables, they persist across sessions until explicitly dropped.
* Why Transient Tables:Transient tables provide a cost-effective solution for storing data that is temporary but needs to be available longer than a single session. They have lower data storage costs because Snowflake does not maintain historical data (Time Travel) for as long as it does for permanent tables.
* Creating a Transient Table:
* To create a transient table, use theTRANSIENTkeyword in the CREATE TABLE statement:
CREATETRANSIENTTABLEmy_transient_table (...);
* Use Case Considerations:Transient tables are ideal for scenarios where the data is not critical, can be easily recreated, and where cost optimization is a priority. They are suitable for development, testing, or staging environments where data longevity is not a concern.
Reference:For more details on transient tables and their usage scenarios, refer to the Snowflake documentation on table types: https://docs.snowflake.com/en/sql-reference/sql/create-table.html#table-types

To convert semi-structured data into rows and columns in Snowflake, theFLATTENfunction is utilized.
FLATTEN Function: This function takes semi-structured data (e.g., JSON) and transforms it into a relational table format by breaking down nested structures into individual rows. This process is essential for querying and analyzing semi-structured data using standard SQL operations.
Example Usage:
SELECT
f.value:attribute1 AS attribute1,
f.value:attribute2 AS attribute2
FROM
my_table,
LATERAL FLATTEN(input => my_table.semi_structured_column) f;
Snowflake Documentation on FLATTEN

In a multi-cluster warehouse with an economy scaling policy, queries will wait in the queue for 2 minutes before another cluster is started. This is to minimize costs by allowing queries to queue up for a short period before adding additional compute resources. References: [COF-C02] SnowPro Core Certification Exam Study Guide

To evaluate the details of dynamic data masking,
the POLICY_REFERENCES and ACCESS_HISTORY views in the account_usage schema are used.
The POLICY_REFERENCES view provides information about the objects to which a masking policy is applied, and the ACCESS_HISTORY view contains details about access to the masked data, which can be used to audit and verify the application of dynamic data masking policies.
References:
* [COF-C02] SnowPro Core Certification Exam Study Guide
* Snowflake Documentation on Dynamic Data Masking1