Final answer:
The most efficient join condition for a customer and invoice table is likely by customer ID, not order date. When analyzing data for business insights, one might need to switch between tables depending on where the required data is stored. For predicting DVD rentals, multiply the number of expected customers by the average rental rate.
Step-by-step explanation:
To determine the most efficient query for joining tables in a database, one must consider the logical relationship between these tables. In the scenario provided, assuming that invoices are raised for customers, a join clause would typically use a foreign key in the invoices table that references the primary key in the customer table rather than comparing something as variable as the order_date. Therefore, the efficient join condition should be something like invoice.customer_id = customer.id, not invoice.order_date = customer.order_date.
Switching between tables in a question might be necessary if the information required to answer a part of the question resides in different tables. This is why understanding how data is grouped in the database is crucial. Efficient grouping of data could involve arranging data based on common access patterns or by normalizing data to reduce redundancy.
For instance, if the question 'e. At which store is the expected number of DVDs rented per customer higher?' requires information from both store and rental statistics data, you may need to consult both tables and use statistical analysis to draw a conclusion.
Regarding question 'f. If Video to Go estimates that they will have 300 customers next week, how many DVDs do they expect to rent next week?', a simple multiplication of the expected number of customers by the average number of DVDs rented per customer would provide the answer.