AWS DataSync is a managed data transfer service that simplifies and accelerates moving large amounts of data online between on-premises storage and Amazon S3, EFS, or FSx for Windows File Server. DataSync is optimized for efficient, incremental, and reliable transfers of large datasets, making it suitable for transferring 5 TB of data with daily updates.

Explanation
If your external software exports fields enclosed in quotes but inserts a leading space before the opening quotation character for each field, Snowflake reads the leading space rather than the open-ing quotation character as the beginning of the field. The quotation characters are interpreted as string data.
Use the TRIM_SPACE file format option to remove undesirable spaces during the data load.

Explanation
Retrieving the Snowflake Query ID
A query ID identifies each query executed by Snowflake. When you use the Snowflake Connector for Python to execute a query, you can access the query ID through the sfqid attribute in the Cursor object:
1.# Retrieving a Snowflake Query ID
2.cur = con.cursor()
3.cur.execute("SELECT * FROM testtable")
4.print(cur.sfqid)

Explanation
The performance tuning options that the Engineer can use based on the Query Profile are:
Add a LIMIT to the ORDER BY If possible: This option will improve performance by reducing the amount of data that needs to be sorted and returned by the query. The ORDER BY clause requires sorting all rows in the input before returning them, which can be expensive and time-consuming. By adding a LIMIT clause, the query can return only a subset of rows that satisfy the order criteria, which can reduce sorting time and network transfer time.
Create indexes to ensure sorted access to data: This option will improve performance by reducing the amount of data that needs to be scanned and filtered by the query. The query contains several predicates on different columns, such as o_orderdate, o_orderpriority, l_shipmode, etc. By creating indexes on these columns, the query can leverage sorted access to data and prune unnecessary micro-partitions or rows that do not match the predicates. This can reduce IO time and processing time.
The other options are not optimal because:
Use a multi-cluster virtual warehouse with the scaling policy set to standard: This option will not improve performance, as the query is already using a multi-cluster virtual warehouse with the scaling policy set to standard. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on the load. Changing the warehouse size or the number of clusters will not affect the performance of this query, as it is already using the optimal resources.
Increase the max cluster count: This option will not improve performance, as the query is not limited by the max cluster count. The max cluster count is a parameter that specifies the maximum number of clusters that a multi-cluster virtual warehouse can scale up to. The Query Profile shows that the query is using a 2XL warehouse with 4 clusters and a standard scaling policy, which means that the warehouse can automatically scale up or down based on theload. The default max cluster count for a 2XL warehouse is 10, which means that the warehouse can scale up to 10 clusters if needed. However, the query does not need more than 4 clusters, as it is not CPU-bound or memory-bound. Increasing the max cluster count will not affect the performance of this query, as it will not use more clusters than necessary.