Databricks Data Engineer Associate Question 189
Single answerYou are working with a dataset in Databricks containing sales transactions in the following long format: store_id, month, and sales. Each row represents the total sales for a store in a specific month. You want to transform this dataset so each row represents a store, and the columns represent the sales for each month (e.g., January, February, etc.). Which of the following approaches should you use?
- A
Use the PIVOT clause to convert the long format dataset to a wide format based on the
monthcolumn. - B
Use the GROUP BY clause on the
store_idcolumn and aggregate thesalescolumn. - C
Use the JOIN clause to merge the dataset with a reference table of months.
- D
Use the SELECT clause with CASE statements to manually create columns for each month.
Show answer and explanation
Correct answer: A
Explanation
The PIVOT clause is the correct approach for transforming data from long format to wide format in Databricks. It allows you to create new columns based on the unique values of a specified column (e.g., month) and aggregate the corresponding values (e.g., sales). Other options, such as GROUP BY, JOIN, or manual SELECT with CASE statements, are either incorrect or inefficient for this specific task.
- A. Correct.
Correct. The PIVOT clause is specifically designed to transform datasets from long format to wide format by creating new columns from unique values of a specified column (in this case,
month). - B. Incorrect.
Incorrect. The GROUP BY clause aggregates data but does not pivot it into wide format. It cannot create new columns based on unique values in another column.
- C. Incorrect.
Incorrect. The JOIN clause is used to combine two datasets based on a common key. It cannot pivot a dataset or change its format from long to wide.
- D. Incorrect.
Incorrect. While the SELECT clause with CASE statements could theoretically be used to pivot data, it is not an efficient or scalable solution, especially when working with large datasets with many unique values in the
monthcolumn.