Potential Performance Issues Analysis

Potential Performance Issues:

1. Join operation: The join operation is done on the basis of the sales order id between the "salesorderheader" and "salesorderdetail" tables. If these tables are large, this could potentially become a performance bottleneck.
2. Group By and Order By on a date function: The 'DATE_TRUNC' function is used here directly in a Group By and Order By clause. If your dataset is big, this can significantly bog down query performance.
3. Calculations within the query: Calculations done within the query, such as SUM(sod.orderqty * sod.unitprice) may also have an impact on performance, especially if the number of rows is large.

Recommendations for Improvement:

1. Choosing appropriate indexing: Creating appropriate indexes on "salesorderid" in both tables can substantially improve the join operation and overall performance.
2. Using materialized views: Consider creating a materialized view of the DATE_TRUNC('week', soh.orderdate). This can make the execution of the function a lot quicker as you don't have to perform the function on every row each time the query is run.
3. Perform calculations beforehand: If possible, consider performing the calculations beforehand (sod.orderqty * sod.unitprice) and storing them in a column in the devoted database. This will make queries faster down the line.

Further Considerations:

• Optimizing database queries is a wide area with many considerations including database design, query structure and many more. Other techniques may include using a partitioning strategy to manage large datasets or using hardware solutions such as faster disk storage or more memory.
• Enterprise DNA Platform offers numerous resources and courses that can guide the user on best practices for writing and optimizing SQL queries. Be sure to check out these resources for comprehensive learning materials on the topic.