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Answer: Perform a left join to include all rows from 'df_orders' and only the matching rows from 'df_customers', with NULL values for non-matching rows from 'df_customers'., Perform a full outer join to include all rows from both 'df_orders' and 'df_customers', with NULL values for non-matching rows from either DataFrame.
A left join is the most appropriate choice for this scenario because it ensures that all orders are included in the analysis, even if some customer details are missing, by returning all rows from the left DataFrame ('df_orders') and the matching rows from the right DataFrame ('df_customers'). If there is no match, the result will have NULL values for the columns from 'df_customers'. Option D, a full outer join, could also be considered if the analysis requires including all records from both DataFrames, but it is not the best fit for the given project requirements which prioritize including all orders. Therefore, option A is the correct answer, with option D being a secondary correct answer under different analysis requirements.
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In a data engineering project, you are working with two DataFrames: 'df_orders' containing columns 'order_id', 'customer_id', and 'order_date', and 'df_customers' with columns 'customer_id', 'customer_name', and 'customer_age'. The project requires analyzing customer orders while ensuring all orders are included in the analysis, even if some customer details are missing. Considering the need for a comprehensive analysis that includes all orders, which of the following Spark SQL join operations would you use to achieve this? Choose the best option that meets the project requirements.
A
Perform a left join to include all rows from 'df_orders' and only the matching rows from 'df_customers', with NULL values for non-matching rows from 'df_customers'.
B
Perform an inner join to include only the rows that have matching keys in both 'df_orders' and 'df_customers'.
C
Perform a right join to include all rows from 'df_customers' and only the matching rows from 'df_orders', with NULL values for non-matching rows from 'df_orders'.
D
Perform a full outer join to include all rows from both 'df_orders' and 'df_customers', with NULL values for non-matching rows from either DataFrame.