Which approach is not recommended while creating SQL Queries Analytics controls?

Applying filters to retrieve the required amount of data is a best practice in SQL query optimization. It allows for better performance by narrowing down the dataset that needs to be processed, which can significantly reduce the amount of time it takes for the query to execute. Efficiently filtering data helps in retrieving only what is necessary for analysis, thereby enhancing both clarity and efficiency.

In contrast, reducing the usage of subqueries, using the LIMIT clause, and the usage of the LIKE operator can introduce complexities or performance issues in certain situations. For instance, while subqueries can be powerful, excessive use can lead to inefficient execution plans. Using the LIMIT clause can help in obtaining just the needed results, but it might not always be practical if not paired with adequate filtering. The LIKE operator is useful for pattern matching but can lead to slower queries if not indexed properly or applied to large datasets without additional filters.

Nevertheless, option B stands out as a fundamental and essential best practice in SQL analytics design to ensure that queries are efficient and manageable.